CN113219461A - Millimeter wave radar sparse array design method based on maximized signal-to-noise ratio - Google Patents

Millimeter wave radar sparse array design method based on maximized signal-to-noise ratio Download PDF

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CN113219461A
CN113219461A CN202110442489.8A CN202110442489A CN113219461A CN 113219461 A CN113219461 A CN 113219461A CN 202110442489 A CN202110442489 A CN 202110442489A CN 113219461 A CN113219461 A CN 113219461A
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millimeter wave
wave radar
matrix
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王洪雁
薛喜扬
周贺
袁海
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Zhejiang University of Technology ZJUT
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/93Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50Systems of measurement based on relative movement of target
    • G01S13/52Discriminating between fixed and moving objects or between objects moving at different speeds
    • G01S13/536Discriminating between fixed and moving objects or between objects moving at different speeds using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415Identification of targets based on measurements of movement associated with the target

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Abstract

The invention discloses a millimeter wave radar sparse array design method based on a maximized signal-to-noise ratio, and relates to the field of target detection of an automatic driving millimeter wave radar; constructing a maximized SINR problem based on an FMCW millimeter wave radar array detection model; under the condition of definite autocorrelation of spatial lag data, designing a sparse array by a successive linear approximation algorithm; under the condition of uncertain autocorrelation of spatial lag data, interpolating autocorrelation values corresponding to missing lag by adopting a low-rank matrix completion method based on semi-definite Toeplitz constraint, and solving the sparse array design problem by a convex optimization method. The method provided by the invention can improve the target detection performance under the resolution constraint and simultaneously meet the requirements of distance and speed resolution, and has good convergence. Furthermore, the beam pattern has lower sidelobes because the proposed method optimizes the reception weights to concentrate the power in the direction of the target while suppressing echoes in other directions.

Description

Millimeter wave radar sparse array design method based on maximized signal-to-noise ratio
Technical Field
The invention relates to the field of target detection of an automatic driving millimeter wave radar, in particular to a millimeter wave radar sparse array design method based on a maximized signal-to-noise ratio.
Background
In recent years, with rapid iteration in the automobile industry, millimeter wave radar gradually becomes an indispensable sensor for automatic driving due to its advantages of low cost, high precision, good stability and the like. The millimeter wave radar transmits a designable signal to a free space through a transmitter, receives echoes of a target and other objects through a receiver, and processes the obtained echoes based on a related signal processing method to perceive environment information. Therefore, the system airspace degree of freedom can influence the perception accuracy of the environment information, and the parameter resolution, the measurement accuracy and the clutter suppression performance can be improved by increasing the system airspace degree of freedom, so that the system target detection and estimation capability is improved, and the unmanned environment perception capability is enhanced. However, due to the limited size of the automobile platform, the number of array elements cannot be increased without limit to improve the spatial degree of freedom, and for this problem, researchers select the transceiving array elements to improve the spatial degree of freedom and reduce the system overhead, that is, the design of the sparse array is adopted.
In the case of no interference, Wang et al propose a sparse array design method based on convex relaxation and Iterative Linear Fractional Programming (ILFP), which solves the problem of non-convex antenna selection of a sparse array beamformer. In the case of interference, Hamza et al propose a sparse array design method for receive beamforming with maximum signal-to-interference-and-noise ratios for single-point sources and multi-point sources. Furthermore, Zheng et al propose a sparse array design method for adaptive beamforming that obtains an optimal array configuration based on a maximum signal to interference noise ratio (SINR) criterion, which, however, ignores the angular spreading effect of radio propagation. Aiming at the problem, Hamza et al propose an optimal sparse array design method with local scattering, and solve the influence of angular diffusion effect on detection performance. In order to reduce the influence brought by high side lobe level, Jarske et al propose an array refinement design method with minimized side lobe, i.e. based on completely filling the array, systematically and sequentially removing the sensors. In addition, Leahy et al propose a sparse array design method for optimizing peak sidelobe levels, which relates to the joint design of sensor positions and corresponding beam forming weights thereof. For global optimization tools to solve sparse array beamforming, industries such as Genetic Algorithm (GA) algorithm and convex relaxation method have been widely used for sensor selection problem. The array configuration and weights are adapted to the time-varying perceptual environment, which can be achieved by adjusting the antenna positions and the corresponding weights. In summary, the sparse array design problem can be expressed by maximizing the SINR model, however, maximizing SINR over all possible sparse topologies is a combinatorial optimization problem, which is typically a challenging polynomial solution problem. Furthermore, sparse array optimization design requires estimation of all spatial lag data autocorrelation across the array aperture, whereas existing approaches typically assume that the full correlation matrix is known to design a sparse array.
Disclosure of Invention
Aiming at the problem that the degree of freedom of a millimeter wave radar system is low due to the fact that a limited platform space is automatically driven, and therefore target detection performance is poor, the invention provides a millimeter wave radar sparse array design method based on a maximized signal-to-noise ratio, which comprises the step of constructing a maximized SINR problem based on an FMCW millimeter wave radar array detection model; under the condition that the autocorrelation of the spatial lag data is known, designing a sparse array by a successive linear approximation (SCA) algorithm; under the condition of uncertain autocorrelation of spatial lag data, interpolating autocorrelation values corresponding to missing lag by adopting a low-rank matrix completion method based on semi-definite Toeplitz constraint, and solving the sparse array design problem by a convex optimization method.
Due to the adoption of the technical scheme, the invention can obtain the following technical effects: the method provided by the invention can improve the target detection performance under the resolution constraint and simultaneously meet the requirements of distance and speed resolution, and has good convergence. Furthermore, the beam pattern has lower sidelobes because the proposed method optimizes the reception weights to concentrate the power in the direction of the target while suppressing echoes in other directions. And establishing a transmitting waveform parameter and receiving weight value combined optimization model with distance and speed resolution constraint, thereby realizing improvement of target detection and distance and speed resolution performance of the millimeter wave radar.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be 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 described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a diagram of a sparse array element selection structure under a single interference condition;
FIG. 3 is a graph of average SINR output from various sparse arrays as a function of the angle of incidence of the desired signal under single interference conditions;
FIG. 4 is a diagram of a sparse array element selection structure under a multi-interference condition;
fig. 5 is a graph of average SINR output from various sparse arrays as a function of angle of incidence of a desired signal under multiple interference conditions.
Detailed Description
The implementation steps of the present invention are further described in detail below with reference to fig. 1: the invention provides a method for improving the target detection probability of a millimeter wave radar, namely a millimeter wave radar sparse array design method based on a maximized signal-to-noise ratio, which specifically comprises the following steps:
step 1, constructing a maximized SINR problem based on an FMCW millimeter wave radar array detection model;
because the FMCW radar has the characteristics of low cost, high resolution, high integration level and the like, the autopilot millimeter wave radar often transmits FMCW with a constant amplitude characteristic, and the FMCW signal can be expressed as:
s(t)=exp(j2πf0t+jπμt2)
wherein f is0For the initial frequency, μ ═ B/T is the modulation frequency, and B and T are the signal bandwidth and sweep period, respectively.
The millimeter wave radar receiving array is composed of N uniformly spaced and isotropic array elements, and then a signal x (t) received by the millimeter wave radar array element at time t can be represented as:
Figure BDA0003035525780000031
wherein K is the number of interference sources,
Figure BDA0003035525780000032
is theta0The direction target is directed to the vector of the vector,
Figure BDA0003035525780000033
is thetakAnd d and lambda are the interval between adjacent array elements and the wavelength of a carrier wave respectively, and d is usually less than or equal to lambda/2. v (t) is the receive array noise, which can be modeled as following a Gaussian distribution with a mean of 0 and a covariance of σv 2。siAnd (t) is an interference source signal. At a sampling rate fsThe digital beamforming input signals obtained by sampling are:
Figure BDA0003035525780000034
after weighting by the beam weighting coefficients, the resulting beam forming output signal is:
y(n)=wHx(n)
where w is a receive weight vector.
Based on the above, the available SINR expression is as follows:
Figure BDA0003035525780000035
wherein R iss=σ2a(θ0)aH0) Is a target signal covariance matrix, σ2=E{s(n)sH(n) is the target signal power.
Figure BDA0003035525780000036
As a covariance matrix of interference and uncorrelated noise,
Figure BDA0003035525780000037
is the power of the kth interferer. Design weight w to maximize optimization of SINR based on minimum distortion (MVDR) criterionThe problem can be expressed as:
Figure BDA0003035525780000041
s.t.wHRsw=1
because noise and interference can not be separated from array elements, R is adopted in practical applicationx=Rs+RiIn place of RiThen the above formula can be rewritten as:
Figure BDA0003035525780000042
s.t.wHRsw=1
solving the above problem requires only receiving the data correlation matrix Rx=E(xxH) And the desired DOA. Based on
Figure BDA0003035525780000043
The former can be more easily estimated from the received L snapshots x.
Solving the above equation yields:
Figure BDA0003035525780000044
thus, the optimal output SINR may be expressed as:
Figure BDA0003035525780000045
wherein, Λmax{. denotes the maximum eigenvalue. The above applies to any array topology.
Step 2, under the self-correlation definite condition of the spatial lag data, designing a sparse array through a successive linear approximation algorithm;
under the condition that the full correlation matrix is certain, in order to obtain a sparse solution, an additional sparse constraint can be introduced into the maximum SINR problem model, namely:
Figure BDA0003035525780000046
s.t.wHRsw≥1
||w||0=P
wherein | · | purple sweet0The number of the constraint weight vectors w is represented as the selected sensor number P. The above formula is obviously a non-convex problem and is not easy to solve based on the traditional optimization method. To solve this problem, the objective function in the above equation can be interchanged with a quadratic constraint, i.e.:
Figure BDA0003035525780000047
s.t.wHRxw≤1
||w||0=P
in general, the receiving weights are complex numbers, and the quadratic function is real number, so the above problem cannot be solved directly. Since the real and imaginary complex terms of the optimal weight vector are usually decoupled, the above problem can be solved by defining the receiving weight real vector and the corresponding correlation matrix, that is:
Figure BDA0003035525780000051
s.t.
Figure BDA0003035525780000052
||w||0=P
wherein,
Figure BDA0003035525780000053
and
Figure BDA0003035525780000054
are each Rs、RiAnd w is expressed in real numbers.
To solve the above problem efficiently, the problem can be equivalent to the following successive linear approximation:
Figure BDA0003035525780000055
s.t.
Figure BDA0003035525780000056
||w||0=P
wherein,
Figure BDA0003035525780000057
i denotes the ith iteration. Finally, by minimizing the mixing l1-∞Norm relaxation non-convexoNorm to obtain sparse solution with high efficiency:
Figure BDA0003035525780000058
s.t.
Figure BDA0003035525780000059
wherein the vector
Figure BDA00030355257800000510
Comprises a real part and an imaginary part of a beam forming weight corresponding to the first sensor, | · caly |, aSelecting
Figure BDA00030355257800000511
Is measured. For the initial iteration, the regularization parameter μmay be set to 0 to allow the optimization problem described above to converge quickly. It should be noted that the parameter μ itself does not guarantee that the final solution is sparse. Therefore, to ensure sparsity of the final solution, μ optimal needs to be obtained by binary search within the possible upper and lower limits to ensure that the optimization problem can converge to P sparsity.
And 3, under the condition that the autocorrelation of the spatial lag data is not known, interpolating autocorrelation values corresponding to the missing lags by adopting a low-rank matrix completion method under the semi-positive Toeplitz constraint on the basis of obtaining the autocorrelation of all the spatial lag data of the array aperture, and solving the design problem of the sparse array by adopting a convex optimization method.
The sparse array design based on the SCA algorithm assumes that the full correlation matrix is known, but the full correlation matrix is very difficult to obtain in an actual scene, and a large number of missing correlation lags may occur. Aiming at the problem, the invention provides a semi-positive Toepltiz matrix completion method to effectively utilize an unknown correlation matrix structure so as to improve the detection performance of a designed array. The proposed matrix completion method can be expressed as:
Figure BDA00030355257800000512
s.t.Toeplitz(l)0
wherein Toeplitz (l) returns a symmetric Toeplitz matrix, where l and lHThe first row and the first column of Toeplitz (l) are defined, respectively. Matrix RPFor a received data correlation matrix with a missing correlation lag, the corresponding element of the missing correlation lag is zeroed out. The symbol ≧ represents the Hadamard product, ≧ represents the matrix inequality with the semi-positive definite constraint. The matrix Z is a binary matrix and,
Figure BDA0003035525780000061
the square of the Frobenius norm of the matrix is expressed, where the norm is intended to minimize the sum of the squared errors between the observed correlation values and the corresponding terms of the unknown Toeplitz matrix. The regularization parameter ζ weighs the error term and the tracking heuristic, whose nominal values are typically adjusted based on numerical experience of the problem. The problem is a convex problem, and thus an efficient solution can be obtained based on a multitude of convex optimization toolkits.
Based on the above, the invention provides a millimeter wave radar sparse array design method based on a maximized signal-to-noise ratio, aiming at the problem that the millimeter wave radar has low target detection performance due to the fact that the space freedom of the millimeter wave radar is small due to the limitation of the space of the automatic driving platform. The method comprises the steps of firstly, constructing a problem of maximizing SINR (signal to interference and noise ratio) optimized weight based on an FMCW millimeter wave radar array detection model; secondly, optimizing a weight value based on an SCA algorithm to further realize sparse array design; and finally, in order to obtain the data autocorrelation of all the spatial lags on the array aperture, interpolating autocorrelation values corresponding to the missing lags by adopting a low-rank matrix completion method under the semi-positive Toeplitz constraint. Simulation results show that compared with a sparse array obtained based on an SCA algorithm and an enumeration method, the method can obviously improve the target detection performance of the millimeter wave array radar.
The effects of the present invention can be further illustrated by the following simulations:
the method is compared with the optimal sparse array obtained based on the SCA algorithm and the optimal sparse array obtained by an enumeration method, and the detection performance under the conditions of single interference and multiple interferences is analyzed successively to verify the effectiveness of the algorithm. The simulation environment is as follows: CPU Intel (R) core (TM) i7-7700, RAM 8GB, MATLAB R2016 a. The simulation conditions are as follows: considering that the given receiving sensor number N is 10, the array element spacing d is λ/2, the sensor number P can be selected to be 6, the signal-to-noise ratio SNR is 0dB, the dry-to-noise ratio INR is 20dB, the initialization epsilon is 0.05, and the binary search method range of the sparsity parameter μ is set to be 0.01 to 5.
Assuming that a target signal is in a 10-degree direction and an interference signal is in a-40-degree direction, fig. 2 is a structure diagram for selecting a sparse array element under a single interference condition. Fig. 2(a) shows an initial 6-element sparse array configuration with missing correlation lag and occupying only a small portion of the aperture, with the estimated data correlation matrix randomly selected from 10 array element positions. Fig. 2(b) shows the sparse array configuration designed based on the SCA algorithm, and the resulting optimal sparse array output SINR is 11.23 dB. While fig. 2(c) shows the best SINR array obtained by the enumeration method, the SINR of the array is 11.65dB, it is noted that the possible array configuration is orders of magnitude larger, which may cause the problem to be unable to be solved by enumeration search. And randomly selecting 200 snapshot data, and restoring the whole array Toeplitz estimation through matrix completion of a regularization parameter zeta of 0.5. Fig. 2(d) shows the optimal sparse array configuration achieved by matrix completion, with an SINR of 11.50dB, and better performance is obtained by matrix completion configuration than by SCA algorithm-based optimal configuration.
Assume that the target signal is in the direction of θ [ -30:10:30] ° and that there is a simultaneous disturbance signal position in the direction of-40 °. Solving the SINR obtained by the optimal sparse array in the graph of FIG. 2, and FIG. 3 is a graph of the variation of the average SINR output by various sparse arrays along with the incident angle of the expected signal, as can be seen from FIG. 3, the SINR output by the proposed algorithm is only second to the optimal SINR obtained by the enumeration method, the average error is about 0.2dB, and the maximum error is not more than 0.4dB, compared with the SINR output based on the SCA algorithm, the SINR output by the proposed algorithm is obviously higher than the SINR output based on the SCA algorithm, because the structure of the unknown correlation matrix is effectively utilized by the matrix completion. The detection performance can be obviously improved by the algorithm.
Assume that the target signal is in the 10 ° direction and that there are three interfering signals in the-40 °, -20 °, and 50 ° directions simultaneously. Fig. 4 is a structure diagram of sparse array element selection under the condition of multiple interferences. Fig. 4(a) is an initial 6 array element sparse array configuration in which an estimated data correlation matrix is randomly selected from 10 array element positions, fig. 4(b) is a sparse array configuration designed based on an SCA algorithm, and the SINR of the calculated optimal sparse array output is 10.72 dB. Meanwhile, fig. 4(c) shows the optimal SINR array solved by the enumeration method, and the SINR of the optimal SINR array is 11.35 dB. Randomly selecting 200 snapshot data, recovering full-array Toeplitz estimation through matrix completion with regularization parameter ζ being 0.5, and fig. 4(d) shows that the optimal sparse array configuration is realized through the matrix completion, the SINR is 11.11dB, and the matrix completion configuration has higher output SINR, which indicates that the matrix completion algorithm is still effective for improving the SINR under the multi-interference condition.
Assume that the target signal is in the direction of-30: 10:30, while there are three interfering signals in the-40, -20, and 50 directions. Solving the SINR obtained by the optimal sparse array in fig. 4, fig. 5 is a graph of average SINR performance of various sparse arrays relative to the incident angle of the desired signal, and as can be seen from fig. 5, the SINR can be significantly improved by using the matrix completion method under the condition of multiple interferences, and next to the SINR obtained by the enumeration method, the average SINR of the proposed method is lower than about 0.3dB by using the enumeration method. Therefore, the detection performance of the algorithm can still be improved under the condition of multiple interferences.
The enumeration method has too high computational complexity and is not suitable for practical application. In summary, compared with the sparse array obtained based on the SCA algorithm and the enumeration method, the method can significantly improve the target detection performance of the millimeter wave array radar. Therefore, the algorithm provided by the invention can provide a solid theoretical and engineering realization basis for improving the detection performance of the millimeter wave radar target of the automatic driving platform in practical application.
The embodiments of the present invention are illustrative, but not restrictive, of the invention in any manner. The technical features or combinations of technical features described in the embodiments of the present invention should not be considered as being isolated, and they may be combined with each other to achieve a better technical effect. The scope of the preferred embodiments of the present invention may also include additional implementations, and this should be understood by those skilled in the art to which the embodiments of the present invention pertain.

Claims (6)

1. A millimeter wave radar sparse array design method based on a maximized signal-to-noise ratio is characterized by comprising the following steps:
constructing a maximized SINR problem based on an FMCW millimeter wave radar array detection model;
under the condition of definite autocorrelation of spatial lag data, designing a sparse array by a successive linear approximation algorithm;
under the condition of uncertain autocorrelation of spatial lag data, interpolating autocorrelation values corresponding to missing lag by adopting a low-rank matrix completion method based on semi-definite Toeplitz constraint, and solving the sparse array design problem by a convex optimization method.
2. The millimeter wave radar sparse array design method based on the maximized signal-to-noise ratio as claimed in claim 1, wherein the problem of maximizing SINR is established based on an FMCW millimeter wave radar array detection model, specifically:
the automatic driving millimeter wave radar adopts FMCW with constant amplitude characteristic, and FMCW signals are expressed as:
s(t)=exp(j2πf0t+jπμt2)
wherein f is0Setting mu as initial frequency, setting B/T as modulation frequency, and setting B and T as signal frequency modulation bandwidth and frequency sweep period;
the millimeter wave radar receiving array is composed of N uniformly spaced and isotropic array elements, and then a signal x (t) received by the millimeter wave radar array element at time t is represented as:
Figure FDA0003035525770000011
wherein K is the number of interference sources,
Figure FDA0003035525770000012
is theta0The direction target is directed to the vector of the vector,
Figure FDA0003035525770000013
is thetakThe direction interference guide vector, d and lambda are the interval between adjacent array elements and the wavelength of the carrier wave respectively, and d is usually less than or equal to lambda/2; v (t) is the receive array noise, modeled as a gaussian distribution with a mean of 0 and a covariance of σv 2;si(t) is the interferer signal; at a sampling rate fsSampling is performed, and the digital beam forming input signal is:
Figure FDA0003035525770000014
after weighting by the beam weighting coefficients, the beam forming output signal is:
y(n)=wHx(n)
wherein w is a receiving weight vector;
based on the above, the SINR expression is as follows:
Figure FDA0003035525770000021
wherein R iss=σ2a(θ0)aH0) Is a target signal covariance matrix, σ2=E{s(n)sH(n) is the target signal power;
Figure FDA0003035525770000022
as a covariance matrix of interference and uncorrelated noise,
Figure FDA0003035525770000023
is the power of the kth interferer.
3. The millimeter wave radar sparse array design method based on the maximized signal-to-noise ratio as claimed in claim 2, wherein based on the minimum distortion MVDR criterion, the optimization problem of designing the weight w to maximize SINR is represented as:
Figure FDA0003035525770000024
s.t.wHRsw=1
because noise and interference can not be separated from array elements, R is adopted in practical applicationx=Rs+RiIn place of RiThen the above equation is written as:
Figure FDA0003035525770000025
s.t.wHRsw=1
solving the above problem requires only receiving the data correlation matrix Rx=E(xxH) And a desired DOA; based on
Figure FDA0003035525770000026
The received data correlation matrix is estimated from the received L snapshots x;
solving the above equation yields:
Figure FDA0003035525770000027
thus, the optimal output SINR is expressed as:
Figure FDA0003035525770000028
wherein, Λmax{. denotes the maximum eigenvalue.
4. The millimeter wave radar sparse array design method based on the maximized signal-to-noise ratio as claimed in claim 1, wherein under the condition of the known autocorrelation of the spatial lag data, the sparse array is designed by a successive linear approximation algorithm, specifically:
an additional sparsity constraint is introduced in the maximize SINR problem, namely:
Figure FDA0003035525770000029
s.t.wHRsw≥1
||w||0=P
wherein | · | purple sweet0Representing the number of the constraint weight vectors w as the number P of the selected sensors; the objective function in the above equation is interchanged with the quadratic constraint, i.e.:
Figure FDA0003035525770000031
s.t.wHRxw≤1
||w||0=P
the above problem is solved by defining a receiving weight real vector and a corresponding correlation matrix, namely:
Figure FDA0003035525770000032
Figure FDA0003035525770000033
||w||0=P
wherein,
Figure FDA0003035525770000034
and
Figure FDA0003035525770000035
are each Rs、RiAnd w is expressed in real numbers.
5. The millimeter wave radar sparse array design method based on the maximization of the signal-to-noise ratio as claimed in claim 4, wherein the SINR problem is equivalent to the following successive linear approximation form:
Figure FDA0003035525770000036
Figure FDA0003035525770000037
||w||0=P
wherein,
Figure FDA0003035525770000038
i denotes the ith iteration; by minimizing mixing1-∞Norm relaxation non-convexoNorm to obtain sparse solution with high efficiency:
Figure FDA0003035525770000039
Figure FDA00030355257700000310
wherein the vector
Figure FDA00030355257700000311
Comprises a real part and an imaginary part of a beam forming weight corresponding to the first sensor, | · caly |, aSelecting
Figure FDA00030355257700000312
Maximum value of (d); for the initial iteration, the regularization parameter μ is set to 0, and μ optimal values are obtained by binary search within upper and lower bounds.
6. The millimeter wave radar sparse array design method based on the maximization of the signal-to-noise ratio as claimed in claim 1, wherein the matrix completion method is represented as:
Figure FDA00030355257700000313
s.t.Toeplitz(l)≥0
wherein Toeplitz (l) returns a symmetric Toeplitz matrix, where l and lHDefining a first row and a first column, respectively, of Toeplitz (l); matrix RPFor a received data correlation matrix with a missing correlation lag, the corresponding element of the missing correlation lag is zeroed out; the symbol ≧ represents the Hadamard product, ≧ represents the matrix inequality with the semi-positive definite constraint; the matrix Z is a binary matrix and,
Figure FDA0003035525770000041
representing the square of the Frobenius norm of a matrix, where the norm is intended to minimize the sum of the squared errors between the observed correlation values and the corresponding terms of the unknown Toeplitz matrix; the regularization parameter ζ weighs error terms and tracking heuristic terms, the nominal value of which is usually adjusted according to numerical experience of the problem; efficient solutions are obtained based on a convex optimization toolkit.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113656747A (en) * 2021-08-13 2021-11-16 南京理工大学 Array self-adaptive beam forming method under multiple expected signals based on branch and bound
CN113779795A (en) * 2021-09-13 2021-12-10 中国科学院声学研究所 Array design method and device
CN114280545A (en) * 2021-12-08 2022-04-05 电子科技大学 Sparse linear array radar array distribution method based on low-rank Hankel matrix completion
CN115801075A (en) * 2022-11-08 2023-03-14 南京理工大学 Multi-band sparse array antenna selection and beam forming combined design method
WO2024178653A1 (en) * 2023-03-01 2024-09-06 Qualcomm Incorporated Techniques for obtaining spatial information with sparse antenna array

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105807275A (en) * 2016-04-28 2016-07-27 大连大学 MIMO-OFDM-STAP steady waveform design method based on partial clutter priori knowledge
CN107024681A (en) * 2017-05-05 2017-08-08 大连大学 MIMO radar transmit-receive combination optimization method under the conditions of not known based on clutter knowledge
CN107329110A (en) * 2017-08-24 2017-11-07 浙江大学 Wave arrival direction estimating method based on thinned array Direct interpolation
CN109298395A (en) * 2018-09-28 2019-02-01 西安建筑科技大学 A kind of thinned array Beamforming Method based on maximum Signal to Interference plus Noise Ratio
CN112099015A (en) * 2020-08-26 2020-12-18 浙江理工大学 Adaptive waveform design method for improving millimeter wave radar detection estimation performance

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105807275A (en) * 2016-04-28 2016-07-27 大连大学 MIMO-OFDM-STAP steady waveform design method based on partial clutter priori knowledge
CN107024681A (en) * 2017-05-05 2017-08-08 大连大学 MIMO radar transmit-receive combination optimization method under the conditions of not known based on clutter knowledge
CN107329110A (en) * 2017-08-24 2017-11-07 浙江大学 Wave arrival direction estimating method based on thinned array Direct interpolation
CN109298395A (en) * 2018-09-28 2019-02-01 西安建筑科技大学 A kind of thinned array Beamforming Method based on maximum Signal to Interference plus Noise Ratio
CN112099015A (en) * 2020-08-26 2020-12-18 浙江理工大学 Adaptive waveform design method for improving millimeter wave radar detection estimation performance

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
SYED A. HAMZA: "Sparse Array Beamforming Design for Wideband Signal Models", 《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》, pages 1211 - 1226 *
康雪艳;江碧涛;张云华;云日升;: "星载GMTI稀疏阵雷达的STAP研究", 系统工程与电子技术, no. 09 *
李前言;康春玉;: "阵列协方差矩阵与FOCUSS算法的DOA估计方法", 舰船电子工程, no. 09 *
王洪雁;房云飞;裴炳南;: "基于矩阵补全的二阶统计量重构DOA估计方法", 电子与信息学报, no. 06 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113656747A (en) * 2021-08-13 2021-11-16 南京理工大学 Array self-adaptive beam forming method under multiple expected signals based on branch and bound
CN113779795A (en) * 2021-09-13 2021-12-10 中国科学院声学研究所 Array design method and device
CN113779795B (en) * 2021-09-13 2023-06-30 中国科学院声学研究所 Array design method and device
CN114280545A (en) * 2021-12-08 2022-04-05 电子科技大学 Sparse linear array radar array distribution method based on low-rank Hankel matrix completion
CN114280545B (en) * 2021-12-08 2023-04-25 电子科技大学 Sparse linear array radar array method based on low-rank Hankel matrix completion
CN115801075A (en) * 2022-11-08 2023-03-14 南京理工大学 Multi-band sparse array antenna selection and beam forming combined design method
WO2024178653A1 (en) * 2023-03-01 2024-09-06 Qualcomm Incorporated Techniques for obtaining spatial information with sparse antenna array

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