CN105607091B - A kind of improved carrier tracking loop based on EKF - Google Patents

A kind of improved carrier tracking loop based on EKF Download PDF

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CN105607091B
CN105607091B CN201610079931.4A CN201610079931A CN105607091B CN 105607091 B CN105607091 B CN 105607091B CN 201610079931 A CN201610079931 A CN 201610079931A CN 105607091 B CN105607091 B CN 105607091B
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local
phase
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CN105607091A (en
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田甜
于志杰
赵鹏
张金巍
朱倩
崔超
赵欢
程立明
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BEIJING SUNWISE INFORMATION TECHNOLOGY Co Ltd
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BEIJING SUNWISE INFORMATION TECHNOLOGY Co Ltd
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    • 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

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  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
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  • Digital Transmission Methods That Use Modulated Carrier Waves (AREA)

Abstract

A kind of improved carrier tracking loop based on EKF, including baseband signal pretreatment module, matrix computations module, EKF module and local NCO.Baseband signal pretreatment module is pre-processed to obtain observation according to local carrier signal to signal I and signal Q in each period T, matrix computations module calculates linearisation matrix H, the estimate of phase and frequency is calculated by EKF module, exported for local NCO generations local carrier signal and give baseband signal pretreatment module, realize the carrier track in each period T.The present invention is aided in without phase discriminator, and suitable for track demand during modulation intelligence in the case of low signal-to-noise ratio, high dynamic be present, hardware is realized simply, can effectively improve the operating rate of whole carrier tracking loop, while reduce resource consumption.

Description

Improved carrier tracking loop based on extended Kalman filtering
Technical Field
The invention relates to a carrier tracking loop based on extended Kalman filtering, and belongs to the field of carrier tracking of communication receivers.
Background
Under a high dynamic application environment, the relative motion track between a receiver carrier and a satellite has severe nonlinear change and has large speed, acceleration and jerk, so that signals received by a communication receiver contain large Doppler frequency shift and high-order derivatives thereof, and a tracking loop is extremely easy to lose lock. In general, to accommodate large input signal doppler dynamics, the carrier tracking loop needs to increase the loop bandwidth, but in low signal to noise ratio situations, increasing the loop bandwidth results in increased noise entering the loop, which is more prone to loss of lock.
In a high dynamic carrier tracking algorithm based on digital signal processing, compared with the fixed loop bandwidth characteristics of a Phase Locked Loop (PLL) and a Frequency Locked Loop (FLL), a carrier tracking loop based on kalman filtering can adaptively change the loop bandwidth, so that the loop characteristics can adaptively change according to the dynamics of an input signal, and thus, the carrier tracking algorithm has better tracking performance. However, the existing linear kalman filtering algorithm needs the assistance of a phase discriminator, and the introduction of the phase discriminator needs a loop to meet enough signal-to-noise ratio to ensure normal operation, so that the application under the condition of low signal-to-noise ratio is limited. The carrier tracking algorithm based on the extended Kalman filtering does not need the assistance of a phase discriminator, but the existing algorithm assumes that the signal amplitude is constant and is only suitable for the single carrier tracking condition without integration-zero clearing processing. Tracking loops where modulation information is present are not suitable for high dynamic situations.
Disclosure of Invention
The technical problem solved by the invention is as follows: the defects of the prior art are overcome, the improved carrier tracking loop based on the extended Kalman filtering is provided, the phase discriminator is not needed for assistance, and the method is suitable for the tracking requirement when modulation information exists under the conditions of low signal-to-noise ratio and high dynamic.
The technical solution of the invention is as follows: an improved carrier tracking loop based on extended Kalman filtering comprises a baseband signal preprocessing module, a matrix calculation module, an extended Kalman filtering module and a local NCO;
the baseband signal preprocessing module receives a local carrier signal output by a local NCO in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver, and in each time period T, the signal I and the signal Q are preprocessed according to the local carrier signal to obtain an update value of the in-phase branch signalSum quadrature branch signal update valueAnd further obtaining an observation matrix Z and outputting the observation matrix Z to a matrix calculation module and an extended Kalman filtering module, wherein
In each time period T, the matrix calculation module carries out frequency estimation on the input signal output by the extended Kalman filter module in the last time period according to the observation matrix Z, the frequency value of the local NCO output by the local NCO and the frequency estimation value f of the input signal output by the extended Kalman filter module in the last time period 0 And frequency change rate estimate f 1 Calculating a linearization matrix H, and outputting the linearization matrix H to the extended Kalman filtering module; the input signal refers to an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of the receiver;
in each time period T, the extended Kalman filtering module calculates a filtering result and sends the filtering result to the local NCO according to the linearized matrix H, the observation matrix Z, the frequency value of the local NCO output by the local NCO, the phase adjustment quantity of the local NCO at the beginning of the time period T and the filtering result of the previous time period, wherein the filtering result comprises a phase difference estimated value theta of an input signal and a local carrier signal e Inputting a signal frequency estimation value and a frequency change rate estimation value, and sending the input signal frequency estimation value and the frequency change rate estimation value to a matrix calculation module;
the local NCO outputs the frequency value of the local NCO when generating the local carrier signal to the matrix calculation module in each time period T, and outputs the frequency value of the local NCO when generating the local carrier signal and the phase adjustment quantity of the local NCO at the beginning of the T time period to the extended Kalman filtering module; and generating a local carrier signal of the current time period T according to the filtering result sent by the last time period extended Kalman filtering module, and outputting the local carrier signal to the baseband signal preprocessing module to realize carrier tracking in each time period T.
The matrix calculation module is used for calculating the frequency estimation value f of the input signal output by the extended Kalman filter module in each time period T according to the observation matrix Z, the frequency value of the local NCO output by the local NCO and the frequency estimation value f of the input signal output by the extended Kalman filter module in the previous time period 0 And rate of change of frequencyEstimated value f 1 The implementation of calculating the linearization matrix H is:
wherein:
h 1 ＝-Z 2
h 4 ＝Z 1
wherein the content of the first and second substances,f nco the frequency value of the local NCO output by the local NCO.
In each time period T, the extended Kalman filtering module calculates a filtering result according to the linearized matrix H, the observation matrix Z, the frequency value of the local NCO output by the local NCO and the phase adjustment quantity of the local NCO at the beginning of the time period T in the following implementation mode:
the extended Kalman filtering module calculates and generates an intermediate vector in the kth time period T according to the frequency value of the local NCO output by the local NCO, the phase adjustment quantity of the local NCO at the beginning of the kth time period and the filtering result of the kth time periodAnd
whereinΔθ 2 The phase adjustment quantity of the local NCO at the beginning of the kth time period;
the extended Kalman filtering module is used for carrying out linear matrix H according to the k time period T k Observation matrix Z k Calculating the filtering result of the k time segment
k =1,2,3,4 \8230, N is a natural number greater than 1, and the filtering result of the 0 th time segment is a frequency estimation value output by an acquisition module in the receiver.
The baseband signal preprocessing module comprises an in-phase branch preprocessing module, a symbol decision module, a first multiplier, a second multiplier and an orthogonal branch preprocessing module;
the in-phase branch preprocessing module receives a local carrier signal output by a local NCO in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver, and preprocesses the signal I according to the local carrier signal and the signal Q in each time period T to obtain an in-phase branch tracking signal I Tracking And output to the symbol decision module and the first multiplier;
the symbol decision module tracks the signal I to the cophase branch Tracking Making symbol decision to obtain decision signalOutputting to a first multiplier and a second multiplier;
the first multiplier tracks the in-phase branch with the signal I Tracking And a decision signalMultiplying to obtain the updated value of the in-phase branch signalOutputting the data to a matrix calculation module and an extended Kalman filtering module;
the orthogonal branch preprocessing module receives a local carrier signal output by a local NCO in real time and an in-phase branch signal I and an orthogonal branch signal Q output by a down-conversion module at the front end of a receiver, and preprocesses the signal Q according to the local carrier signal and the signal I in each time period T to obtain an orthogonal branch tracking signal Q Tracking And output to the second multiplier; the second multiplier tracks the quadrature branch with the signal Q Tracking And a decision signalMultiplying to obtain updated value of orthogonal branch signalOutput to the matrix calculationThe device comprises a module and an extended Kalman filtering module.
The symbol decision module tracks the signal I to the cophase branch Tracking Making symbol decision to obtain decision signalThe implementation mode of the method is as follows:
the in-phase branch preprocessing module comprises a first phase rotation module and a first integral zero clearing module;
the first phase rotation module receives a local carrier signal output by a local NCO in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver, performs phase rotation on the in-phase branch signal I according to the local carrier signal and the quadrature branch signal Q, and outputs an obtained signal to the first integral zero clearing module;
the first integral zero clearing module carries out integral zero clearing on the signal after phase rotation in each time period T to obtain an in-phase branch tracking signal I with phase and frequency estimation errors Tracking And output to the first multiplier and the symbol decision module;
the orthogonal branch preprocessing module comprises a second phase rotation module and a second integral zero clearing module;
the second phase rotation module receives a local carrier signal output by the local NCO in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of the receiver, performs phase rotation on the quadrature branch signal Q according to the local carrier signal and the in-phase branch signal I, and outputs the obtained signal to the second integral zero clearing module;
the second integral zero clearing module carries out integral zero clearing on the signal after the phase rotation in each time period T to obtain a quadrature branch tracking signal Q with phase and frequency estimation frequency difference Tracking And output to the second multiplier.
Compared with the prior art, the invention has the advantages that:
(1) The carrier tracking loop can realize carrier tracking in each time period T through the cooperation of the baseband signal preprocessing module, the extended Kalman filtering module and the local NCO module, is suitable for the carrier tracking condition with modulation information, simultaneously utilizes the extended Kalman filtering to carry out carrier tracking without the assistance of a phase discriminator, has more excellent tracking performance under the condition of low signal-to-noise ratio, can realize the self-adaptive change of the loop bandwidth of a tracking loop according to the dynamic range of an input signal, and realizes smaller tracking error by using the more excellent loop bandwidth.
(2) Compared with the traditional calculation method of the linearization matrix in the extended Kalman filtering, the calculation method of the linearization matrix in the extended Kalman filtering is simple and direct, is easy to realize by hardware, can effectively improve the running speed of the whole carrier tracking loop, and simultaneously reduces the resource consumption of the carrier tracking loop.
(3) The extended Kalman filtering module carries out filtering processing on the frequency estimation value and the frequency change rate estimation value of the input signal in the previous time period in each time period T, and realizes unbiased estimation and tracking of the frequency change rate estimation value, so that the carrier tracking loop is suitable for the tracking environment under the high dynamic condition.
(4) The signal processed by the in-phase branch preprocessing module of the baseband signal preprocessing module is judged by the symbol judgment module to obtain a judgment signal, and the judgment signal is respectively multiplied by the signal processed by the in-phase branch preprocessing module and the signal processed by the orthogonal branch preprocessing module to obtain an in-phase branch signal update valueSum quadrature branch signal updateCan be effectively usedThe influence of modulation information is eliminated, so that the carrier tracking loop is suitable for a general communication system with modulation information, and has wider application range compared with the existing carrier tracking loop under the low signal-to-noise ratio high dynamic environment.
(5) The in-phase branch preprocessing module and the quadrature branch preprocessing module respectively consist of the phase rotation module and the integral zero clearing module, so that branch tracking signals with phase and frequency estimation errors are obtained, the processing requirement on modulation information signals under the high dynamic condition is met, and the method is suitable for tracking loops with low signal-to-noise ratio and high dynamic condition and modulation information.
Drawings
FIG. 1 is a schematic diagram of a carrier tracking loop of the present invention;
fig. 2 is a schematic diagram of a baseband signal preprocessing module.
Detailed Description
The invention provides an improved carrier tracking loop based on extended Kalman filtering, which comprises a baseband signal preprocessing module 1, a matrix calculation module 3, an extended Kalman filtering module 4 and a local NCO 5, as shown in figure 1.
The baseband signal preprocessing module 1 receives a local carrier signal output by a local NCO 5 and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver in real time, and within each time period T, the signal I and the signal Q are preprocessed according to the local carrier signal to obtain an in-phase branch signal update valueSum quadrature branch signal update valueFurther obtain an observation matrix Z and output the observation matrix Z to the matrix calculation module 3 and the extended Kalman filtering module 4, wherein
The matrix calculation module 3 calculates the frequency estimation value f of the input signal output by the extended Kalman filter module 4 in each time period T according to the observation matrix Z, the frequency value of the local NCO output by the local NCO 5 and the frequency estimation value f of the input signal output by the extended Kalman filter module 4 in the last time period 0 And frequency change rate estimate f 1 Calculating a linearization matrix H, and outputting the linearization matrix H to the extended Kalman filtering module 4; the input signals refer to an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of the receiver.
The implementation of calculating the linearization matrix H is:
wherein:
h 1 ＝-Z 2
h 4 ＝Z 1
wherein, the first and the second end of the pipe are connected with each other,f nco to a local NCOThe output frequency value of the local NCO.
In each time period T, the extended Kalman filtering module 4 obtains a filtering result in each time period T and sends the filtering result to the local NCO 5 according to the linearized matrix H, the observation matrix Z, the frequency value of the local NCO output by the local NCO 5, the phase adjustment quantity of the local NCO at the beginning of the time period T and the filtering result of the previous time period, wherein the filtering result comprises a phase difference estimation value theta of an input signal and a local carrier signal e An input signal frequency estimate and a frequency rate of change estimate. And simultaneously sends the frequency estimation value and the frequency change rate estimation value of the input signal to the matrix calculation module 3.
In the kth time period T, the extended Kalman filtering module 4 calculates and generates an intermediate vector according to the frequency value of the local NCO output by the local NCO 5, the phase adjustment quantity of the local NCO at the beginning of the time period T and a filtering result in the kth-1 time periodAnd
wherein
The extended Kalman filtering module 4 calculates a filtering result in the kth time period T according to the linearized matrix H and the observation matrix Z
k =1,2,3,4 \8230, 8230, N is a natural number more than 1. The filtering result of the 0 th time segment is a rough frequency estimation value output by the internal acquisition module of the receiver.
The local NCO 5 outputs the frequency value of the local NCO when generating the local carrier signal to the matrix calculation module 3 in each time period T, outputs the frequency value of the local NCO when generating the local carrier signal and the phase adjustment quantity of the local NCO at the beginning of the time period T to the extended Kalman filtering module 4, and generates the local carrier signal in the current time period T according to the filtering result sent by the extended Kalman filtering module 4 in the previous time period (the initial phase and the frequency are respectively theta) 2 And f nco The cos value and the sin value) of the local carrier, and outputs the cos value and the sin value to the baseband signal preprocessing module 1, thereby realizing carrier tracking in each time period T.
As shown in fig. 2, the baseband signal preprocessing module 1 includes an in-phase branch preprocessing module 11, a symbol decision module 12, a first multiplier 13, a second multiplier 14, and a quadrature branch preprocessing module 15, where the in-phase branch preprocessing module 11 includes a first phase rotation module 111 and a first integral zero clearing module 112. The first phase rotation module 111 receives a local carrier signal output by the local NCO 5 and an in-phase branch signal I and a quadrature branch signal Q output by a receiver front-end down-conversion module in real time in each time period T, and uses a formula Qcos ω nT according to the local carrier signal and the quadrature branch signal Q s +IsinωnT s ω is the frequency value when generating the local carrier, n is the count value of the sampling point, T s For the same-phase branch signal I for the carrier ring sampling periodThe line phase is rotated and the resulting signal is output to the first integrate and dump block 112. The first integral zero clearing module 112 performs integral zero clearing on the phase-rotated signal in each time period T to obtain an in-phase branch tracking signal I with phase and frequency estimation errors Tracking And output to the first multiplier 13 and the symbol decision block 12. The symbol decision module 12 tracks the signal I for the in-phase branch Tracking Making symbol decision to obtain decision signalAnd outputs to the first multiplier 13 and the second multiplier 14. The in-phase branch tracking signal I is provided by a first multiplier 13 Tracking And a decision signalMultiplying to obtain the updated value of the in-phase branch signalAnd the output is sent to a matrix calculation module 3 and an extended Kalman filtering module 4.
The symbol decision module 12 tracks the signal I for the in-phase branch Tracking Making symbol decision to obtain decision signalThe implementation mode of the method is as follows:
the quadrature branch pre-processing block 15 comprises a second phase rotation block 151 and a second integrate and dump block 152. The second phase rotation module 151 receives the local carrier signal output by the local NCO 5 in real time and the in-phase branch signal I and the quadrature branch signal Q output by the down-conversion module at the front end of the receiver, and uses the formula Icos ω nT according to the local carrier signal and the in-phase branch signal I s -QsinωnT s The quadrature branch signal Q is phase-rotated, and the obtained signal is output to the second integrate and clear module 152. The second integrate and dump module 152 is atIn each time period T, the signal after phase rotation is subjected to integral zero clearing to obtain a quadrature branch tracking signal Q with phase and frequency estimation frequency difference Tracking And output to the second multiplier 14. The second multiplier 14 tracks the quadrature branch signal Q Tracking And a decision signalMultiplying to obtain updated value of orthogonal branch signalAnd the output is sent to a matrix calculation module 3 and an extended Kalman filtering module 4.
Example (b):
the local NCO 5 generates an initial phase and a frequency theta in the current T time period (assumed as the kth T time period) 2 And f nco The cos value and the sin value of the local carrier are output to the first phase rotation module 111 and the second phase rotation module 151, the first phase rotation module 111 uses the local carrier and the in-phase branch signal I and the quadrature branch signal Q output by the receiver front-end down-conversion module to generate a signal Icos-Qsin, the second phase rotation module 151 uses the local carrier and the in-phase branch signal I and the quadrature branch signal Q output by the receiver front-end down-conversion module to generate a signal Qcos + Isin, and then the signal Icos-Qsin is subjected to integration-zero clearing processing for a time length of T through the first integration zero clearing module 112 to obtain an in-phase branch tracking signal I k And outputs to the first multiplier 13 and the symbol decision block 12. The signal Qcos + Isin is subjected to integration-zero clearing processing for T duration by the second integration zero clearing module 152 to obtain the tracking signal Q of the quadrature branch k And output to the second multiplier 14.
The symbol decision module 12 tracks the signal I to the in-phase branch k The symbol decision is obtained by using the following formulaOutput to the first multiplier 13 and the second multiplier 14:
the first multiplier 13 willAnd I k Multiplying to obtain the updated value of the in-phase branch signalThe second multiplier 14 willAnd Q k Multiplying to obtain updated value of orthogonal branch signalFurther obtaining an observation matrix Z of the extended Kalman filtering k And output to the matrix calculation module 3:
suppose a signal I k &gt, 0, then
The matrix calculation module 3 calculates a linearization matrix H in the extended kalman filter during the current time period T by using the following formula:
wherein:
wherein T is integral zero clearing time, f 0 As an estimate of the frequency of the input signal, f 1 For frequency rate of change estimationValue f nco Is the frequency value of the local NCO when a local carrier is generated during the current T period.
And the extended Kalman filtering module 4 carries out filtering according to an extended Kalman filtering equation to obtain a filtering result of the current T time period. Wherein
The state transition matrix is
Δθ 2 The phase adjustment amount of the local NCO at the beginning of the current k-th time period is obtained.
The filtering process is as follows:
the time update equation is
The state update equation is
When k is&When the ratio is more than gt and 1,as a result of the filtering for the (k-1) th time segment, when k =1,is the frequency estimation value output by the internal acquisition module of the receiver.
And the local NCO 5 calculates the initial phase and the frequency of the local NCO in the k +1 th T time period according to the k time period filtering result:
according to theta 2 (k + 1) and f nco (k + 1), the local NCO generates a sine value and a cosine value of a local carrier wave in the (k + 1) th T time period, and the sine value and the cosine value are used for the phase rotation and integral-zero clearing process in the (k + 1) th T time period. And circulating in this way, and realizing carrier tracking in each time period T.
Compared with a phase-locked loop and a linear Kalman filtering carrier tracking loop assisted by a phase discriminator, the phase-locked loop has lower out-of-lock probability and smaller phase tracking error; compared with the existing extended Kalman filtering loop, the carrier tracking loop can be suitable for the condition of signed modulation and is more suitable for a general communication system. Meanwhile, in the structural design of the tracking loop, the H matrix is constructed by utilizing the observed value information, so that a filtering equation is improved, the loop filtering calculation process is greatly simplified, the operation amount is reduced, the hardware implementation is easy, the operation rate of the whole carrier tracking loop can be effectively improved, and the resource consumption is reduced.
Those skilled in the art will appreciate that those matters not described in detail in the present specification are well known in the art.

Claims (5)

1. An improved carrier tracking loop based on extended Kalman filtering is characterized in that: the system comprises a baseband signal preprocessing module (1), a matrix calculation module (3), an extended Kalman filtering module (4) and a local NCO (5);
the baseband signal preprocessing module (1) receives a local carrier signal output by a local NCO (5) in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver, and in each time period T, the signal I and the signal Q are preprocessed according to the local carrier signal to obtain an update value of the in-phase branch signalSum quadrature branch signal updateFurther obtaining an observation matrix Z and outputting the observation matrix Z to a matrix calculation module (3) and an extended Kalman filtering module (4), wherein
In each time period T, the matrix calculation module (3) performs frequency estimation on the input signal output by the last time period expansion Kalman filtering module (4) according to the observation matrix Z, the frequency value of the local NCO output by the local NCO (5) and the frequency estimation value f of the input signal output by the last time period expansion Kalman filtering module (4) 0 And frequency change rate estimate f 1 Calculating a linearization matrix H, and outputting the linearization matrix H to the extended Kalman filtering module (4); the input signal refers to an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of the receiver;
the implementation of calculating the linearization matrix H is:
wherein:
h 1 ＝-Z 2
h 4 ＝Z 1
wherein, the first and the second end of the pipe are connected with each other,f nco the frequency value of the local NCO output by the local NCO (5);
in each time period T, the extended Kalman filtering module (4) calculates a filtering result and sends the filtering result to the local NCO (5) according to the linearized matrix H, the observation matrix Z, the frequency value of the local NCO output by the local NCO (5), the phase adjustment amount of the local NCO at the beginning of the time period T and the filtering result of the previous time period, wherein the filtering result comprises a phase difference estimated value theta of an input signal and a local carrier signal e Inputting a signal frequency estimation value and a frequency change rate estimation value, and sending the input signal frequency estimation value and the frequency change rate estimation value to a matrix calculation module (3);
the local NCO (5) outputs the frequency value of the local NCO when a local carrier signal is generated to the matrix calculation module (3) in each time period T, and outputs the frequency value of the local NCO when the local carrier signal is generated and the phase adjustment quantity of the local NCO when the time period T starts to the extended Kalman filter module (4); and generating a local carrier signal of the current time period T according to the filtering result sent by the last time period extended Kalman filtering module (4), and outputting the local carrier signal to the baseband signal preprocessing module (1) to realize carrier tracking in each time period T.
2. The improved extended kalman filter-based carrier tracking loop according to claim 1, wherein: in each time period T, the extended Kalman filtering module (4) calculates a filtering result according to the linearization matrix H, the observation matrix Z, the frequency value of the local NCO output by the local NCO (5) and the phase adjustment quantity of the local NCO at the beginning of the time period T in the following implementation mode:
the extended Kalman filtering module (4) calculates and generates an intermediate vector according to the frequency value of the local NCO output by the local NCO (5) in the kth time period T, the phase adjustment quantity of the local NCO at the beginning of the kth time period and the filtering result of the kth time periodAnd
whereinΔθ 2 The phase adjustment quantity of the local NCO at the beginning of the kth time period;
the extended Kalman filtering module (4) is used for linearizing a matrix H in a kth time period T k And an observation matrix Z k Calculating a filtering result of the kth time segment
k =1,2,3,4 \8230, N is a natural number greater than 1, and the filtering result of the 0 th time segment is a frequency estimation value output by an acquisition module in the receiver.
3. The improved extended kalman filter-based carrier tracking loop according to claim 1, wherein: the baseband signal preprocessing module (1) comprises an in-phase branch preprocessing module (11), a symbol decision module (12), a first multiplier (13), a second multiplier (14) and a quadrature branch preprocessing module (15);
the in-phase branch preprocessing module (11) receives a local carrier signal output by a local NCO (5) and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver in real time, and preprocesses the signal I according to the local carrier signal and the signal Q in each time period T to obtain an in-phase branch tracking signal I Tracking And outputs the signal to a symbol decision module (12) and a first multiplier (13);
a symbol decision module (12) tracks a signal I for the in-phase branch Tracking Making symbol decision to obtain decision signalOutput to a first multiplier (13) and a second multiplier (14);
a first multiplier (13) tracks the in-phase branch signal I Tracking And a decision signalMultiplying to obtain the updated value of the in-phase branch signalOutput to a matrix meterThe system comprises a calculation module (3) and an extended Kalman filtering module (4);
the orthogonal branch preprocessing module (15) receives a local carrier signal output by a local NCO (5) in real time and an in-phase branch signal I and an orthogonal branch signal Q output by a down-conversion module at the front end of a receiver, and preprocesses the signal Q according to the local carrier signal and the signal I in each time period T to obtain an orthogonal branch tracking signal Q Tracking And output to a second multiplier (14); a second multiplier (14) tracks the quadrature branch signal Q Tracking And a decision signalMultiplying to obtain updated value of orthogonal branch signalAnd the output is sent to a matrix calculation module (3) and an extended Kalman filtering module (4).
4. The improved extended kalman filter based carrier tracking loop according to claim 3, wherein: a symbol decision module (12) tracks a signal I for the in-phase branch Tracking Making symbol decision to obtain decision signalThe implementation mode of the method is as follows:
5. the improved extended kalman filter based carrier tracking loop according to claim 3, wherein: the in-phase branch preprocessing module (11) comprises a first phase rotation module (111) and a first integral zero clearing module (112);
the first phase rotation module (111) receives a local carrier signal output by a local NCO (5) in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of a receiver, performs phase rotation on the in-phase branch signal I according to the local carrier signal and the quadrature branch signal Q, and outputs the obtained signal to the first integral zero clearing module (112);
the first integral zero clearing module (112) carries out integral zero clearing on the signal after the phase rotation in each time period T to obtain an in-phase branch tracking signal I with phase and frequency estimation errors Tracking And outputs to the first multiplier (13) and the symbol decision module (12);
the orthogonal branch preprocessing module (15) comprises a second phase rotation module (151) and a second integral zero clearing module (152);
the second phase rotation module (151) receives a local carrier signal output by the local NCO (5) in real time and an in-phase branch signal I and a quadrature branch signal Q output by a down-conversion module at the front end of the receiver, performs phase rotation on the quadrature branch signal Q according to the local carrier signal and the in-phase branch signal I, and outputs an obtained signal to the second integral zero clearing module (152);
the second integral zero clearing module (152) carries out integral zero clearing on the signals after phase rotation in each time period T to obtain the orthogonal branch tracking signal Q with phase and frequency estimation frequency difference Tracking And output to a second multiplier (14).
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