CN112986924A - OFDM radar communication integrated range and speed fast super-resolution estimation method - Google Patents

OFDM radar communication integrated range and speed fast super-resolution estimation method Download PDF

Info

Publication number
CN112986924A
CN112986924A CN202110121380.4A CN202110121380A CN112986924A CN 112986924 A CN112986924 A CN 112986924A CN 202110121380 A CN202110121380 A CN 202110121380A CN 112986924 A CN112986924 A CN 112986924A
Authority
CN
China
Prior art keywords
distance
matrix
speed
estimation
ofdm
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
CN202110121380.4A
Other languages
Chinese (zh)
Other versions
CN112986924B (en
Inventor
刘永军
刘旭宸
廖桂生
李海川
王椿富
唐皓
陈毓锋
姜孟超
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.)
Xidian University
Original Assignee
Xidian University
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 Xidian University filed Critical Xidian University
Priority to CN202110121380.4A priority Critical patent/CN112986924B/en
Publication of CN112986924A publication Critical patent/CN112986924A/en
Application granted granted Critical
Publication of CN112986924B publication Critical patent/CN112986924B/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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/26—Systems using multi-frequency codes
    • H04L27/2601—Multicarrier modulation systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Mathematical Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Algebra (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Signal Processing (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a method for estimating the distance speed and the super resolution of OFDM radar communication integration, which comprises the following steps: constructing an OFDM radar communication integrated signal model, and inputting relevant parameters for the OFDM radar communication integrated signal model; carrying out communication information compensation on the OFDM radar communication integrated signal model to obtain a communication information compensation model; carrying out frequency smoothing processing on the communication information compensation model to obtain a target signal model of the distance and the speed to be estimated; and performing distance estimation and speed estimation on the target signal model based on a super-resolution method to obtain a distance estimation result and a speed estimation result with super-resolution. The invention utilizes the OFDM radar communication integrated waveform to carry out distance and speed super-resolution estimation, and has lower calculation complexity compared with the prior method under the condition of equal estimation precision.

Description

OFDM radar communication integrated range and speed fast super-resolution estimation method
Technical Field
The invention belongs to the field of radar and communication interdisciplinary science, and particularly relates to a range speed fast super-resolution estimation method integrating OFDM radar communication.
Background
Orthogonal Frequency Division Multiplexing (OFDM) waveforms have been widely used in digital television broadcasting, digital video broadcasting, digital audio broadcasting, and Wireless Local Area Network (WLAN). In addition, OFDM has also attracted a great deal of attention in radar applications. Such as Synthetic Aperture Radar (SAR) imaging, Space-time adaptive Processing (STAP), Radar target parameter estimation, and Radar target detection. Therefore, it is reasonable to use the OFDM waveform as a radar communication integrated waveform. In addition, many scholars have conducted intensive research on OFDM radar communication integrated waveforms.
In the intelligent transportation system, the intelligent vehicles need to communicate with other vehicles, and at the same time, need to estimate their distances, speeds, and the like, in order to improve driving safety. Because the OFDM radar communication integrated waveform is the traditional OFDM communication waveform, the OFDM communication waveform is widely applied in practical application, and the communication function can be realized by utilizing the existing processing method. In contrast, the distance and speed of other vehicles cannot be estimated directly using the conventional radar parameter estimation method because the OFDM radar communication integrated waveform is different from the OFDM waveform in the radar. The OFDM radar waveform is generally a pulse waveform, and although researchers have studied the radar communication integration waveform of the pulse OFDM, it is found that the data transmission rate is low. On the other hand, the OFDM radar communication integrated waveform contains communication information, and each OFDM symbol is usually different, whereas the OFDM radar waveform does not need to transmit communication information, and each transmitted pulse is the same. Due to these differences, the range and speed estimation method of the OFDM radar cannot be directly applied to the radar communication integration system.
In order to estimate the distance and speed using an OFDM radar communication integrated waveform, c.sturm and w.wisebeck propose a method of dividing a received modulation symbol by a transmitted modulation symbol and then using Inverse Discrete Fourier Transform (IDFT) and Discrete Fourier Transform (DFT). In this method, the range resolution is determined by the signal bandwidth and the velocity resolution is determined by the Coherent Processing Interval (CPI). However, to improve distance and speed resolution, the signal bandwidth and CPI need to be increased, respectively. However, increasing the signal bandwidth increases the hardware implementation cost and the consumption of frequency resources, while increasing CPI decreases the update rate. In addition, the CPI may not be increased due to fluctuations in the target.
Disclosure of Invention
In order to estimate the distance and the speed by utilizing an OFDM radar communication integrated waveform, the invention provides a method for quickly estimating the distance and the speed based on OFDM radar communication integration.
The technical problem to be solved by the invention is realized by the following technical scheme:
an OFDM radar communication integrated range speed fast super-resolution estimation method comprises the following steps:
step A: constructing an OFDM radar communication integrated signal model, and inputting relevant parameters for the OFDM radar communication integrated signal model;
and B: carrying out communication information compensation on the OFDM radar communication integrated signal model to obtain a communication information compensation model;
and C: carrying out frequency smoothing processing on the communication information compensation model to obtain a target signal model of the distance and the speed to be estimated;
step D: and performing distance estimation and speed estimation on the target signal model based on a super-resolution method to obtain a distance estimation result and a speed estimation result with super-resolution.
In one embodiment, the communication information compensation model is expressed as:
Figure BDA0002922147850000031
wherein N istTo point target number, xiiIs the attenuation factor due to the propagation loss, scattering and radar cross-sectional area of the ith target, i is 0,1, …, Nt-1；
Figure BDA0002922147850000032
Figure BDA0002922147850000033
Figure BDA0002922147850000034
Figure BDA0002922147850000035
Is a Gaussian noise vector; j is the imaginary symbol, e is the natural base number, Δ f is the subcarrier spacing, fcIs the carrier frequency, RiIs the distance of the ith target, viIs the relative velocity of the ith target, c is the signal propagation velocity; t issFor the length of a complete OFDM symbol, TgIs the length of the cyclic prefix CP in the complete OFDM symbol, Tp＝NsTs，NsFor the number of complete OFDM symbols, NcIs the number of subcarriers; n is the number of valid OFDM symbols in the complete OFDM symbol, p is the number of pulses,
Figure BDA0002922147850000036
the superscript T in the expression of (a) denotes a matrix transposition.
In one embodiment, the target signal model collects column vectors for the kth through (k + M-1) th received signals;
the expression of the target signal model is as follows;
Figure BDA0002922147850000037
wherein,
Figure BDA0002922147850000038
k＝0,1,…,Nc-M; m is the number of subcarriers;
Figure BDA0002922147850000039
Figure BDA00029221478500000310
is composed of
Figure BDA00029221478500000311
The kth element of (1).
In one embodiment, the step D includes:
step D1: order to
Figure BDA00029221478500000312
Structure of the device
Figure BDA00029221478500000313
Wherein,
Figure BDA0002922147850000041
Figure BDA0002922147850000042
Figure BDA0002922147850000043
represents the kronecker product;
Figure BDA0002922147850000044
Figure BDA0002922147850000045
step D2: to be provided with
Figure BDA0002922147850000046
Computing covariance matrix estimates for training samples
Figure BDA0002922147850000047
Figure BDA0002922147850000048
Wherein,
Figure BDA0002922147850000049
to represent
Figure BDA00029221478500000410
The conjugate transpose of (1);
step D3: computing the covariance matrix estimate
Figure BDA00029221478500000411
To obtain a signal subspace Us(ii) a The signal subspace UsTo be estimated from said covariance matrix
Figure BDA00029221478500000412
Obtained NtThe characteristic vector corresponding to the maximum characteristic value is formed; the signal subspace UsIs one MNp×NtOrder matrix, NpIs the number of pulses;
step D4: constructing a selection matrix;
step D5: based on the signal subspace UsAnd the selection matrix, calculate the distance-related eigenvalue decomposition and velocity-related eigenvalue decomposition;
step D6: a distance estimation result is computed based on the distance-related eigenvalue decomposition and a velocity estimation result is computed based on the velocity-related eigenvalue decomposition.
In one embodiment, step D4 includes:
constructing a selection matrix J, selecting the matrix JRAnd a selection matrix Jv；
Figure BDA00029221478500000413
Figure BDA00029221478500000414
Figure BDA00029221478500000415
Wherein,nI0＝[In,0n×1]；0Im＝[0m×1,Im]，0m×nrepresents an m × n zero matrix, ImDenotes an m × m identity matrix, InRepresenting an n x n identity matrix.
In one embodiment, step D5 includes:
step E5-1: computing
Figure BDA0002922147850000051
And
Figure BDA0002922147850000052
step E5-2: computing
Figure BDA0002922147850000053
And calculate
Figure BDA0002922147850000054
Step E5-3: computing
Figure BDA0002922147850000055
Eigenvalue decomposition G of-1ΛRG, decomposing as a distance-dependent characteristic value, and calculating
Figure BDA0002922147850000056
Eigenvalue decomposition G of-1ΛvG, decomposing as a speed-related characteristic value;
wherein,
Figure BDA0002922147850000057
the superscript H of (a) represents the matrix transposition conjugate; dRAnd DvAre all diagonal matrices;
Figure BDA0002922147850000058
Figure BDA0002922147850000059
g is composed of
Figure BDA00029221478500000510
Matrix of all eigenvectors, G-1Is the inverse matrix of G;
Figure BDA00029221478500000511
is a diagonal matrix, ΛRBy
Figure BDA00029221478500000512
All the characteristic values of (1);
Figure BDA00029221478500000513
is a diagonal matrix, ΛvIs equal to the corresponding gavvG-1A diagonal element.
In one embodiment, step D6 includes:
step E6-1: calculating a distance estimation result using a distance estimation formula based on the distance-dependent eigenvalue decomposition
Figure BDA00029221478500000514
Step E6-2: calculating a velocity estimation result using a velocity estimation formula based on the velocity-related eigenvalue decomposition
Figure BDA00029221478500000515
The distance estimation formula is as follows:
Figure BDA00029221478500000516
the velocity estimation formula is:
Figure BDA0002922147850000061
where angle () denotes the angle of a complex scalar,
Figure BDA0002922147850000062
is ΛRThe (c) th element of (a),
Figure BDA0002922147850000063
is ΛvThe ith element in (1).
In one embodiment, the length T ═ T of the effective OFDM symbols-TgSatisfy the requirement of
Figure BDA0002922147850000064
Wherein,
Figure BDA0002922147850000065
is the maximum detectable distance.
In one embodiment, the length T of the pulsepSatisfies the following conditions:
Figure BDA0002922147850000066
wherein,
Figure BDA0002922147850000067
is the maximum speed of the target.
According to the OFDM radar communication integrated distance and speed fast super-resolution estimation method, the interference of communication information on distance and speed estimation is eliminated through communication information compensation; through frequency smoothing, the correlation among the received signals is reduced; moreover, distance estimation and speed estimation are carried out on the target signal model subjected to frequency smoothing processing based on a super-resolution method, and a translation invariance structure of the signal model in a frequency domain and a pulse domain is utilized; thus, the present invention can improve the resolution of distance and velocity estimation without increasing the signal bandwidth and CPI. Moreover, the method has lower calculation complexity under the condition of equal estimation accuracy.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic flow chart of a method for fast super-resolution estimation of range and speed integrated with OFDM radar communication according to an embodiment of the present invention;
FIGS. 2(a) and 2(b) show waveforms of an OFDM radar communication integration signal utilized by embodiments of the present invention;
FIGS. 3(a), 3(b), 3(c) and 3(d) illustrate the distance and velocity estimation comparison of an embodiment of the present invention to a prior method;
FIGS. 4(a) and 4(b) show the results of a resolution comparison of the present invention with a prior method;
fig. 5(a) and 5(b) show the rms error versus signal-to-noise ratio for the distance and velocity estimates of the present invention versus the prior method.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
In order to estimate the distance and the speed by utilizing an OFDM radar communication integrated waveform, an embodiment of the present invention provides an OFDM radar communication integrated distance and speed fast super-resolution estimation method, which is shown in fig. 1 and includes the following steps:
step A: and constructing an OFDM radar communication integrated signal model, and inputting relevant parameters for the OFDM radar communication integrated signal model.
In the embodiment of the present invention, the used OFDM radar communication integrated waveform is shown in fig. 2(a), and the OFDM radar communication integrated waveform is composed of a plurality of complete OFDM symbols. As shown in fig. 2(b), each complete OFDM symbol is composed of an effective OFDM symbol and a Cyclic Prefix (CP), and the CP is a repetition of an end portion of the effective OFDM symbol. Each complete OFDM symbol may contain different communication information, with a length TsThe length of CP contained therein is TgThe length of the effective OFDM symbol is T, T is equal to
Figure BDA0002922147850000071
Δ f is the subcarrier spacing. As can be seen from FIG. 2(b), Ts＝T+Tg。
Firstly, from NsNpThe baseband of the OFDM radar communication integrated waveform composed of the OFDM symbols can be expressed as:
Figure BDA0002922147850000081
wherein,
Figure BDA0002922147850000082
introduced by CP, NsFor the number of complete OFDM symbols, NpIs the number of pulses; t isp＝NsTsIs represented by NsLength of one complete OFDM symbol, NcIs the number of subcarriers, rect [ T/T ]s]Is a rectangular window function, when T is more than or equal to 0 and less than TsIts value is equal to 1; for T is more than or equal to 0 and less than Ts，
Figure BDA0002922147850000083
The communication information modulated on the mth subcarrier, the nth OFDM symbol, and the pth pulse is represented. Wherein, thetam,n,pIs the m-th subcarrier, the n-th OFDM symbolThe sign and the information phase on the p-th pulse, j is the imaginary sign, e is the natural base number, and the rest of the parameters can be referred to above.
Further, based on the baseband expression, the finally transmitted narrowband OFDM radar communication integrated waveform may be represented as:
Figure BDA0002922147850000084
wherein f iscIs the carrier frequency, j is the imaginary sign, and the remaining parameters are as described above.
Assuming a sampling frequency fs＝NcΔ f, i.e. obtaining N-N from each valid OFDM symbolcTime samples of (a). After removing the CP, the CP is expressed by the equation Δ t 1/(N)cΔf)＝T/NcThen, the received signal of the p-th pulse, the n-th OFDM symbol and the k-th time sample can be represented as:
Figure BDA0002922147850000085
wherein N istIs the point target number; in an actual intelligent traffic system, the targets are vehicles, namely the number of point targets is the number of vehicles; the distance of the ith target is RiThe relative velocity of the i-th target is vi，i＝0,1,…,Nt1, c is the signal propagation speed, n (t) represents noise, ξiIs the attenuation factor due to propagation loss, scattering and radar cross-sectional area of the ith target;
Figure BDA0002922147850000091
substituting t in the formula by (k delta t + nT)s+pTp) The last term of the above formula (3) can be obtained
Figure BDA0002922147850000092
k is 0,1, …, N-1, and the rest of the parameters can be referred to above.
And (3) is the signal model of the OFDM radar communication integration constructed in the step a.
Then, the model is input with relevant parameters including Nt、Ns、Np、NcAnd M, etc. Where M is the number of subcarriers.
And B: and carrying out communication information compensation on the OFDM radar communication integrated signal model to obtain a communication information compensation model.
Specifically, the communication information compensation is performed on the OFDM radar communication integrated signal model shown in the above formula (3) to obtain a received signal vector
Figure BDA0002922147850000093
Obtaining a communication information compensation model; the expression of the communication information compensation model is as follows:
Figure BDA0002922147850000094
wherein,
Figure BDA0002922147850000095
Figure BDA0002922147850000096
Figure BDA0002922147850000097
is a Gaussian noise vector; n is the number of valid OFDM symbols in the complete OFDM symbol, p is the number of pulses,
Figure BDA0002922147850000098
the superscript T in the expression of (a) denotes the matrix transpose, and the remaining parameters can be referred to above.
And C: and carrying out frequency smoothing treatment on the communication information compensation model to obtain a target signal model of the distance and the speed to be estimated.
Here, the frequency smoothing process is performed on the communication information compensation model, and the obtained target signal model of the distance and the speed to be estimated may be represented as:
Figure BDA0002922147850000101
wherein,
Figure BDA0002922147850000102
Figure BDA0002922147850000103
Figure BDA0002922147850000104
is that
Figure BDA0002922147850000105
The k-th element of (a), the remaining parameters may be as described above.
Step D: and performing distance estimation and speed estimation on the target signal model based on a super-resolution method to obtain a distance estimation result and a speed estimation result with super-resolution.
Specifically, the step D may include a plurality of sub-steps as follows:
step D1: order to
Figure BDA0002922147850000106
Structure of the device
Figure BDA0002922147850000107
Wherein,
Figure BDA0002922147850000108
Figure BDA0002922147850000109
Figure BDA00029221478500001010
represents the kronecker product;
Figure BDA00029221478500001011
Figure BDA00029221478500001012
the remaining parameters can be referred to above.
Step D2: to be provided with
Figure BDA00029221478500001013
Computing covariance matrix estimates for training samples
Figure BDA00029221478500001014
Figure BDA00029221478500001015
Wherein,
Figure BDA00029221478500001016
to represent
Figure BDA00029221478500001017
The other parameters can be referred to above.
Step D3: computing covariance matrix estimates
Figure BDA00029221478500001018
To obtain a signal subspace Us(ii) a The signal subspace UsCorresponding covariance matrix estimates are collected
Figure BDA00029221478500001019
N of (A)tA feature vector of the largest feature value; the signal subspace UsIs one MNp×NtOrder matrix, NpIs the number of pulses.
Step D4: a selection matrix is constructed.
Specifically, a selection matrix J is constructed and constructedRAnd a selection matrix Jv；
Figure BDA0002922147850000111
Figure BDA0002922147850000112
Figure BDA0002922147850000113
Wherein,nI0＝[In,0n×1]；0Im＝[0m×1,Im]，0m×nrepresents an m × n zero matrix, ImDenotes an m × m identity matrix, InRepresenting an n x n identity matrix.
It will be appreciated that in selecting the expression of matrix J, the sign of the kronecker product is evaluated
Figure BDA0002922147850000114
Taking N from the left and right sides N respectivelyp-1 and M-1; in the selection matrix JRIn the expression of
Figure BDA0002922147850000115
Left N is Np-1, in
Figure BDA0002922147850000116
Taking M-1 as the right M; in the selection matrix JvIn the expression of
Figure BDA0002922147850000117
Left m is Np-1, in
Figure BDA0002922147850000118
The right M is M-1.
Step D5: based on signal subspace UsAnd selecting a matrix, calculating a distance-related eigenvalue decomposition and a velocity-related eigenvalue decomposition.
Specifically, the step D5 may include the following sub-steps:
step D5-1: computing
Figure BDA0002922147850000119
And
Figure BDA00029221478500001110
step D5-2: computing
Figure BDA00029221478500001111
And calculate
Figure BDA00029221478500001112
Step D5-3: computing
Figure BDA00029221478500001113
Eigenvalue decomposition G of-1ΛRG, decomposing as a distance-dependent characteristic value, and calculating
Figure BDA00029221478500001114
Eigenvalue decomposition G of-1ΛvG, as a velocity-related eigenvalue decomposition.
Wherein,
Figure BDA00029221478500001115
the superscript H of (1) represents matrix conjugate transpose; dRAnd DvAre all diagonal matrices;
Figure BDA00029221478500001116
Figure BDA00029221478500001117
g is composed of
Figure BDA00029221478500001118
Matrix of all eigenvectors, G-1Is the inverse matrix of G;
Figure BDA00029221478500001119
is a diagonal matrix, ΛRBy
Figure BDA00029221478500001120
All the characteristic values of (1);
Figure BDA0002922147850000121
is a diagonal matrix, ΛvIs equal to the corresponding gavvG-1A diagonal element.
Step D6: a distance estimation result is computed based on the distance-related eigenvalue decomposition, and a velocity estimation result is computed based on the velocity-related eigenvalue decomposition.
Specifically, the step D6 may include the following sub-steps:
e6-1: calculating a distance estimation result using a distance estimation formula based on the distance-dependent eigenvalue decomposition
Figure BDA00029221478500001211
E6-2: calculating a velocity estimation result using a velocity estimation formula based on the velocity-related eigenvalue decomposition
Figure BDA00029221478500001212
In practical application, the step E6-1 and the step E6-2 are not executed sequentially, or executed in parallel.
Wherein, the distance estimation formula used in step E6-1 is:
Figure BDA0002922147850000122
the velocity estimation formula used in step E6-2 is:
Figure BDA0002922147850000123
where angle () represents a complex scalar quantity
Figure BDA0002922147850000124
And scalar quantity
Figure BDA0002922147850000125
The angle of (a) is determined,
Figure BDA0002922147850000126
is ΛRThe (c) th element of (a),
Figure BDA0002922147850000127
is ΛvThe ith element in (1).
In addition, in the intelligent transportation system, in order to ensure that the distance estimation result is unambiguous, the length T of the effective OFDM symbol should satisfy
Figure BDA0002922147850000128
Wherein,
Figure BDA0002922147850000129
is the maximum detectable distance. While the length T of each pulse is such as to ensure that the estimated velocity result is unambiguouspIt should satisfy:
Figure BDA00029221478500001210
wherein,
Figure BDA0002922147850000131
is the maximum speed of the target, i.e. the maximum possible speed of the vehicle in the intelligent transportation system. It will be appreciated that the parameter N in this formulasAnd TgIs designed to meet the above maximum unambiguous distance and maximum unambiguous speed, or the parameter T and the parameter TpTogether satisfy the requirements of the formula.
According to the OFDM radar communication integrated distance and speed fast super-resolution estimation method provided by the embodiment of the invention, through communication information compensation, the interference of communication information on distance and speed estimation is eliminated; through frequency smoothing, the correlation among the received signals is reduced; moreover, distance estimation and speed estimation are carried out on the target signal model subjected to frequency smoothing processing based on a super-resolution method, and a translation invariance structure of the signal model in a frequency domain and a pulse domain is utilized; thus, the present invention can improve the resolution of distance and velocity estimation without increasing the signal bandwidth and CPI.
In addition, compared with the existing algorithm, the method and the device have lower calculation complexity under the condition of the same estimation precision. Specifically, in the embodiment of the present invention, the total computation complexity is about when expressed by using a large O-notation:
Figure BDA0002922147850000132
the total computational complexity of the existing R-V-MUSIC algorithm, when expressed using large O-notation, is approximately:
Figure BDA0002922147850000133
the total computational complexity of the existing R-V-Capon algorithm, when expressed using large O notation, is about:
Figure BDA0002922147850000134
wherein N isvIs the number of divided velocity grids in the R-V-MUSIC algorithm, NRIs the number of distance grids divided in the R-V-MUSIC algorithm. Due to NvNRTypically compared to MNpTherefore, compared with the existing R-V-MUSIC algorithm and R-V-Capon algorithm, the OFDM radar communication integrated range-speed fast super-resolution estimation method provided by the embodiment of the invention has much lower computational complexity.
In order to verify the effectiveness of the method for quickly estimating the range and speed of the OFDM radar communication integration provided by the embodiment of the invention, the beneficial effects of the embodiment of the invention are explained by adopting a simulation verification and a CRB (Cram é -Rao, Cramer-Rao bound) calculation mode.
First, CRB of the distance estimation result and the velocity estimation result in the embodiment of the present invention is calculated. Specifically, the CRB of the velocity estimation result v may be expressed as:
Figure BDA0002922147850000141
and the CRB of the distance estimation result may be obtained by calculating the CRB of the time delay τ, which may be expressed as:
Figure BDA0002922147850000142
in the above two calculation formulas of CRB:
Figure BDA0002922147850000143
Figure BDA0002922147850000144
Figure BDA0002922147850000145
Figure BDA0002922147850000146
in addition, N0Is the power spectral density of the noise, where λ represents the carrier wavelength and Re {. cndot.) represents the real part of the complex scalar.
The above Jττ、Jτv、JvτAnd JvvIn the expression of (a):
Figure BDA0002922147850000151
Figure BDA0002922147850000152
Figure BDA0002922147850000153
wherein,
Figure BDA0002922147850000154
is the Fourier transform of (t- τ) s (t, τ), S (jw) is
Figure BDA0002922147850000155
Fourier transform of (1)*Represents the conjugate operator, |, represents the absolute value of the scalar,
Figure BDA0002922147850000156
and D, representing the OFDM radar communication integrated waveform of the baseband mentioned in the step A at the time delay of tau.
Using the CRB of the time delay τ, the CRB of the resulting distance estimation structure is:
Figure BDA0002922147850000157
then, the beneficial effects of the present invention are further explained by the simulation verification results. Wherein, the simulation environment is MATLAB R2016 a. The simulation parameters comprise: the noise adopts complex Gaussian noise with the mean value of zero, the length of an effective OFDM symbol is T-10, and the length of a CP is Tg4 mus, subcarrier spacing Δ f 0.1MHz, carrier frequency fcThe modulation scheme is BPSK (Binary Phase Shift Keying) at 1GHz, and is used for transmitting communication information.
FIG. 3(a), FIG. 3(b), FIG. 3(c) and FIG. 3(d) are the prior R-V-MUSIC algorithm of the present invention, respectivelyDistance estimation and velocity estimation of existing R-V-Capon algorithms and existing R-V-FFT algorithms. Wherein the number of subcarriers is Nc128, the number of complete OFDM symbols is Ns64, pulse number N p15. A total of 4 targets are set: target 1, target 2, target 3 and target 4; their respective distance and speed parameters are (80m, 30m/s), (90m, 30m/s), (30m, -7m/s), (30m, 0m/s), respectively. The Signal-to-Noise Ratio (SNR) is 0 dB. By comparison, the invention can obtain super-resolution distance and speed estimation equivalent to R-V-MUSIC and R-V-Capon algorithms. And the result of the R-V-FFT algorithm has only two peak values, and the four targets can not be effectively distinguished.
Fig. 4(a) and 4(b) are resolution comparison results of the present invention and the prior art method. FIG. 4(a) shows the distance estimation result of 30m/s, and the distance estimation of the targets 1 and 2 can be obtained with a small estimation error by the R-V-MUSIC and R-V-Capon algorithm. But the R-V-FFT algorithm can only obtain a range estimate with a higher estimation error for one target. Fig. 4(b) shows a velocity image of 30m, the present invention can directly obtain a velocity estimate. The R-V-MUSIC and R-V-Capon algorithms obtain two peak values at corresponding target speeds. Whereas the R-V-FFT algorithm can only get one peak, a longer CPI is needed to distinguish between targets 3 and 4 using the R-V-FFT algorithm.
Based on the simulation results, it can be understood that: to achieve super-resolution range and velocity estimation, the invention does not need to search the whole range and velocity space, which is needed by the R-V-MUSIC and R-V-Capon algorithms. Therefore, the R-V-MUSIC and R-V-Capon algorithms have higher computational complexity. Furthermore, to achieve the same range and velocity resolution, the R-V-FFT algorithm requires a larger signal bandwidth and a longer CPI, which is not required by the present invention.
Fig. 5(a) and 5(b) are plots of Root Mean Square Error (RMSEs) versus SNR for the distance and velocity estimates of the present invention versus the prior art method. Wherein each SNR is subjected to 200 independent Monte Carlo experiments, and in each Monte Carlo experiment, the number of subcarriers is Nc12, the number of OFDM symbols is Ns200 and number of pulses Np＝6。
In fig. 5(a), the RMSEs of the distance estimation are shown as a function of SNR. It can be seen that as the SNR increases, the RMSEs of the range estimates and the root CRB on the range estimates decrease. Fig. 5(b) shows the dependence of RMSEs of the velocity estimates on SNR. It can be seen that the RMSEs of the velocity estimates and the root CRB of the velocity estimates decrease as the SNR increases. Compared with the R-V-MUSIC algorithm and the R-V-Capon algorithm, the method has lower calculation complexity and can realize quick estimation compared with the existing algorithm under the same estimation precision.
In the description of the specification, reference to the description of the term "one embodiment", "some embodiments", "an example", "a specific example", or "some examples", etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by those skilled in the art.
While the present application has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the drawings, the disclosure, and the appended claims.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (9)

1. An OFDM radar communication integrated range speed fast super-resolution estimation method is characterized by comprising the following steps:
step A: constructing an OFDM radar communication integrated signal model, and inputting relevant parameters for the OFDM radar communication integrated signal model;
and B: carrying out communication information compensation on the OFDM radar communication integrated signal model to obtain a communication information compensation model;
and C: carrying out frequency smoothing processing on the communication information compensation model to obtain a target signal model of the distance and the speed to be estimated;
step D: and performing distance estimation and speed estimation on the target signal model based on a super-resolution method to obtain a distance estimation result and a speed estimation result with super-resolution.
2. The method of claim 1, wherein the communication information compensation model is expressed as:
Figure FDA0002922147840000011
wherein N istTo point target number, xiiIs the attenuation factor due to the propagation loss, scattering and radar cross-sectional area of the ith target, i is 0,1, …, Nt-1；
Figure FDA0002922147840000012
Figure FDA0002922147840000013
Figure FDA0002922147840000014
Figure FDA0002922147840000015
Is a Gaussian noise vector; j is the imaginary symbol, e is the natural base number, Δ f is the subcarrier spacing, fcIs the carrier frequency, RiIs the distance of the ith target, viIs the relative velocity of the ith target, c is the signalA propagation speed; t issFor the length of a complete OFDM symbol, TgIs the length of the cyclic prefix CP in the complete OFDM symbol, Tp＝NsTs，NsFor the number of complete OFDM symbols, NcIs the number of subcarriers; n is the number of valid OFDM symbols in the complete OFDM symbol, p is the number of pulses,
Figure FDA0002922147840000021
the superscript T in the expression of (a) denotes a matrix transposition.
3. The method of claim 2, wherein the target signal model collects column vectors of k to (k + M-1) th received signals;
the expression of the target signal model is as follows;
Figure FDA0002922147840000022
wherein,
Figure FDA0002922147840000023
m is the number of subcarriers;
Figure FDA0002922147840000024
Figure FDA0002922147840000025
is composed of
Figure FDA0002922147840000026
The kth element of (1).
4. The method of claim 3, wherein step D comprises:
step D1: order to
Figure FDA0002922147840000027
Structure of the device
Figure FDA0002922147840000028
Wherein,
Figure FDA0002922147840000029
Figure FDA00029221478400000210
Figure FDA00029221478400000211
represents the kronecker product;
Figure FDA00029221478400000212
Figure FDA00029221478400000213
step D2: to be provided with
Figure FDA00029221478400000214
Computing covariance matrix estimates for training samples
Figure FDA00029221478400000215
Figure FDA00029221478400000216
Wherein,
Figure FDA00029221478400000217
to represent
Figure FDA00029221478400000218
The conjugate transpose of (1);
step D3: computing the covariance matrix estimate
Figure FDA00029221478400000219
To obtain a signal subspace Us(ii) a The signal subspace UsTo be estimated from said covariance matrix
Figure FDA00029221478400000220
Obtained NtThe characteristic vector corresponding to the maximum characteristic value is formed; the signal subspace UsIs one MNp×NtOrder matrix, NpIs the number of pulses;
step D4: constructing a selection matrix;
step D5: based on the signal subspace UsAnd the selection matrix, calculate the distance-related eigenvalue decomposition and velocity-related eigenvalue decomposition;
step D6: a distance estimation result is computed based on the distance-related eigenvalue decomposition and a velocity estimation result is computed based on the velocity-related eigenvalue decomposition.
5. The method of claim 4, wherein step D4 includes:
constructing a selection matrix J, selecting the matrix JRAnd a selection matrix Jv；
Figure FDA0002922147840000031
Figure FDA0002922147840000032
Figure FDA0002922147840000033
Wherein,nI0＝[In,0n×1]；0Im＝[0m×1,Im]，0m×nrepresents an m × n zeroMatrix, ImDenotes an m × m identity matrix, InRepresenting an n x n identity matrix.
6. The method of claim 5, wherein step D5 comprises:
step E5-1: computing
Figure FDA0002922147840000034
And
Figure FDA0002922147840000035
step E5-2: computing
Figure FDA0002922147840000036
And calculate
Figure FDA0002922147840000037
Step E5-3: computing
Figure FDA0002922147840000038
Eigenvalue decomposition G of-1ΛRG, decomposing as a distance-dependent characteristic value, and calculating
Figure FDA0002922147840000039
Eigenvalue decomposition G of-1ΛvG, decomposing as a speed-related characteristic value;
wherein,
Figure FDA00029221478400000310
the superscript H of (a) represents the matrix transposition conjugate; dRAnd DvAre all diagonal matrices;
Figure FDA00029221478400000311
Figure FDA00029221478400000312
g is composed of
Figure FDA00029221478400000313
Matrix of all eigenvectors, G-1Is the inverse matrix of G;
Figure FDA0002922147840000041
is a diagonal matrix, ΛRBy
Figure FDA0002922147840000042
All the characteristic values of (1);
Figure FDA0002922147840000043
is a diagonal matrix, ΛvIs equal to the corresponding gavvG-1A diagonal element.
7. The method of claim 6, wherein step D6 comprises:
step E6-1: calculating a distance estimation result using a distance estimation formula based on the distance-dependent eigenvalue decomposition
Figure FDA0002922147840000044
Step E6-2: calculating a velocity estimation result using a velocity estimation formula based on the velocity-related eigenvalue decomposition
Figure FDA0002922147840000045
The distance estimation formula is as follows:
Figure FDA0002922147840000046
the velocity estimation formula is:
Figure FDA0002922147840000047
where angle () denotes the angle of a complex scalar,
Figure FDA0002922147840000048
is ΛRThe (c) th element of (a),
Figure FDA0002922147840000049
is ΛvThe ith element in (1).
8. The method of claim 7, wherein the length T ═ T of the effective OFDM symbols-TgSatisfy the requirement of
Figure FDA00029221478400000410
Wherein,
Figure FDA00029221478400000411
is the maximum detectable distance.
9. Method according to claim 8, characterized in that the length T of the pulsepSatisfies the following conditions:
Figure FDA00029221478400000412
wherein,
Figure FDA00029221478400000413
is the maximum speed of the target.
CN202110121380.4A 2021-01-28 2021-01-28 OFDM radar communication integrated distance speed rapid super-resolution estimation method Active CN112986924B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110121380.4A CN112986924B (en) 2021-01-28 2021-01-28 OFDM radar communication integrated distance speed rapid super-resolution estimation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110121380.4A CN112986924B (en) 2021-01-28 2021-01-28 OFDM radar communication integrated distance speed rapid super-resolution estimation method

Publications (2)

Publication Number Publication Date
CN112986924A true CN112986924A (en) 2021-06-18
CN112986924B CN112986924B (en) 2023-12-26

Family

ID=76345723

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110121380.4A Active CN112986924B (en) 2021-01-28 2021-01-28 OFDM radar communication integrated distance speed rapid super-resolution estimation method

Country Status (1)

Country Link
CN (1) CN112986924B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113805170A (en) * 2021-09-02 2021-12-17 江苏科技大学 OFDM radar communication integrated high-speed target distance and speed estimation method
CN114422303A (en) * 2021-12-29 2022-04-29 哈尔滨工程大学 Range-speed joint rapid estimation method for radar communication shared signals
CN115499043A (en) * 2022-08-17 2022-12-20 西安电子科技大学 Parameter estimation method based on MIMO-OFDM radar communication sharing signal
CN117607849A (en) * 2023-11-13 2024-02-27 中山大学 Multi-target complex scene-oriented distance and speed combined high-precision sensing method and system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110158361A1 (en) * 2009-12-30 2011-06-30 Dent Paul W Radio channel analyzer to determine doppler shifts across multiple frequencies of a wideband signal
JP2012042372A (en) * 2010-08-20 2012-03-01 Mitsubishi Electric Corp Radar device
CN104678372A (en) * 2015-03-14 2015-06-03 西安电子科技大学 Joint estimation method for super-resolution distance value and angle value by using orthogonal frequency division multiplexing radar
CN105137410A (en) * 2015-07-24 2015-12-09 西安电子科技大学 OFDM-based high-resolution radar communication integration waveform optimization method
CN106249231A (en) * 2016-07-12 2016-12-21 电子科技大学 A kind of radar-communication integration system based on CE OFDM
CN106353749A (en) * 2016-08-31 2017-01-25 电子科技大学 Super-resolution TDCSRAD (Transform Domain Communication System Radar) and communication integrated design method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110158361A1 (en) * 2009-12-30 2011-06-30 Dent Paul W Radio channel analyzer to determine doppler shifts across multiple frequencies of a wideband signal
JP2012042372A (en) * 2010-08-20 2012-03-01 Mitsubishi Electric Corp Radar device
CN104678372A (en) * 2015-03-14 2015-06-03 西安电子科技大学 Joint estimation method for super-resolution distance value and angle value by using orthogonal frequency division multiplexing radar
CN105137410A (en) * 2015-07-24 2015-12-09 西安电子科技大学 OFDM-based high-resolution radar communication integration waveform optimization method
CN106249231A (en) * 2016-07-12 2016-12-21 电子科技大学 A kind of radar-communication integration system based on CE OFDM
CN106353749A (en) * 2016-08-31 2017-01-25 电子科技大学 Super-resolution TDCSRAD (Transform Domain Communication System Radar) and communication integrated design method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘永军;廖桂生;杨志伟;许京伟;: "一种超分辨OFDM雷达通信一体化设计方法", 电子与信息学报, no. 02 *
李明兵;谷亚彬;张林让;王正海;: "一种OFDM雷达通信一体化的距离超分辨方法", 电讯技术, no. 06 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113805170A (en) * 2021-09-02 2021-12-17 江苏科技大学 OFDM radar communication integrated high-speed target distance and speed estimation method
CN113805170B (en) * 2021-09-02 2023-08-22 江苏科技大学 OFDM radar communication integrated high-speed target distance and speed estimation method
CN114422303A (en) * 2021-12-29 2022-04-29 哈尔滨工程大学 Range-speed joint rapid estimation method for radar communication shared signals
CN114422303B (en) * 2021-12-29 2023-08-15 哈尔滨工程大学 Distance-speed joint fast estimation method for radar communication shared signal
CN115499043A (en) * 2022-08-17 2022-12-20 西安电子科技大学 Parameter estimation method based on MIMO-OFDM radar communication sharing signal
CN117607849A (en) * 2023-11-13 2024-02-27 中山大学 Multi-target complex scene-oriented distance and speed combined high-precision sensing method and system

Also Published As

Publication number Publication date
CN112986924B (en) 2023-12-26

Similar Documents

Publication Publication Date Title
CN112986924A (en) OFDM radar communication integrated range and speed fast super-resolution estimation method
CN106546965B (en) Space-time adaptive processing method based on radar amplitude and Doppler-frequency estimation
Feng et al. A reduced-dimension MUSIC algorithm for monostatic FDA-MIMO radar
CN104678372B (en) OFDM radar super-resolution distance and angle value combined estimation method
CN105137410B (en) The waveform optimization method of high-resolution radar communicating integral based on OFDM
CN103744061A (en) Iterative least square method-based MIMO (multiple input multiple output) radar DOA (direction-of-arrival) estimation method
CN106483516A (en) Radar clutter space-time adaptive processing method based on priori
CN103210603A (en) Estimation of a multipath signal in a wireless communication system
CN113376607B (en) Airborne distributed radar small sample space-time self-adaptive processing method
CN103713276A (en) Arrival direction estimation method based on minimum mutual entropy spectrum analysis
CN111830482A (en) FDA radar target positioning method based on agile OFDM
CN109521411A (en) A kind of detection method of range extension target
Wang et al. Joint range and velocity estimation for integration of radar and communication based on multi‐symbol OFDM radar pulses
CN114895251A (en) Multivariable optimization-based OFDM radar communication integrated signal design method
CN108896963B (en) Airborne radar space-time self-adaptive dimension reduction processing method
Wang et al. Parafac-direct: A joint parameters estimation method for slow-time mimo
Delamou et al. Deep learning-based estimation for multitarget radar detection
Wang et al. Joint range and velocity estimation for orthogonal frequency division multiplexing‐based high‐speed integrated radar and communications system
Bhattacharjee et al. Multi-carrier wideband OCDM-based THz automotive radar
CN112014807A (en) Self-adaptive clutter suppression method for frequency agile radar
KR102076096B1 (en) Apparatus and method for processing radar signal
Zhong et al. Performance optimization and parameters estimation for MIMO-OFDM dual-functional communication-radar systems
CN113805170B (en) OFDM radar communication integrated high-speed target distance and speed estimation method
Bhogavalli et al. Estimating the target DOA, range and velocity using subspace methods in a MIMO OFDM DFRC system
Shang et al. A target parameter estimation algorithm for integration of radar and communication based on orthogonal time frequency space

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