CN112835075A - Tracking method and device for orthogonal frequency division multiplexing carrier phase and electronic equipment - Google Patents

Tracking method and device for orthogonal frequency division multiplexing carrier phase and electronic equipment Download PDF

Info

Publication number
CN112835075A
CN112835075A CN201911198870.3A CN201911198870A CN112835075A CN 112835075 A CN112835075 A CN 112835075A CN 201911198870 A CN201911198870 A CN 201911198870A CN 112835075 A CN112835075 A CN 112835075A
Authority
CN
China
Prior art keywords
time
carrier phase
ofdm
value
state vector
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911198870.3A
Other languages
Chinese (zh)
Other versions
CN112835075B (en
Inventor
达人
于大飞
任斌
李刚
郑占旗
张振宇
孙韶辉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Datang Mobile Communications Equipment Co Ltd
Original Assignee
Datang Mobile Communications Equipment Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Datang Mobile Communications Equipment Co Ltd filed Critical Datang Mobile Communications Equipment Co Ltd
Priority to CN201911198870.3A priority Critical patent/CN112835075B/en
Publication of CN112835075A publication Critical patent/CN112835075A/en
Application granted granted Critical
Publication of CN112835075B publication Critical patent/CN112835075B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/29—Acquisition or tracking or demodulation of signals transmitted by the system carrier including Doppler, related

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Synchronisation In Digital Transmission Systems (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The embodiment of the invention discloses a tracking method, a device and electronic equipment of orthogonal frequency division multiplexing carrier phase, wherein the method comprises the following steps: acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment; the state vector estimated value of the OFDM carrier phase is obtained based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, the OFDM carrier phase is tracked, and the carrier phase in a 5G NR/4G LTE FDD/TDD system can be tracked in real time.

Description

Tracking method and device for orthogonal frequency division multiplexing carrier phase and electronic equipment
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for tracking an orthogonal frequency division multiplexing carrier phase, and an electronic device.
Background
GNSS (Global Navigation Satellite System) carrier phase positioning technology is a well-known high-precision positioning technology. In GNSS carrier-phase positioning, a GNSS receiver accurately determines the position of the GNSS receiver by measuring carrier-phase measurements obtained from GNSS satellite signals. The main drawback of GNSS positioning is the inability to operate in environments where the terminals do not receive GNSS satellite signals.
One of the keys to carrier phase positioning, whether of GNSS signals or based on signals of the wireless communication system itself, is that the receiver must be able to track and lock onto the carrier signal from the transmitter in real time to obtain carrier phase measurements. In recent years, the research on GNSS carrier phase tracking is more and the technology is mature, wherein the method for tracking GNSS carrier signals based on EKF (Extended Kalman Filter) is one of the commonly used methods, but the research on carrier phase tracking of the signals of the wireless communication system itself is less; and GNSS is a wireless communication system based on CDMA (Code Division Multiple Access) system, but 3GPP (3rd Generation Partnership Project) 5G (5th Generation, fifth Generation mobile communication technology) NR (New Radio interface) system is a wireless communication system based on OFDM (Orthogonal Frequency Division Multiplexing) system, where the OFDM system mainly includes a 5G (5th Generation, fifth Generation mobile communication technology) NR system and a 4G (4G, fourth Generation mobile communication technology) LTE (Long Term Evolution ) FDD (Frequency Division Duplexing, Frequency Division Duplexing)/TDD (Time Division Duplexing ) system. However, the carrier signal tracking method of GNSS is not necessarily suitable for the OFDM system.
Therefore, it is currently impossible to track the carrier phase in an OFDM system in real time.
Disclosure of Invention
Because the existing method cannot realize the real-time tracking of the OFDM carrier phase, the embodiment of the invention provides a tracking method and a tracking device of an orthogonal frequency division multiplexing carrier phase and electronic equipment.
In a first aspect, an embodiment of the present invention provides a method for tracking an orthogonal frequency division multiplexing carrier phase, including:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
In a second aspect, an embodiment of the present invention further provides an apparatus for tracking an orthogonal frequency division multiplexing carrier phase, including:
the value acquisition module is used for acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and the tracking module is used for obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing a computer program, the computer program causing the computer to execute the following method:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
According to the technical scheme, the state vector estimated value of the OFDM carrier phase is obtained by calculating the state vector predicted value of the OFDM carrier phase and the scalar measured value of the OFDM carrier phase at each moment, the OFDM carrier phase is tracked, and the carrier phase in the 5G NR/4G LTE FDD/TDD system can be tracked in real time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a method for tracking an ofdm carrier phase according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an EKF receiver for carrier phase tracking in an open-loop configuration according to an embodiment of the present invention;
FIG. 3 is a flowchart of an EKF receiver with carrier phase tracking in a closed loop configuration according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a CPRS-OFDM symbol and a CPRS RE of an OFDM system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus for tracking an ofdm carrier phase according to an embodiment of the present invention;
fig. 6 is a logic block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Fig. 1 shows a schematic flow chart of a tracking method for orthogonal frequency division multiplexing carrier phases provided in this embodiment, including:
s101, obtaining a state vector predicted value of the OFDM carrier phase and a scalar measured value of the OFDM carrier phase at each moment.
The parameters for representing the carrier phase comprise two-dimensional parameters of a state vector and a scalar.
Wherein the state vector x is: x ═ xt,xf,xo]TWherein: x is the number oftIs corresponding to fcTime offset of carrier phase (in carrier frequency f)cIn units of cycles); x is the number offIs the carrier frequency offset (in Hz); x is the number ofoIs the carrier frequency offset rate of change (in Hz/s).
Wherein, the scalar y of the carrier phase is the radian value of the phase; for example, a signal at a certain time is represented as: s ═ A × ej0.5πWherein: and a is the signal amplitude, the carrier phase scalar y is 0.5 pi.
And the state vector predicted value is the predicted value of the state vector of the OFDM carrier phase obtained by calculation.
The state vector estimation value is an estimation value of the state vector of the OFDM carrier phase obtained through calculation.
The scalar measurement is a calculated scalar measurement of the OFDM carrier phase.
The scalar prediction value is a scalar prediction value of the calculated OFDM carrier phase.
And the scalar measurement value of the OFDM carrier phase is a value of the OFDM carrier phase calculated according to specific parameters in the OFDM symbol.
The predicted value of the OFDM carrier phase state vector is the value of the OFDM carrier phase state vector predicted according to an EKF time updating algorithm.
The EKF time updating algorithm is used for updating the predicted value of the OFDM carrier phase state vector at the current moment according to the estimated value of the OFDM carrier phase state vector at the previous moment.
S102, obtaining a state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
To realize the tracking of the OFDM carrier phase, it is necessary to obtain a state vector predicted value of the OFDM carrier phase and a scalar measurement value of the OFDM carrier phase at each time, and then obtain a state vector estimated value of the corresponding OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at the corresponding time and the scalar measurement value of the OFDM carrier phase at the corresponding time to track the OFDM carrier phase at the corresponding time.
Specifically, a scalar quantity predicted value of the current-time OFDM carrier phase is obtained based on a predicted value of the current-time OFDM carrier phase state vector, then an estimated value of the current-time OFDM carrier phase state vector is obtained based on a scalar quantity measured value of the current-time OFDM carrier phase and the scalar quantity predicted value of the current-time OFDM carrier phase, and further the OFDM carrier phase of the terminal at the current time is obtained, so that tracking of the OFDM carrier phase is achieved.
In this embodiment, the state vector estimation value of the OFDM carrier phase is obtained by calculating the state vector prediction value of the OFDM carrier phase and the scalar measurement value of the OFDM carrier phase at each time, and the OFDM carrier phase is tracked, so that the carrier phase in the 5G NR/4G LTE FDD/TDD system can be tracked in real time.
Further, S102 specifically includes:
acquiring a scalar quantity predicted value of the OFDM carrier phase at the moment k +1 according to the state vector predicted value of the OFDM carrier phase at the moment k +1, calculating a difference value between the scalar quantity measured value of the OFDM carrier phase at the moment k +1 and the scalar quantity predicted value of the OFDM carrier phase at the moment k +1, and updating a state vector estimated value and a covariance matrix estimated value of the OFDM carrier phase at the moment k +1 according to an Extended Kalman Filter (EKF) measurement updating algorithm;
according to a terminal positioning algorithm, obtaining the OFDM carrier phase of the terminal at the k +1 moment based on the state vector estimation value of the OFDM carrier phase at the k +1 moment so as to realize the tracking of the OFDM carrier phase;
wherein k is a positive integer.
The terminal positioning algorithm obtains the position information of the terminal at each moment by using relevant algorithms such as a TDOA principle, a carrier phase whole period estimation algorithm and the like. The tracking method of the OFDM carrier phase provided by the present embodiment is applicable to an open-loop structure (fig. 2) and a closed-loop structure (fig. 3). The difference between open-loop and closed-loop architectures is: in an open loop architecture, the EKF estimate is used to forward correct the impact on the post-FFT data symbols introduced by time and frequency offsets; whereas in the closed-loop architecture, the EKF estimate is used to feedback correct for the effects introduced by time and frequency offsets on the pre-FFT incoming data samples.
Specifically, the steps performed by the EKF receiver with respect to OFDM carrier phase tracking are as follows: let EKF state vector be x, and state vector and covariance matrix estimated at initial time k equal to 0 are respectively
Figure BDA0002295353410000067
And the transmitted OFDM symbol at a certain k time is on the set of L sub-carriers
Figure BDA0002295353410000061
With known CPRS sample sequences
Figure BDA0002295353410000062
The baseband discrete data samples corresponding to the received OFDM symbol are { x }nAnd (N-0, …, N-1), where N is the number of discrete data sample points.
As shown in fig. 2, the EKF receiver based on OFDM carrier phase tracking of open loop structure comprises the following steps:
step 1: for baseband discrete data samples xnFFT to obtain the frequency domain signal corresponding to the sub-carrier with known CPRS sample value sequence in the OFDM symbol
Figure BDA0002295353410000066
Step 2: will be provided with
Figure BDA0002295353410000063
With its known CPRS sample sequence
Figure BDA0002295353410000064
Performing correlation calculation to obtain scalar measurement value of phase
Figure BDA0002295353410000065
Step 3: using state estimation values based on the k time according to an EKF time update algorithm
Figure BDA0002295353410000071
And a covariance matrix P (k | k) to predict the state vector at time k +1
Figure BDA0002295353410000072
And a covariance matrix P (k +1| k);
step 4: based on predicted state vector
Figure BDA0002295353410000073
The carrier phase is predicted as
Figure BDA0002295353410000074
Step 5: calculating the difference between the scalar measured value and the scalar predicted value of the carrier phase obtained at the time k +1, and recording as
Figure BDA0002295353410000075
Step 6: according to EKF measurement update algorithm based on
Figure BDA0002295353410000079
P (k +1| k) and
Figure BDA0002295353410000077
updating state vector estimation value at k +1 moment
Figure BDA0002295353410000078
And a covariance matrix P (k +1| k + 1);
step 7: and repeating Step 3-Step 6, calculating a scalar measured value and a state vector predicted value of the carrier phase at each moment, and obtaining an estimated value of the carrier phase, thereby completing the tracking of the carrier phase.
Further, on the basis of the above method embodiment, after obtaining the scalar predicted value of the OFDM carrier phase at the time k +1 according to the state vector predicted value of the OFDM carrier phase at the time k +1, calculating a difference value between the scalar measured value of the OFDM carrier phase at the time k +1 and the scalar predicted value of the OFDM carrier phase at the time k +1, and updating the state vector estimated value and the covariance matrix estimated value of the OFDM carrier phase at the time k +1 according to the extended kalman filter EKF measurement update algorithm, the method further includes:
the baseband discrete data samples at time k +2 are phase rotated based on the state vector estimate of the OFDM carrier phase at time k +1 to correct for the effects of time and frequency offsets on the baseband discrete data samples at time k + 2.
Specifically, as shown in fig. 3, the EKF receiver based on the OFDM carrier phase tracking of the closed loop structure includes the following steps:
step1 to Step 6: the phase tracking method is the same as that of Step1 to Step6 of the OFDM carrier phase tracking based on the open loop structure in the embodiment corresponding to the FIG. 2;
step 7: firstly, at the time k +2, the EKF state vector estimated value at the time k +1 is used for feedback correction of the incoming data sample (baseband discrete data sample { x) before FFT by time and frequency offsetn}) the influence of the introduction;
step 8: and repeating Step 1-Step 7, calculating a scalar measured value and a state vector predicted value of the carrier phase at each moment, and obtaining an estimated value of the carrier phase, thereby completing the tracking of the carrier phase. .
Further, on the basis of the above method embodiment, the phase rotating the baseband discrete data sample at the time k +2 according to the state vector estimation value at the time k +1 to correct the baseband discrete data sample at the time k +2 by time and frequency offsets specifically includes:
for the baseband discrete data sample x at time k +2 according to the following equationn(k +2) phase rotation to obtain a new sequence of data samples { z }n(k+2)}：
Figure BDA0002295353410000081
Wherein the content of the first and second substances,
Figure BDA0002295353410000082
is composed of
Figure BDA0002295353410000083
The phase rotation calculated is rotated by the phase of the signal,
Figure BDA0002295353410000084
j is a unit imaginary number; x is the number ofn(k +2) is the nth known CPRS sample sequence in the OFDM symbol at the time k + 2; n is the number of baseband discrete data samples contained in each OFDM symbol;
Figure BDA0002295353410000085
is the predicted time offset of OFDM symbol at the time k +2, which is in unit of period and has the size of delta t (k +2) fcΔ t (k +2) is the time offset between the signal received by the receiver at the start of the OFDM symbol at time k +2 and the signal generated by the receiver itself, in seconds; f. ofcK is a positive integer for the carrier frequency.
Further, based on the above method embodiment, S101 specifically includes:
performing correlation calculation on a known carrier phase reference signal CPRS sample sequence in an OFDM symbol carried at the moment k and a frequency domain signal of a subcarrier corresponding to the sample sequence at the moment k to obtain a scalar measurement value of the OFDM carrier phase at the moment k, wherein the frequency domain signal of the subcarrier is obtained by performing fast Fourier transform on a baseband discrete data sample received at the moment k;
according to an EKF time updating algorithm, based on the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the time k, the state vector prediction value and the covariance matrix prediction value of the OFDM carrier phase at the time k +1 are obtained, and based on the state vector prediction value of the OFDM carrier phase at the time k +1 and a preset EKF measurement matrix, the scalar prediction value of the OFDM carrier phase at the time k +1 is obtained.
Further, on the basis of the above method embodiment, the performing correlation calculation on the known carrier phase reference signal CPRS sample sequence in the OFDM symbol carried at the time k and the frequency domain signal of the subcarrier corresponding to the sample sequence at the time k to obtain the scalar measurement value of the OFDM carrier phase at the time k specifically includes:
obtaining a frequency domain signal R of a subcarrier corresponding to the l known CPRS sample value sequence in the OFDM symbol carried at the k momentl(k)：
Figure BDA0002295353410000091
For frequency domain signal Rl(k) And corresponding known CPRS sample sequence Xl(k) Performing correlation calculation to obtain scalar measurement value y of the carrier phase of the first subcarrier of the OFDM at the time kl(k)：
Figure BDA0002295353410000092
Wherein δ f is the normalized frequency deviation; h is0Attenuation for the first transmission path; n is the number of baseband discrete data samples contained in each OFDM symbol; j is a unit imaginary number; f. ofcIs the carrier frequency; l is the serial number of the subcarrier in the known CPRS sample value sequence; Δ fSCsIs the subcarrier spacing of the OFDM system; Δ t (k) is the time offset between the signal received by the receiver at the beginning of the OFDM symbol at time k and the signal generated by the receiver itself, in seconds; tau is0(k) Time offset generated by Doppler in channel transmission for OFDM symbols at time k; gl(k) A radio channel frequency response for the l-th known CPRS sample sequence in the OFDM symbol at time k; xl(k) Is the l known CPRS sample value sequence in the OFDM symbol at the time k; wl(k) Additive white gaussian noise AWGN of the l-th known CPRS sample sequence in the OFDM symbol at time k; rl(k) Frequency domain signal of the first known CPRS sample sequence in the OFDM symbol at time k, xt(k) Is the time offset of OFDM symbol at time k, which has a unit of period and size of delta t (k)c，xf(k) Frequency offset, T, of OFDM symbols at time ksK is a positive integer for the sampling time interval;
scalar measurement y of the carrier phase from the first subcarrier of OFDM at time kl(k) The EKF measurement matrix H obtained by the expression equation of (a) is:
Figure BDA0002295353410000108
Figure BDA0002295353410000101
wherein HlEKF measurement matrix for the l sub-carrier, TsIs the sampling interval.
Specifically, in the tracking process of the OFDM carrier phase in the open-loop structure and the closed-loop structure, the above formula can be used to calculate the measured value of the OFDM carrier phase at each time.
Further, on the basis of the above method embodiment, the obtaining, according to the EKF time update algorithm, a state vector predicted value and a covariance matrix predicted value at a time k +1 based on a state vector estimated value and a covariance matrix estimated value at the time k, and obtaining a scalar predicted value of an OFDM carrier phase at the time k +1 based on the state vector predicted value at the time k +1 and a preset EKF measurement matrix specifically include:
state vector estimation value of OFDM carrier phase based on k time
Figure BDA0002295353410000102
And a state transition matrix F (k) for calculating the state vector prediction of the OFDM carrier phase at the time k +1Value of
Figure BDA0002295353410000103
Comprises the following steps:
Figure BDA0002295353410000104
Figure BDA0002295353410000105
wherein, Δ T (k) is the time difference between the k +1 time and the k time, and the unit is second;
calculating a covariance matrix predicted value P (k +1| k) at the moment k +1 according to the covariance matrix estimated value P (k | k) at the moment k and the state transition matrix F (k):
P(k+1|k)＝F(k)P(k|k)FT(k)+Q(k)
state vector predictor based on time k +1
Figure BDA0002295353410000106
And the EKF measurement matrix H of the l sub-carrierlObtaining scalar quantity predicted value of the carrier phase of the first sub-carrier of OFDM at the moment of k +1
Figure BDA0002295353410000107
Comprises the following steps:
Figure BDA0002295353410000111
wherein the content of the first and second substances,
Figure BDA0002295353410000112
time offset of the predicted OFDM symbol at time k + 1;
Figure BDA0002295353410000113
frequency offset of the OFDM symbol at the predicted k +1 moment; fT(k) Is the transposed matrix of F (k), and Q (k) is the process noise covariance at time k.
Further, on the basis of the above method embodiment, the updating the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the k +1 time according to the extended kalman filter EKF measurement update algorithm specifically includes:
state vector prediction value according to OFDM carrier phase at k +1 moment
Figure BDA0002295353410000114
An EKF gain matrix K (K +1) at time K +1, a scalar measurement y (K +1) of the OFDM carrier phase at time K +1, and a scalar prediction of the carrier phase at time K +1
Figure BDA0002295353410000116
Updating state vector estimation value at k +1 moment
Figure BDA0002295353410000117
Figure BDA0002295353410000118
Updating the covariance matrix estimation value P (K +1| K +1) at the time K +1 according to the covariance matrix prediction value P (K +1| K) at the time K +1, the EKF gain matrix K (K +1) at the time K +1, and the EKF measurement matrix H at the time K + 1:
P(k+1|k+1)＝[I-K(k+1)H]P(k+1|k)
wherein K (K +1) ═ P (K +1| K) HT×[HP(k+1|k)HT+Rl(k)]-1
HTA transposed matrix of H.
Further, on the basis of the above method embodiment, the estimated value of the time offset of the OFDM symbol at the initial time (k ═ 0)
Figure BDA0002295353410000119
The estimation formula of (c) is as follows:
Figure BDA00022953534100001110
Figure BDA00022953534100001112
or the like, or, alternatively,
estimation value of time offset of OFDM symbol at initial time (k is 0)
Figure BDA00022953534100001111
Setting according to the estimated value of the estimated time of arrival (TOA);
wherein ljIs the jth subcarrier number; liIs the ith subcarrier number;
Figure BDA0002295353410000121
frequency domain data of ith subcarrier of OFDM symbol at the time k;
Figure BDA0002295353410000122
the conjugate of the frequency domain data of the ith subcarrier of the OFDM symbol at the time k;
Figure BDA0002295353410000123
frequency domain data of the jth subcarrier of the OFDM symbol at the time k;
Figure BDA0002295353410000124
the conjugate of the frequency domain data for the jth subcarrier of the OFDM symbol at time k.
Specifically, in addition to calculating an estimated value of the time offset of the OFDM symbol at the initial time using the above estimation formula
Figure BDA0002295353410000125
The TOA may also be estimated according to conventional algorithms, and then the estimated value of TOA is used to set the initial time offset xt(0)。
The TOA algorithm is to obtain the propagation delay of a signal between a transmitter and a receiver through a correlation algorithm of a convex optimization theory by using a relation between the distance between the transmitter and the receiver and time.
Specifically, the EKF receiver for OFDM carrier phase tracking includes selection of an EKF state vector, establishment of EKF state equations and EKF measurement equations, an EKF iterative update algorithm (including time update and measurement update of estimated state vectors and covariance matrices), and EKF initialization. The following describes the principles and processes involved in the method provided in this embodiment in detail:
first, an OFDM system transmission model is introduced:
in an EKF receiver for carrier phase measurement tracking, consider the following OFDM wireless communication system uplink or downlink transmission model, as shown in fig. 4:
the OFDM system has N1 sub-carriers with a sub-carrier spacing of Δ fSCS；
N1 symbols X may be transmitted in each OFDM symbolk,k∈{0,1,…,N1-1}；
If a certain OFDM symbol contains CPRS, the OFDM symbol is called CPRS-OFDM symbol and can be used for tracking carrier phase signals; here, CPRS is used to represent all reference signals that can be used for carrier phase measurement, including LTE/NR CSI-RS, SSB, PRS, SRS, etc.
Without loss of generality, an OFDM symbol for tracking a carrier phase signal is provided that comprises L CPRS sample sequences
Figure BDA0002295353410000126
Subcarrier set
Figure BDA0002295353410000127
Representing the subcarrier locations of the L sequences of CPRS samples in the CPRS-OFDM symbol. In the frequency resource configuration, the EKF does not require a certain CPRS sample sequence mapping pattern, i.e., the L CPRS sample sequences can be arbitrarily mapped to subcarriers in the OFDM symbol.
Each OFDM symbol has a duration of T seconds and a sampling time interval of Ts＝1/(NΔfSCS) Thus, each OFDM symbol includes N (N ≧ N1) baseband discrete data samples { xnThe (without taking into account the cyclic prefix CP) is:
Figure BDA0002295353410000131
in the time domain resource configuration, the time interval from the CPRS-OFDM symbol starting from the moment k to the CPRS-OFDM symbol starting from the moment k +1 is delta T (k) (seconds); in the EKF receiver of this embodiment, Δ t (k) may have any value. The transmitter may transmit the CPRS-OFDM symbols periodically or aperiodically. During the at (k) time interval, the receiver may change to the transmitter and vice versa. Thus, the EKF carrier phase tracking method of the present embodiment can be used for FDD and TDD OFDM systems.
It is assumed that the channel remains unchanged for the duration of each OFDM symbol, i.e. the transmission channel is quasi-static. In carrier phase positioning, an EKF is required to track the carrier phase of a first transmission path; thus, the present embodiment only considers the first transmission path, and the wireless channel frequency response of the ith subcarrier can be described as follows:
Figure BDA0002295353410000132
wherein, g0And τ0Attenuation and propagation delay (in seconds) of the first transmission path, respectively, fcIs the carrier frequency, Δ fSCSIs the subcarrier spacing.
Let us assume that at the beginning of a CPRS-OFDM symbol (excluding CP) at time k, the time and frequency offsets between the signal received by the receiver and the signal generated by the receiver itself are Δ t (k) (seconds) and Δ f (t) (hz), respectively. If δ f is used to denote the normalized frequency offset, i.e., δ f ═ Δ f/Δ fSCS. When the interference among the sub-carriers is ignored, the received OFDM symbol is in the frequency domain signal R of the l sub-carrierlCan be described as:
Figure BDA0002295353410000133
wherein the content of the first and second substances,
Figure BDA0002295353410000141
for AWGN noise, GlFor the channel frequency response of the l sub-carrier, XlIs a CPRS sample sequence at the l-th subcarrier position in the CPRS-OFDM symbol.
Specifically, for an EKF receiver based on OFDM carrier phase tracking in an open loop configuration, a carrier phase tracking EKF receiver for 5G NR carrier phase positioning includes the following aspects: an EKF state vector, an EKF state equation, an EKF measurement equation, an EKF algorithm, an EKF initialization method, and EKF closed-loop feedback.
Wherein, in order to enable EKF to realize carrier phase tracking of a 5G NR/4G LTE FDD/TDD multi-carrier system; in this embodiment, the EKF state vector is redesigned, the phase variable of the conventional EKF state vector is removed, the time variable is added, and the corresponding EKF state equation and the EKF measurement equation are also redesigned to be changed into a function of the time variable; the EKF initialization method and EKF closed-loop feedback are improved by modifying the EKF input from conventional amplitude information to phase information and modifying the carrier phase prediction equation and the error variance between the carrier phase scalar measurement and scalar prediction.
For the EKF status vector:
when designing an EKF receiver, firstly, an unknown state vector of the EKF needs to be reasonably selected, and the state vector is composed of a plurality of state variables. The present embodiment provides a new EKF state vector, which overcomes the disadvantage that the conventional EKF state vector only uses a single carrier system, and makes it suitable for carrier phase tracking of a 5G NR/4G LTE FDD/TDD multi-carrier system, and the following describes in detail the state variables that can be considered by an EKF receiver, and specifically includes the following:
state variable (x) related to carrier phaset): the EKF state vector used for carrier phase tracking must include state variables associated with the carrier phase. GNSS is a CDMA system with only one carrier frequency. Thus, in a GNSS EKF receiver that tracks carrier phase, the EKF state typically includes directly the carrier phase of the tracked carrier frequency. But in OFDM systems, for the sameTime offset, the carrier phase on different subcarriers is different. Instead of the carrier phase, the time offset can then be used as the EKF state, i.e. the time difference x between the carrier signal received by the receiver and the carrier signal of the receiver itselfΔTAs the EKF status. x is the number ofΔTThe method comprises the following two parts: first, by the offset Δ t between the time of the transmitter and the time of the receiver. One of the main causes of the time offset is clock error caused by oscillator error between the transmitter and the receiver. In OFDM systems, it is also often referred to as phase noise. Second, the propagation of the signal from the transmitter to the receiver is delayed by τ. The propagation delay τ is mainly related to the distance between the transmitter and the receiver. In order to increase the numerical stability (x) of an EKF receiverΔTGenerally small), where x may be usedt＝(xΔTfc) An EKF variable representing a time offset. x is the number oftCan also be regarded as corresponding to the carrier frequency fcCarrier phase x oftIn cycles.
State variable (x) related to carrier frequency offsetf): the state variable x related to the frequency offset is typically included in the EKF state vector, taking into account the dynamic mobility of the UE, and the difference in carrier frequency between the transmitter and the receiverf(in Hz). x is the number offThe method comprises the following two parts: first, the frequency offset Δ f between the carrier frequency generated by the transmitter and the carrier frequency generated by the receiver. One of the main causes of time offset is the oscillator frequency offset error between the transmitter and the receiver. In OFDM systems, this is also often referred to as the rate of change of phase noise. Second, the Doppler frequency f due to relative motion between the transmitter and receiverd。
State variable (x) related to carrier frequency offset rate of changeo): the EKF state vector may also include a carrier frequency offset rate state, the purpose of which is to improve the accuracy of the carrier frequency offset estimation. The EKF variable x may be used hereinoThe rate of change of frequency offset is expressed (in Hz/s). Here, we will xoArranged to model a random walk process in first order, i.e.
Figure BDA0002295353410000151
Wherein, wo(t) is continuous-time Gaussian white noise,
Figure BDA0002295353410000152
is the variance of the gaussian noise.
From the above discussion, the EKF state vector x used to track the carrier phase of an OFDM signal from a transmitter is:
x＝[xt,xf,xo]T (5)
wherein
xtIs corresponding to fcTime offset of carrier phase (in carrier frequency f)cIn units of cycles);
xfis the carrier frequency offset (in Hz);
xofor the rate of change of carrier frequency offset (in Hz/s)
xtAnd xfAnd xfAnd xoThe relationship between can be expressed as:
Figure BDA0002295353410000161
Figure BDA0002295353410000162
wherein, wt(t) and wo(t) modeling as continuous-time gaussian white noise,
Figure BDA0002295353410000163
is the noise wt(t) the variance of the (t),
Figure BDA0002295353410000164
is the noise wo(t) variance. Note: the frequency offset being substantially the carrier frequency phaseIn contrast, the frequency offset of each subcarrier in an OFDM system is not the same. But since the difference in frequency offset of each subcarrier is generally small compared to the subcarrier spacing, the difference in frequency offset of different subcarriers has been ignored in equations (6) and (7).
For EKF equation of state:
in order to overcome the defect that the conventional EKF state vector only uses a single carrier system, the EKF state vector is suitable for a 5G NR/4G LTE FDD/TDD multi-carrier system, carrier phase tracking can be simultaneously carried out on a plurality of sub-carrier signals, and an EKF state equation is redesigned.
Based on the selected EKF state vector x of equation (5), the EKF continuous-time state equation that tracks the carrier phase can be written as:
Figure BDA0002295353410000165
wherein the content of the first and second substances,
Figure BDA0002295353410000166
Figure BDA0002295353410000167
wherein, wc(t) is the process noise of the continuous time equation of state; qcA process noise covariance matrix that is a continuous time equation of state.
The main causes of time offset as mentioned before are clock errors caused by oscillator errors between the transmitter and the receiver and the propagation delay τ of the signal from the transmitter to the receiver. The propagation delay τ is then mainly related to the distance between the transmitter and the receiver.
Figure BDA0002295353410000171
And
Figure BDA0002295353410000172
the values of (a) need to consider both the quality of the transmitter and receiver crystal oscillators and the motion dynamics of the UE under consideration.
If it is used
Figure BDA0002295353410000173
And
Figure BDA0002295353410000174
represents
Figure BDA0002295353410000175
And
Figure BDA0002295353410000176
the part of the transmitter and receiver crystal oscillator error, as can be seen from equation (2),
Figure BDA0002295353410000177
wherein f iscIs the carrier frequency, { h0,h-2And is the Allen variance coefficient of the crystal oscillator. { h0,h-2Typical values for are the following table:
crystal oscillator h0 h-2
Low quality temperature compensated crystal oscillator (TCXO) 2×10-19 2×10-20
High quality temperatureCompensated crystal oscillator (TCXO) 2×10-21 2×10-24
Oven controlled crystal oscillator (OCXO) 2×10-25 2×10-25
If it is used
Figure BDA0002295353410000178
And
Figure BDA0002295353410000179
represents
Figure BDA00022953534100001710
And
Figure BDA00022953534100001711
part of the error in the relative position and relative movement of the transmitter and receiver, then
Figure BDA00022953534100001712
And
Figure BDA00022953534100001713
and relative moving speed and relative acceleration of the transmitter and the receiver. It is further assumed that the relative moving speed of the transmitter and the receiver does not exceed v (m/s) and the acceleration does not exceed a (m/s)2) Can have
Figure BDA00022953534100001714
Where c is the speed of the light. For example, it can be assumed here that the indoor carrier phase position is located at a speed of less than 6 km/h and an acceleration of less than 0.1 m/s2。
Suppose that
Figure BDA00022953534100001715
And
Figure BDA00022953534100001716
is independent of and
Figure BDA00022953534100001717
and
Figure BDA00022953534100001718
independently, it can be derived from equations (11) and (12):
Figure BDA00022953534100001719
Figure BDA00022953534100001720
may be considered based on the transmitter and receiver's crystal oscillator masses and the relative positions and relative movements of the transmitter and receiver. For the sake of simplicity, it is preferred that,
Figure BDA00022953534100001721
can be set to one relative to σfSmaller numbers, e.g. assuming σ0＝0.01σf。
The EKF discrete time state equation of the tracking carrier phase after being dispersed by the EKF continuous time state equation (8) is as follows:
x(k+1)＝F(k)x(k)+w(k) (14)
wherein:
Figure BDA0002295353410000181
E[w(i)wT(j)]＝Qδij (16)
where Δ t (k) represents (in seconds) the time interval from CPRS-OFDM symbol at time k +1 to CPRS-OFDM symbol at time k +1, as shown in fig. 4. The process noise matrix Q of the discrete state equation may be calculated based on the following method:
Figure BDA0002295353410000182
Figure BDA0002295353410000183
wherein Q is(i)The i-th derivative of Q.
Equation of measurement for EKF
In order to overcome the defect that the conventional EKF state vector only uses a single carrier system, the EKF state vector is suitable for a 5G NR/4G LTE FDD/TDD multi-carrier system, carrier phase tracking can be simultaneously carried out on a plurality of sub-carrier signals, and an EKF measurement equation is redesigned.
The frequency domain signal of the ith subcarrier can be obtained from equations (3) and (2):
Figure BDA0002295353410000184
from Ts＝1/(NΔfsCS),δf＝xf/ΔfSCS,xΔT＝(Δt+τ0),xt＝xΔTfcIs obtained by
Figure BDA0002295353410000185
Then, it corresponds to the time k (t ═ t)k) The measurement equation for the carrier phase measurement of a CPRS-OFDM symbol can be written as
y(k)＝h(x(k))+v(k) (21)
Wherein
y(k)＝{yl(k)}；h＝{hl(k)}；v＝{vl(k)} (22)
Figure BDA0002295353410000191
Figure BDA0002295353410000192
Figure BDA0002295353410000193
Figure BDA0002295353410000194
Where v is the measurement noise and R represents the covariance matrix of the measurement noise v.
Relating to EKF algorithms
The EKF algorithm comprises two steps of time updating and measurement updating, wherein the time updating is to predict the EKF state at the next moment based on the EKF state measured at the current moment, and mainly comprises an EKF state vector x and an EKF state covariance matrix P; the measurement update is to measure the EKF state at the current time based on the EKF state predicted at the previous time and the output of the measurement matrix at the current time, wherein the EKF state vector x and the EKF state covariance matrix P are included.
In the time update step, the EKF predicts the EKF state at time k +1 based on the state estimate at time k. The EKF time update algorithm is
Figure BDA0002295353410000195
P(k+1|k)＝F(k)P(k|k)FT(k)+Q(k) (28)
Wherein
Figure BDA0002295353410000196
And P (k | k) represents the state vector estimated by the EKF at time k and its covariance matrix, respectively;
Figure BDA0002295353410000197
and P (k +1| k) indicates that EKF is based on
Figure BDA0002295353410000198
And the state vector at time k +1 predicted by P (k | k) and its covariance matrix.
In the measurement update step, the EKF updates the state vector of the EKF at time k +1 predicted at time k based on the scalar measurement value at time k + 1:
Figure BDA0002295353410000201
K(k+1)＝P(k+1|k)HT×[HP(k+1|k)HT+R(k)]-1 (30)
P(k+1|k+1)＝[I-K(k+1)H]P(k+1|k) (31)
wherein
y (k +1) is a scalar measurement of the carrier phase at time k + 1;
Figure BDA0002295353410000202
a scalar quantity predicted value of the carrier phase at the moment k + 1;
h is the EKF measurement matrix at time k + 1.
The measurement matrix H is given by
Figure BDA00022953534100002017
Figure BDA0002295353410000203
Due to the modulo 2 pi operation of the phase measurement in equation (24)), the scalar measurement y (k +1) of the measured OFDM carrier phase may differ from the true phase by an integer number of cycles. In the measurement update equation (29), it is necessary to accurately determine the scalar measurement value y (k +1) and the predicted phase value of the OFDM carrier phase
Figure BDA0002295353410000204
Difference of (2)
Figure BDA0002295353410000205
Based on predicted state
Figure BDA0002295353410000206
Predicted carrier phase
Figure BDA0002295353410000207
Comprises the following steps:
Figure BDA0002295353410000208
Figure BDA0002295353410000209
the integer part and the fractional part of (a) are respectively
Figure BDA00022953534100002010
And
Figure BDA00022953534100002011
if a scalar prediction of EKF is assumed
Figure BDA00022953534100002012
Within 0.5 cycles, can be determined by the following equation
Figure BDA00022953534100002013
Figure BDA00022953534100002014
Finally, the predicted carrier phase is compared
Figure BDA00022953534100002015
And sending the information to a terminal positioning algorithm to obtain the high-precision position information of the user terminal at each moment.
EKF initialization method
First, EKF state quantity initialization is carried out:
from equation (20), one obtains:
Figure BDA00022953534100002016
Figure BDA0002295353410000211
the equation (36) can obtain an estimated value of the time offset of the OFDM symbol at the initial time (k ═ 0)
Figure BDA0002295353410000212
The estimation method comprises the following steps:
Figure BDA0002295353410000213
another method is to estimate the time of arrival (TOA) according to a conventional algorithm and then use the estimate of TOA to set the initial time offset xt(0)。
Remaining state quantity x for EKFf(0),xo(0) Their initial values are generally not known and thus their initial estimate may be set to zero. Thus EKF initial state vector
Figure BDA00022953534100002110
Comprises the following steps:
Figure BDA0002295353410000214
the initial covariance matrix P (0) represents the initial estimated value
Figure BDA0002295353410000215
The uncertainty of (2) can be generally set as a diagonal matrix as follows
Figure BDA0002295353410000216
Wherein the content of the first and second substances,
Figure BDA0002295353410000217
may be based on assumptions
Figure BDA0002295353410000218
Is set, for example: set to the square of the maximum error.
In addition, for an EKF receiver based on OFDM carrier phase tracking in a closed-loop architecture, the EKF with the closed-loop architecture corrects the time offset, frequency offset and phase noise effects on the carrier phase of the received data samples with the estimated state quantity.
Let the sequence of data samples corresponding to the CPRS OFDM symbol starting at time k +2 be denoted as { xn(k +2) } (N ═ 0,1, …, N-1). For EKF receivers in closed loop configuration, { x }nThe carrier phase of the (k +2) sequence will be { x } for the data sample prior to FFT using equation (40) based on the EKF state predicted at time k +2nAre phase rotated to form a new sequence of data samples zn(k +2) } as input sequence for FFT:
Figure BDA0002295353410000219
wherein the content of the first and second substances,
zn(k +2) phase rotated data samples;
Figure BDA0002295353410000221
prediction-based time migration
Figure BDA0002295353410000222
Phase rotation of
Figure BDA0002295353410000223
The closed loop configuration of the EKF receiver has the same steps as the open loop configuration of the EKF receiver, including EKF status, time and measurement update EKF algorithms, and EKF initialization. The difference lies in that: because of the closed loop structure EKF uses the predicted time offset
Figure BDA0002295353410000224
For received data sample sequence xn(k +2) } phase rotated, closed loop configuration of EKF
Figure BDA0002295353410000225
The calculation of (b) is not used for the calculation of equation (34), but instead is calculated using equation (42) as follows:
Figure BDA0002295353410000226
no published literature in the prior art discusses an EKF design method suitable for tracking carrier phase of an OFDM wireless network (LTE,5G NR) in real time, but the embodiment proposes a real-time tracking method for carrier phase positioning of an OFDM wireless network based on EKF, and combines the implementation process of the above specific formula, and the steps of the corresponding open-loop structure and closed-loop structure EKF receiver for tracking OFDM carrier phase are as follows:
as shown in fig. 2, the EKF receiver based on OFDM carrier phase tracking of open loop structure comprises the following steps:
step 1: for baseband discrete data samples xnFFT to obtain the frequency domain signal corresponding to the sub-carrier with known CPRS sample value sequence in the OFDM symbol
Figure BDA0002295353410000227
As in equation (19);
step 2: will be provided with
Figure BDA0002295353410000228
With its known CPRS sample sequence
Figure BDA0002295353410000229
Performing correlation calculation to obtain scalar measurement value of phase
Figure BDA00022953534100002210
As in equation (23);
step 3: using state estimation values based on the k time according to an EKF time update algorithm
Figure BDA00022953534100002211
And a covariance matrix P (k | k) to predict the state vector at time k +1
Figure BDA00022953534100002212
And a covariance matrix P (k +1| k) as in equations (27) and (28);
step 4: based on predicted state vector
Figure BDA00022953534100002213
The carrier phase is predicted as
Figure BDA00022953534100002214
As in equation (34);
step 5: calculating the difference between the scalar measured value and the scalar predicted value of the carrier phase obtained at the time k +1, and recording as
Figure BDA00022953534100002215
As in equation (35);
step 6: according to EKF measurement update algorithms (29) - (31), based on
Figure BDA0002295353410000231
P (k +1| k) and
Figure BDA0002295353410000232
updating state vector estimation value at k +1 moment
Figure BDA0002295353410000233
And a covariance matrix P (k +1| k + 1);
step 7: and repeating Step 3-Step 6, calculating a scalar measured value and a state vector predicted value of the carrier phase at each moment, and obtaining an estimated value of the carrier phase, thereby completing the tracking of the carrier phase. .
As shown in fig. 3, the EKF receiver based on OFDM carrier phase tracking of closed loop structure comprises the following steps:
step1 to Step 6: the method is the same as steps 1-Step 6 of OFDM carrier phase tracking based on an open loop structure;
step 7: firstly, at the time k +2, the EKF state vector estimated value at the time k +1 is used for feedback correction of the incoming data sample (baseband discrete data sample { x) before FFT by time and frequency offsetn}) of the influence introduced, as in equation (40);
step 8: and repeating Step 1-Step 7, calculating a scalar measured value and a state vector predicted value of the carrier phase at each moment, and obtaining an estimated value of the carrier phase, thereby completing the tracking of the carrier phase. .
Fig. 5 is a schematic structural diagram of an apparatus for tracking an orthogonal frequency division multiplexing carrier phase according to this embodiment, where the apparatus includes: a value acquisition module 501 and a tracking module 502, wherein:
the value obtaining module 501 is configured to obtain a state vector predicted value of an OFDM carrier phase and a scalar measurement value of the OFDM carrier phase at each time;
the tracking module 502 is configured to obtain a state vector estimation value of an OFDM carrier phase based on a state vector prediction value of the OFDM carrier phase at each time and a scalar measurement value of the OFDM carrier phase, and track the OFDM carrier phase.
Specifically, the value obtaining module 501 obtains a state vector predicted value of an OFDM carrier phase and a scalar measurement value of the OFDM carrier phase at each time; the tracking module 502 obtains a state vector estimation value of the OFDM carrier phase based on the state vector prediction value of the OFDM carrier phase at each time and the scalar measurement value of the OFDM carrier phase, and tracks the OFDM carrier phase.
The tracking apparatus for ofdm carrier phase described in this embodiment may be used to implement the above method embodiments, and the principle and technical effect are similar, which are not described herein again.
Referring to fig. 6, the electronic device includes: a processor (processor)601, a memory (memory)602, and a bus 603;
wherein the content of the first and second substances,
the processor 601 and the memory 602 communicate with each other through the bus 603;
the processor 601 is used to call the program instructions in the memory 602 to execute the following method:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
Further, on the basis of the foregoing embodiment, the obtaining a state vector estimation value of an OFDM carrier phase based on a state vector prediction value of the OFDM carrier phase at each time and a scalar measurement value of the OFDM carrier phase, and tracking the OFDM carrier phase specifically includes:
acquiring a scalar quantity predicted value of the OFDM carrier phase at the moment k +1 according to the state vector predicted value of the OFDM carrier phase at the moment k +1, calculating a difference value between the scalar quantity measured value of the OFDM carrier phase at the moment k +1 and the scalar quantity predicted value of the OFDM carrier phase at the moment k +1, and updating a state vector estimated value and a covariance matrix estimated value of the OFDM carrier phase at the moment k +1 according to an Extended Kalman Filter (EKF) measurement updating algorithm;
according to a terminal positioning algorithm, obtaining the OFDM carrier phase of the terminal at the k +1 moment based on the state vector estimation value of the OFDM carrier phase at the k +1 moment so as to realize the tracking of the OFDM carrier phase;
wherein k is a positive integer.
Further, on the basis of the foregoing embodiment, after obtaining the scalar predicted value of the OFDM carrier phase at the time k +1 according to the state vector predicted value of the OFDM carrier phase at the time k +1, calculating a difference between the scalar measured value of the OFDM carrier phase at the time k +1 and the scalar predicted value of the OFDM carrier phase at the time k +1, and updating the state vector estimated value and the covariance matrix estimated value of the OFDM carrier phase at the time k +1 according to the extended kalman filter EKF measurement update algorithm, the method further includes:
the baseband discrete data samples at time k +2 are phase rotated based on the state vector estimate of the OFDM carrier phase at time k +1 to correct the baseband discrete data samples at time k +2 by a time and frequency offset.
Further, on the basis of the foregoing embodiment, the phase rotating the baseband discrete data sample at the time k +2 according to the state vector estimation value at the time k +1 to correct the baseband discrete data sample at the time k +2 by time and frequency offset specifically includes:
for the baseband discrete data sample x at time k +2 according to the following equationn(k +2) phase rotation to obtain a new sequence of data samples { z }n(k+2)}：
Figure BDA0002295353410000251
Wherein the content of the first and second substances,
Figure BDA0002295353410000252
is composed of
Figure BDA0002295353410000253
The phase rotation calculated is rotated by the phase of the signal,
Figure BDA0002295353410000254
j is a unit imaginary number; x is the number ofn(k +2) is the nth known CPRS sample sequence in the OFDM symbol at the time k + 2; n is the number of baseband discrete data samples contained in each OFDM symbol;
Figure BDA0002295353410000255
for the predicted time offset of the OFDM symbol at time k +2, Δ t (k +2) is the sum of the signals received by the receiver at the beginning of the OFDM symbol at time k +2Time offset between signals generated by the receiver itself; f. ofcK is a positive integer for the carrier frequency.
Further, on the basis of the foregoing embodiment, the obtaining a state vector prediction value of an OFDM carrier phase and a scalar measurement value of the OFDM carrier phase at each time specifically includes:
performing correlation calculation on a known carrier phase reference signal CPRS sample sequence in an OFDM symbol carried at the moment k and a frequency domain signal of a subcarrier corresponding to the sample sequence at the moment k to obtain a scalar measurement value of the OFDM carrier phase at the moment k, wherein the frequency domain signal of the subcarrier is obtained by performing fast Fourier transform on a baseband discrete data sample received at the moment k;
according to an EKF time updating algorithm, based on the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the time k, the state vector prediction value and the covariance matrix prediction value of the OFDM carrier phase at the time k +1 are obtained, and based on the state vector prediction value of the OFDM carrier phase at the time k +1 and a preset EKF measurement matrix, the scalar prediction value of the OFDM carrier phase at the time k +1 is obtained.
Further, on the basis of the above embodiment, the performing correlation calculation on the known carrier phase reference signal CPRS sample sequence in the OFDM symbol carried at the time k and the frequency domain signal of the subcarrier corresponding to the sample sequence at the time k to obtain the scalar measurement value of the OFDM carrier phase at the time k specifically includes:
obtaining a frequency domain signal R of a subcarrier corresponding to the l known CPRS sample value sequence in the OFDM symbol carried at the k momentl(k)：
Figure BDA0002295353410000261
For frequency domain signal Rl(k) And corresponding known CPRS sample sequence Xl(k) Performing correlation calculation to obtain scalar measurement value y of the carrier phase of the first subcarrier of the OFDM at the time kl(k)：
Figure BDA0002295353410000262
Wherein δ f is the normalized frequency deviation; h isoAttenuation for the first transmission path; n is the number of baseband discrete data samples contained in each OFDM symbol; j is a unit imaginary number; f. ofcIs the carrier frequency; l is the serial number of the subcarrier in the known CPRS sample value sequence; Δ fSCSIs the subcarrier spacing of the OFDM system; Δ t (k) is the time offset between the signal received by the receiver at the beginning of the OFDM symbol at time k and the signal generated by the receiver itself; tau is0(k) Time offset generated by Doppler in channel transmission for OFDM symbols at time k; gl(k) A radio channel frequency response for the l-th known CPRS sample sequence in the OFDM symbol at time k; xl(k) Is the l known CPRS sample value sequence in the OFDM symbol at the time k; wl(k) Additive white gaussian noise AWGN of the l-th known CPRS sample sequence in the OFDM symbol at time k; rl(k) Frequency domain signal of the first known CPRS sample sequence in the OFDM symbol at time k, xt(k) Time offset, x, of an OFDM symbol at time kf(k) Frequency offset, T, of OFDM symbols at time ksK is a positive integer for the sampling time interval;
scalar measurement y of the carrier phase from the first subcarrier of OFDM at time kl(k) The EKF measurement matrix H obtained by the expression equation of (a) is:
Figure BDA0002295353410000276
Figure BDA0002295353410000271
wherein HlEKF measurement matrix for the l sub-carrier, TsIs the sampling interval.
Further, on the basis of the above embodiment, the obtaining, according to the EKF time update algorithm, a state vector predicted value and a covariance matrix predicted value at a time k +1 based on a state vector estimated value and a covariance matrix estimated value at the time k, and obtaining a scalar predicted value of an OFDM carrier phase at the time k +1 based on the state vector predicted value at the time k +1 and a preset EKF measurement matrix specifically include:
state vector estimation value of OFDM carrier phase based on k time
Figure BDA0002295353410000272
And a state transition matrix F (k) for calculating the predicted value of the state vector of the OFDM carrier phase at the moment of k +1
Figure BDA0002295353410000273
Comprises the following steps:
Figure BDA0002295353410000274
Figure BDA0002295353410000275
wherein, Δ t (k) is the time difference between the time k +1 and the time k;
calculating a covariance matrix predicted value P (k +1| k) at the moment k +1 according to the covariance matrix estimated value P (k | k) at the moment k and the state transition matrix F (k):
P(k+1|k)＝F(k)P(k|k)FT(k)+Q(k)
state vector predictor based on time k +1
Figure BDA0002295353410000281
And the EKF measurement matrix H of the l sub-carrierlObtaining scalar quantity predicted value of the carrier phase of the first sub-carrier of OFDM at the moment of k +1
Figure BDA0002295353410000282
Comprises the following steps:
Figure BDA0002295353410000283
wherein the content of the first and second substances,
Figure BDA0002295353410000284
time offset of the predicted OFDM symbol at time k + 1;
Figure BDA0002295353410000285
frequency offset of the OFDM symbol at the predicted k +1 moment; fT(k) Is the transposed matrix of F (k), and Q (k) is the process noise covariance at time k.
Further, on the basis of the above embodiment, the updating the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the k +1 time according to the extended kalman filter EKF measurement update algorithm specifically includes:
state vector prediction value according to OFDM carrier phase at k +1 moment
Figure BDA0002295353410000286
An EKF gain matrix K (K +1) at time K +1, a scalar measurement y (K +1) of the OFDM carrier phase at time K +1, and a scalar prediction of the carrier phase at time K +1
Figure BDA0002295353410000287
Updating state vector estimation value at k +1 moment
Figure BDA0002295353410000288
Figure BDA0002295353410000289
Updating the covariance matrix estimation value P (K +1| K +1) at the time K +1 according to the covariance matrix prediction value P (K +1| K) at the time K +1, the EKF gain matrix K (K +1) at the time K +1, and the EKF measurement matrix H at the time K + 1:
P(k+1|k+1)＝[I-K(k+1)H]P(k+1|k)
wherein K (K +1) ═ P (K +1| K) HT×[HP(k+1|k)HT+Rl(k)]-1
HTA transposed matrix of H.
Further, on the basis of the above-mentioned embodiments, the estimated value of the time offset of the OFDM symbol at the initial time is
Figure BDA00022953534100002810
The estimation formula of (c) is as follows:
Figure BDA0002295353410000291
or the like, or, alternatively,
estimation of time offset of OFDM symbol at initial time
Figure BDA0002295353410000292
Setting according to the estimated value of the estimated time of arrival (TOA);
wherein ljIs the jth subcarrier number; liIs the ith subcarrier number;
Figure BDA0002295353410000293
frequency domain data of ith subcarrier of OFDM symbol at the time k;
Figure BDA0002295353410000294
the conjugate of the frequency domain data of the ith subcarrier of the OFDM symbol at the time k;
Figure BDA0002295353410000295
frequency domain data of the jth subcarrier of the OFDM symbol at the time k;
Figure BDA0002295353410000296
the conjugate of the frequency domain data for the jth subcarrier of the OFDM symbol at time k.
The present embodiments disclose a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, the computer is capable of performing the method of:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
The electronic device described in this embodiment may be used to implement the above method embodiments, and the principle and technical effect are similar, which are not described herein again.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
It should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (20)

1. A method for tracking a phase of an orthogonal frequency division multiplexing carrier, comprising:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
2. The method according to claim 1, wherein the obtaining the state vector estimation value of the OFDM carrier phase based on the state vector prediction value of the OFDM carrier phase and the scalar measurement value of the OFDM carrier phase at each time to track the OFDM carrier phase comprises:
acquiring a scalar quantity predicted value of the OFDM carrier phase at the moment k +1 according to the state vector predicted value of the OFDM carrier phase at the moment k +1, calculating a difference value between the scalar quantity measured value of the OFDM carrier phase at the moment k +1 and the scalar quantity predicted value of the OFDM carrier phase at the moment k +1, and updating a state vector estimated value and a covariance matrix estimated value of the OFDM carrier phase at the moment k +1 according to an Extended Kalman Filter (EKF) measurement updating algorithm;
according to a terminal positioning algorithm, obtaining the OFDM carrier phase of the terminal at the k +1 moment based on the state vector estimation value of the OFDM carrier phase at the k +1 moment so as to realize the tracking of the OFDM carrier phase;
wherein k is a positive integer.
3. The method according to claim 2, wherein after obtaining the scalar predicted value of the OFDM carrier phase at the time k +1 according to the state vector predicted value of the OFDM carrier phase at the time k +1, calculating a difference between the scalar measured value of the OFDM carrier phase at the time k +1 and the scalar predicted value of the OFDM carrier phase at the time k +1, and updating the state vector estimated value and the covariance matrix estimated value of the OFDM carrier phase at the time k +1 according to an extended kalman filter EKF measurement update algorithm, the method further comprises:
the baseband discrete data samples at time k +2 are phase rotated based on the state vector estimate of the OFDM carrier phase at time k +1 to correct the baseband discrete data samples at time k +2 by a time and frequency offset.
4. The method of claim 3, wherein the phase rotating the baseband discrete data samples at time k +2 according to the state vector estimate at time k +1 to correct the baseband discrete data samples at time k +2 by time and frequency offsets comprises:
for the baseband discrete data sample x at time k +2 according to the following equationn(k +2) phase rotation to obtain a new sequence of data samples { z }n(k+2)}：
Figure FDA0002295353400000021
Wherein the content of the first and second substances,
Figure FDA0002295353400000022
is composed of
Figure FDA0002295353400000023
The phase rotation calculated is rotated by the phase of the signal,
Figure FDA0002295353400000024
j is a unit imaginary number; x is the number ofn(k +2) is the nth known CPRS sample sequence in the OFDM symbol at the time k + 2; n is the number of baseband discrete data samples contained in each OFDM symbol;
Figure FDA0002295353400000025
time offset of the predicted OFDM symbol at time k + 2; f. ofcIs the carrier frequency; k is a positive integer.
5. The method according to claim 2, wherein the obtaining the state vector predictor of the OFDM carrier phase and the scalar measurement value of the OFDM carrier phase at each time specifically comprises:
performing correlation calculation on a known carrier phase reference signal CPRS sample sequence in an OFDM symbol carried at the moment k and a frequency domain signal of a subcarrier corresponding to the sample sequence at the moment k to obtain a scalar measurement value of the OFDM carrier phase at the moment k, wherein the frequency domain signal of the subcarrier is obtained by performing fast Fourier transform on a baseband discrete data sample received at the moment k;
according to an EKF time updating algorithm, based on the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the time k, the state vector prediction value and the covariance matrix prediction value of the OFDM carrier phase at the time k +1 are obtained, and based on the state vector prediction value of the OFDM carrier phase at the time k +1 and a preset EKF measurement matrix, the scalar prediction value of the OFDM carrier phase at the time k +1 is obtained.
6. The method according to claim 5, wherein the correlation calculation is performed on the known carrier phase reference signal CPRS sample sequence in the OFDM symbol carried at the time k and the frequency domain signal of the subcarrier corresponding to the sample sequence at the time k to obtain the scalar measurement value of the OFDM carrier phase at the time k, and specifically includes:
obtaining a frequency domain signal R of a subcarrier corresponding to the l known CPRS sample value sequence in the OFDM symbol carried at the k momentl(k)：
Figure FDA0002295353400000031
For frequency domain signal Rl(k) And corresponding known CPRS sample sequence Xl(k) Performing correlation calculation to obtain scalar measurement value y of the carrier phase of the first subcarrier of the OFDM at the time kl(k)：
Figure FDA0002295353400000032
Wherein δ f is the normalized frequency deviation; h is0Attenuation for the first transmission path; n is the number of baseband discrete data samples contained in each OFDM symbol; j is a unit imaginary number; f. ofcIs the carrier frequency; l is the serial number of the subcarrier in the known CPRS sample value sequence; Δ fSCSIs the subcarrier spacing of the OFDM system; Δ t (k) is the time offset between the signal received by the receiver at the beginning of the OFDM symbol at time k and the signal generated by the receiver itself; tau is0(k) Time offset generated by Doppler in channel transmission for OFDM symbols at time k; gl(k) A radio channel frequency response for the l-th known CPRS sample sequence in the OFDM symbol at time k; xl(k) Is the l known CPRS sample value sequence in the OFDM symbol at the time k; wl(k) Additive white gaussian noise AWGN of the l-th known CPRS sample sequence in the OFDM symbol at time k; rl(k) Frequency domain signal of the first known CPRS sample sequence in the OFDM symbol at time k, xt(k) Time offset, x, of an OFDM symbol at time kf(k) Frequency offset, T, of OFDM symbols at time ksK is a positive integer for the sampling time interval;
according to the time of kOf the OFDM th subcarrier of (a) a scalar measurement y of the carrier phasel(k) The EKF measurement matrix H obtained by the expression equation of (a) is:
Figure FDA0002295353400000041
Figure FDA0002295353400000042
wherein HlEKF measurement matrix for the l sub-carrier, TsIs the sampling interval.
7. The method according to claim 6, wherein the obtaining a state vector predicted value and a covariance matrix predicted value at a time k +1 based on the state vector estimated value and the covariance matrix estimated value at the time k according to an EKF time update algorithm, and obtaining a scalar predicted value of the OFDM carrier phase at the time k +1 based on the state vector predicted value at the time k +1 and a preset EKF measurement matrix specifically comprises:
state vector estimation value of OFDM carrier phase based on k time
Figure FDA0002295353400000043
And a state transition matrix F (k) for calculating the predicted value of the state vector of the OFDM carrier phase at the moment of k +1
Figure FDA0002295353400000044
Comprises the following steps:
Figure FDA0002295353400000045
Figure FDA0002295353400000046
wherein, Δ t (k) is the time difference between the time k +1 and the time k;
calculating a covariance matrix predicted value P (k +1| k) at the moment k +1 according to the covariance matrix estimated value P (k | k) at the moment k and the state transition matrix F (k):
P(k+1|k)＝F(k)P(k|k)FT(k)+Q(k)
state vector predictor based on time k +1
Figure FDA0002295353400000047
And the EKF measurement matrix H of the l sub-carrierlObtaining scalar quantity predicted value of the carrier phase of the first sub-carrier of OFDM at the moment of k +1
Figure FDA0002295353400000048
Comprises the following steps:
Figure FDA0002295353400000049
wherein the content of the first and second substances,
Figure FDA00022953534000000410
time offset of the predicted OFDM symbol at time k + 1;
Figure FDA00022953534000000411
frequency offset of the OFDM symbol at the predicted k +1 moment; fT(k) Is the transposed matrix of F (k), and Q (k) is the process noise covariance at time k.
8. The method for tracking OFDM carrier phase according to claim 7, wherein the updating the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the time k +1 according to the extended kalman filter EKF measurement update algorithm specifically comprises:
state vector prediction value according to OFDM carrier phase at k +1 moment
Figure FDA0002295353400000051
An EKF gain matrix K (K +1) at time K +1, a scalar measurement y (K +1) of the OFDM carrier phase at time K +1, and a scalar prediction of the carrier phase at time K +1
Figure FDA0002295353400000052
Updating state vector estimation value at k +1 moment
Figure FDA0002295353400000053
Figure FDA0002295353400000054
Updating the covariance matrix estimation value P (K +1| K +1) at the time K +1 according to the covariance matrix prediction value P (K +1| K) at the time K +1, the EKF gain matrix K (K +1) at the time K +1, and the EKF measurement matrix H at the time K + 1:
P(k+1|k+1)＝[I-K(k+1)H]P(k+1|k)
wherein K (K +1) ═ P (K +1| K) HT×[HP(k+1|k)HT+Rl(k)]-1
HTA transposed matrix of H.
9. The OFDM carrier phase tracking method according to claim 6 or 7, wherein the estimated value of the time offset of the OFDM symbol at the initial time is an estimated value of the time offset of the OFDM symbol at the initial time
Figure FDA0002295353400000055
The estimation formula of (c) is as follows:
Figure FDA0002295353400000056
or the like, or, alternatively,
estimation of time offset of OFDM symbol at initial time
Figure FDA0002295353400000057
Setting according to the estimated value of the estimated time of arrival (TOA);
wherein ljIs the jth subcarrier number; liIs the ith subcarrier number;
Figure FDA0002295353400000058
frequency domain data of ith subcarrier of OFDM symbol at the time k;
Figure FDA0002295353400000059
the conjugate of the frequency domain data of the ith subcarrier of the OFDM symbol at the time k;
Figure FDA00022953534000000510
frequency domain data of the jth subcarrier of the OFDM symbol at the time k;
Figure FDA00022953534000000511
the conjugate of the frequency domain data for the jth subcarrier of the OFDM symbol at time k.
10. An apparatus for tracking a phase of an orthogonal frequency division multiplexing carrier, comprising:
the value acquisition module is used for acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and the tracking module is used for obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the method of:
acquiring a state vector predicted value of an Orthogonal Frequency Division Multiplexing (OFDM) carrier phase and a scalar measurement value of the OFDM carrier phase at each moment;
and obtaining the state vector estimated value of the OFDM carrier phase based on the state vector predicted value of the OFDM carrier phase at each moment and the scalar measured value of the OFDM carrier phase, and tracking the OFDM carrier phase.
12. The electronic device according to claim 11, wherein the obtaining a state vector estimate of OFDM carrier phases based on the state vector predictor of OFDM carrier phases at each time and a scalar measure of OFDM carrier phases, and tracking OFDM carrier phases, specifically comprises:
acquiring a scalar quantity predicted value of the OFDM carrier phase at the moment k +1 according to the state vector predicted value of the OFDM carrier phase at the moment k +1, calculating a difference value between the scalar quantity measured value of the OFDM carrier phase at the moment k +1 and the scalar quantity predicted value of the OFDM carrier phase at the moment k +1, and updating a state vector estimated value and a covariance matrix estimated value of the OFDM carrier phase at the moment k +1 according to an Extended Kalman Filter (EKF) measurement updating algorithm;
according to a terminal positioning algorithm, obtaining the OFDM carrier phase of the terminal at the k +1 moment based on the state vector estimation value of the OFDM carrier phase at the k +1 moment so as to realize the tracking of the OFDM carrier phase;
wherein k is a positive integer.
13. The electronic device of claim 12, wherein after obtaining the scalar predicted value of the OFDM carrier phase at the time k +1 according to the state vector predicted value of the OFDM carrier phase at the time k +1, calculating a difference between the scalar measured value of the OFDM carrier phase at the time k +1 and the scalar predicted value of the OFDM carrier phase at the time k +1, and updating the state vector estimated value and the covariance matrix estimated value of the OFDM carrier phase at the time k +1 according to an extended kalman filter EKF measurement update algorithm, the method further comprises:
the baseband discrete data samples at time k +2 are phase rotated based on the state vector estimate of the OFDM carrier phase at time k +1 to correct the baseband discrete data samples at time k +2 by a time and frequency offset.
14. The electronic device of claim 13, wherein phase rotating the baseband discrete data samples at time k +2 based on the state vector estimate at time k +1 to correct the baseband discrete data samples at time k +2 by time and frequency offsets comprises:
for the baseband discrete data sample x at time k +2 according to the following equationn(k +2) phase rotation to obtain a new sequence of data samples { z }n(k+2)}：
Figure FDA0002295353400000071
Wherein the content of the first and second substances,
Figure FDA0002295353400000072
is composed of
Figure FDA0002295353400000073
The phase rotation calculated is rotated by the phase of the signal,
Figure FDA0002295353400000074
j is a unit imaginary number; x is the number ofn(k +2) is the nth known CPRS sample sequence in the OFDM symbol at the time k + 2; n is the number of baseband discrete data samples contained in each OFDM symbol;
Figure FDA0002295353400000075
for the predicted time offset of the OFDM symbol at time k +2, Δ t (k +2) is the time offset between the signal received by the receiver at the beginning of the OFDM symbol at time k +2 and the signal generated by the receiver itself; f. ofcIs the carrier frequency; k is a positive integer.
15. The electronic device according to claim 12, wherein the obtaining the state vector predictor of the OFDM carrier phase and the scalar measure of the OFDM carrier phase at each time instant specifically comprises:
performing correlation calculation on a known carrier phase reference signal CPRS sample sequence in an OFDM symbol carried at the moment k and a frequency domain signal of a subcarrier corresponding to the sample sequence at the moment k to obtain a scalar measurement value of the OFDM carrier phase at the moment k, wherein the frequency domain signal of the subcarrier is obtained by performing fast Fourier transform on a baseband discrete data sample received at the moment k;
according to an EKF time updating algorithm, based on the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the time k, the state vector prediction value and the covariance matrix prediction value of the OFDM carrier phase at the time k +1 are obtained, and based on the state vector prediction value of the OFDM carrier phase at the time k +1 and a preset EKF measurement matrix, the scalar prediction value of the OFDM carrier phase at the time k +1 is obtained.
16. The electronic device according to claim 15, wherein the performing correlation calculation on the known carrier phase reference signal CPRS sample sequence in the OFDM symbol carried at the time k and the frequency domain signal of the subcarrier corresponding to the sample sequence at the time k to obtain the scalar measurement value of the OFDM carrier phase at the time k specifically includes:
obtaining a frequency domain signal R of a subcarrier corresponding to the l known CPRS sample value sequence in the OFDM symbol carried at the k momentl(k)：
Figure FDA0002295353400000081
For frequency domain signal Rl(k) And corresponding known CPRS sample sequence Xl(k) Performing correlation calculation to obtain scalar measurement value y of the carrier phase of the first subcarrier of the OFDM at the time kl(k)：
Figure FDA0002295353400000082
Wherein δ f is the normalized frequency deviation; h is0Attenuation for the first transmission path; n is a radical ofThe number of baseband discrete data samples contained for each OFDM symbol; j is a unit imaginary number; f. ofcIs the carrier frequency; l is the serial number of the subcarrier in the known CPRS sample value sequence; Δ fSCSIs the subcarrier spacing of the OFDM system; Δ t (k) is the time offset between the signal received by the receiver at the beginning of the OFDM symbol at time k and the signal generated by the receiver itself; tau is0(k) Time offset generated by Doppler in channel transmission for OFDM symbols at time k; gl(k) A radio channel frequency response for the l-th known CPRS sample sequence in the OFDM symbol at time k; xl(k) Is the l known CPRS sample value sequence in the OFDM symbol at the time k; wl(k) Additive white gaussian noise AWGN of the l-th known CPRS sample sequence in the OFDM symbol at time k; rl(k) Frequency domain signal of the first known CPRS sample sequence in the OFDM symbol at time k, xt(k) Time offset, x, of an OFDM symbol at time kf(k) Frequency offset, T, of OFDM symbols at time ksK is a positive integer for the sampling time interval;
scalar measurement y of the carrier phase from the first subcarrier of OFDM at time kl(k) The EKF measurement matrix H obtained by the expression equation of (a) is:
Figure FDA0002295353400000091
Figure FDA0002295353400000092
wherein HlEKF measurement matrix for the l sub-carrier, TsIs the sampling interval.
17. The electronic device according to claim 16, wherein the obtaining a state vector predicted value and a covariance matrix predicted value at a time k +1 based on the state vector estimated value and the covariance matrix estimated value at the time k according to the EKF time update algorithm, and obtaining a scalar predicted value of the OFDM carrier phase at the time k +1 based on the state vector predicted value at the time k +1 and a preset EKF measurement matrix specifically comprises:
state vector estimation value of OFDM carrier phase based on k time
Figure FDA0002295353400000093
And a state transition matrix F (k) for calculating the predicted value of the state vector of the OFDM carrier phase at the moment of k +1
Figure FDA0002295353400000094
Comprises the following steps:
Figure FDA0002295353400000095
Figure FDA0002295353400000096
wherein, Δ t (k) is the time difference between the time k +1 and the time k;
calculating a covariance matrix predicted value P (k +1| k) at the moment k +1 according to the covariance matrix estimated value P (k | k) at the moment k and the state transition matrix F (k):
P(k+1|k)＝F(k)P(k|k)FT(k)+Q(k)
state vector predictor based on time k +1
Figure FDA0002295353400000101
And the EKF measurement matrix H of the l sub-carrierlObtaining scalar quantity predicted value of the carrier phase of the first sub-carrier of OFDM at the moment of k +1
Figure FDA0002295353400000102
Comprises the following steps:
Figure FDA0002295353400000103
wherein the content of the first and second substances,
Figure FDA0002295353400000104
time offset of the predicted OFDM symbol at time k + 1;
Figure FDA0002295353400000105
frequency offset of the OFDM symbol at the predicted k +1 moment; fT(k) Is the transposed matrix of F (k), and Q (k) is the process noise covariance at time k.
18. The electronic device according to claim 17, wherein the updating the state vector estimation value and the covariance matrix estimation value of the OFDM carrier phase at the k +1 time according to the extended kalman filter EKF measurement update algorithm specifically includes:
state vector prediction value according to OFDM carrier phase at k +1 moment
Figure FDA0002295353400000106
An EKF gain matrix K (K +1) at time K +1, a scalar measurement y (K +1) of the OFDM carrier phase at time K +1, and a scalar prediction of the carrier phase at time K +1
Figure FDA0002295353400000107
Updating state vector estimation value at k +1 moment
Figure FDA0002295353400000108
Figure FDA0002295353400000109
Updating the covariance matrix estimation value P (K +1| K +1) at the time K +1 according to the covariance matrix prediction value P (K +1| K) at the time K +1, the EKF gain matrix K (K +1) at the time K +1, and the EKF measurement matrix H at the time K + 1:
P(k+1|k+1)＝[I-K(k+1)H]P(k+1|k)
wherein K (K +1) ═ P (K +1| K) HT×[HP(k+1|k)HT+Rl(k)]-1
HTA transposed matrix of H.
19. Electronic device according to claim 16 or 17, characterized in that the estimate of the time offset of the OFDM symbol at the initial instant
Figure FDA00022953534000001010
The estimation formula of (c) is as follows:
Figure FDA00022953534000001011
or the like, or, alternatively,
estimation of time offset of OFDM symbol at initial time
Figure FDA0002295353400000111
Setting according to the estimated value of the estimated time of arrival (TOA);
wherein ljIs the jth subcarrier number; liIs the ith subcarrier number;
Figure FDA0002295353400000112
frequency domain data of ith subcarrier of OFDM symbol at the time k;
Figure FDA0002295353400000113
the conjugate of the frequency domain data of the ith subcarrier of the OFDM symbol at the time k;
Figure FDA0002295353400000114
frequency domain data of the jth subcarrier of the OFDM symbol at the time k;
Figure FDA0002295353400000115
the conjugate of the frequency domain data for the jth subcarrier of the OFDM symbol at time k.
20. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method for tracking phases of orthogonal frequency division multiplexing carriers according to any of claims 1 to 9.
CN201911198870.3A 2019-11-25 2019-11-25 Method and device for tracking orthogonal frequency division multiplexing carrier phase and electronic equipment Active CN112835075B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911198870.3A CN112835075B (en) 2019-11-25 2019-11-25 Method and device for tracking orthogonal frequency division multiplexing carrier phase and electronic equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911198870.3A CN112835075B (en) 2019-11-25 2019-11-25 Method and device for tracking orthogonal frequency division multiplexing carrier phase and electronic equipment

Publications (2)

Publication Number Publication Date
CN112835075A true CN112835075A (en) 2021-05-25
CN112835075B CN112835075B (en) 2024-04-02

Family

ID=75922425

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911198870.3A Active CN112835075B (en) 2019-11-25 2019-11-25 Method and device for tracking orthogonal frequency division multiplexing carrier phase and electronic equipment

Country Status (1)

Country Link
CN (1) CN112835075B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1433167A (en) * 2002-01-09 2003-07-30 松下电器产业株式会社 OFDM communication equipment, OFDM communication method and OFDM communication program
US20040190637A1 (en) * 2003-03-28 2004-09-30 Maltsev Alexander A. System and method for adaptive phase compensation of OFDM signals
DE102010011415B3 (en) * 2010-03-15 2011-03-31 Deutsches Zentrum für Luft- und Raumfahrt e.V. Method for determination and for tracking characteristics of radio transmission channel, involves using extended Kalman-Filter for tracking of condition, with which estimation of condition takes place in prediction step
US20160006557A1 (en) * 2013-02-27 2016-01-07 Panasonic Corporation Reception apparatus, phase error estimation method, and phase error correction method
WO2018014602A1 (en) * 2016-07-19 2018-01-25 东南大学 Volume kalman filtering method suitable for high-dimensional gnss/ins deep coupling

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1433167A (en) * 2002-01-09 2003-07-30 松下电器产业株式会社 OFDM communication equipment, OFDM communication method and OFDM communication program
US20040190637A1 (en) * 2003-03-28 2004-09-30 Maltsev Alexander A. System and method for adaptive phase compensation of OFDM signals
CN1802831A (en) * 2003-03-28 2006-07-12 英特尔公司 Method and device for adaptive phase compensation of OFDM signals
DE102010011415B3 (en) * 2010-03-15 2011-03-31 Deutsches Zentrum für Luft- und Raumfahrt e.V. Method for determination and for tracking characteristics of radio transmission channel, involves using extended Kalman-Filter for tracking of condition, with which estimation of condition takes place in prediction step
US20160006557A1 (en) * 2013-02-27 2016-01-07 Panasonic Corporation Reception apparatus, phase error estimation method, and phase error correction method
WO2018014602A1 (en) * 2016-07-19 2018-01-25 东南大学 Volume kalman filtering method suitable for high-dimensional gnss/ins deep coupling

Also Published As

Publication number Publication date
CN112835075B (en) 2024-04-02

Similar Documents

Publication Publication Date Title
CN107211249B (en) Inter-frequency bias compensation for time difference measurements in position determination
CN109561495B (en) Time-frequency tracking method, user equipment and computer readable medium
WO2021042852A1 (en) Deviation elimination and acquisition method and device for carrier phase measurement, and receiver
CN102664841A (en) Method for SC-FDE (single carrier-frequency domain equalization) system low complexity RLS self-adaption channel estimation
CN104135333A (en) Time synchronization method of open loop network for TDMA (Time Division Multiple Address) node based on kalman filter
EP4182904A1 (en) Systems and methods for multicarrier phase-based localization
CN109495414B (en) Frequency offset estimation method, device and equipment and computer readable storage medium
US20210219258A1 (en) Methods and apparatuses for communicating position reference signals
WO2021143644A1 (en) Carrier phase tracking method and apparatus
EP2887599B1 (en) Method and receiver for estimating and correcting fractional frequency offset in ofdm
Wang et al. Carrier phase tracking architecture for positioning in LTE networks under channel fading conditions
CN111294917B (en) Method, device, storage medium and user equipment for estimating timing deviation based on PDCCH
CN112835075B (en) Method and device for tracking orthogonal frequency division multiplexing carrier phase and electronic equipment
EP3816672A1 (en) Adaptive weighting matrix for adaptive tracking loops
Xu et al. Doppler rate estimation scheme for UFMC based LEO satellite communication system
CN113691477B (en) Carrier phase tracking method and device
Lee et al. Low-complexity leakage-based carrier frequency offset estimation techniques for OFDMA uplink systems
US9769687B2 (en) Method and related mobile device for determining a reference signal received power
CN111371713A (en) Method for resisting Doppler frequency shift and related equipment
US20230341505A1 (en) Methods and apparatus for positioning of a moving unmanned aerial vehicle
WO2023000168A1 (en) Method and device of anenna calibration for radio system
WO2024067434A1 (en) Rs configuration method and apparatus, terminal, and network side device
Diaz et al. A new approach to sign-bit-based parameter estimation in OFDM receivers
Carvajal et al. On maximum likelihood estimation of channel impulse response and carrier frequency offset in OFDM systems
JP2011101167A (en) Radio communication apparatus

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant