CN108303683B - Single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method - Google Patents

Single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method Download PDF

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CN108303683B
CN108303683B CN201810081868.7A CN201810081868A CN108303683B CN 108303683 B CN108303683 B CN 108303683B CN 201810081868 A CN201810081868 A CN 201810081868A CN 108303683 B CN108303683 B CN 108303683B
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CN108303683A (en
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徐丽琴
武利翻
张丽果
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Xian University of Posts and Telecommunications
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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
    • 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
    • G01S7/41—Details 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

Abstract

The invention belongs to the technical field of radars, and discloses a single-base MIMO radar real-valued ESPRIT non-circular signal angle estimation method, which comprises the steps of respectively performing matched filtering on array element received data and transmitted signals to obtain an observation data vector; carrying out dimensionality reduction pretreatment on observation data to obtain a low-dimensional space receiving data vector; constructing a real-valued received data vector with doubled array aperture by using the non-circular characteristic of the signal and an Euler formula; constructing a rotation invariant relationship of the aperture extended virtual array; calculating a covariance matrix of the expanded received data, decomposing the eigenvalue of the covariance matrix, and estimating to obtain a real-value signal subspace; defining a new real-value signal subspace, and solving a rotation invariant equation of the new real-value signal subspace; and calculating to obtain the DOA estimated value of the target. The invention can greatly reduce the computational complexity of the ESPRIT algorithm while obviously improving the DOA estimation precision, and is suitable for occasions with low signal-to-noise ratio and low snapshot number.

Description

Single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method.
Background
Currently, the current state of the art commonly used in the industry is such that: a Multiple Input Multiple Output (MIMO) radar is a radar with a new system developed based on the MIMO communication technology. The MIMO radar utilizes the concept of waveform diversity, adopts a plurality of transmitting antennas to simultaneously transmit mutually orthogonal waveforms, and simultaneously adopts a plurality of receiving antennas to receive target reflected signals. Compared with the traditional phased array radar, the MIMO radar has higher angular resolution, more degrees of freedom and better angle estimation performance. Direction of arrival (DOA) estimation is an important research content of MIMO radar parameter estimation. The rotation invariant subspace technique (ESPRIT) is a classical subspace-like high-resolution DOA estimation algorithm. By utilizing the rotation invariant structures of the MIMO radar transmitting array and the MIMO radar receiving array respectively, the ESPRIT algorithm can be applied to the estimation of the DOA of the MIMO radar target. Researches show that by using the non-circular characteristic of the signal, the accuracy of radar parameter estimation can be obviously improved, and the estimation performance is improved. In the last decade, numerous scholars have conducted intensive research around the ESPRIT algorithm, and various ESPRIT improvement algorithms suitable for MIMO radar have been proposed. The U-ESPRIT algorithm (Electronics Letters,2012,48(3):179-181) carries out the real-valued transformation on the covariance matrix of the received data through the unitary transformation on the basis of ESPRIT, reduces the operation complexity of the algorithm, and improves the angle estimation performance under the conditions of low signal-to-noise ratio and low snapshot number. However, the method does not utilize the characteristics of the transmitted signal, so the gradual estimation performance of the method is the same as that of an ESPRIT algorithm. The C-ESPRIT algorithm (Electronics Letters,2010,46(25): 1692-. The RV-ESPRIT algorithm (Journal of Applied Remote Sensing,2016,10(2):025003) is a real-valued ESPRIT algorithm utilizing the characteristics of non-circular signals, although a real-valued processing means is adopted, the calculation complexity of the algorithm increases in a cubic manner along with the increase of the number of array elements, and when the number of MIMO channels is large, the calculation amount is still considerable.
In summary, the problems of the prior art are as follows:
(1) most of the existing ESPRIT algorithms do not fully utilize the non-circular characteristic of a transmitting signal, and under the conditions of low signal-to-noise ratio and low snapshot number, the precision of angle estimation is low or even the angle estimation is invalid due to inaccuracy of subspace estimation; (2) in order to utilize the non-circular characteristic of a transmitting signal, the existing ESPRIT algorithm generally directly constructs a virtual array with doubled aperture to improve the estimation precision of a target angle, which inevitably causes the calculation complexity of the algorithm to be increased sharply and is not beneficial to the real-time implementation of the algorithm.
The significance of solving the technical problems is as follows: the invention fully utilizes the non-circular characteristic of the transmitting signal, can improve the angle estimation precision of the ESPRIT algorithm, solves the problem that the performance of the ESPRIT algorithm is seriously deteriorated under the conditions of low signal-to-noise ratio and low snapshot number, and lays a theoretical foundation for the practical application of the ESPRIT algorithm. The invention can reduce the complexity of the prior non-circular signal ESPRIT algorithm, provides a high-efficiency MIMO radar non-circular angle degree estimation method, accelerates the target direction estimation speed, is beneficial to the real-time realization of the ESPRIT algorithm and promotes the practical application of the DOA estimation algorithm.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method.
The invention is realized in this way, a single-base MIMO radar real value ESPRIT non-circular signal angle estimation method, the single-base MIMO radar real value ESPRIT non-circular signal angle estimation method carries out matched filtering on array element receiving data and a transmitting waveform to obtain an observation data vector, carries out dimensionality reduction preprocessing on the observation data to obtain a low-dimensional space receiving data vector; constructing a real-value received data vector with doubled array aperture by using the non-circular characteristic of the signal and an Euler formula, constructing a rotation invariant relation of a virtual array with expanded aperture, calculating a covariance matrix of received data, decomposing a characteristic value, and estimating to obtain a signal subspace; and defining a new real-value signal subspace, solving a rotation invariant equation of the real-value signal subspace, and calculating to obtain the DOA of the target.
Further, the single-base MIMO radar real-valued ESPRIT non-circular signal direction-of-arrival method comprises the following steps:
step one, performing matched filtering on the received data of each receiving array element and a transmitting waveform to obtain an observation data vector x (t) after matched filtering;
selecting a dimension reduction transformation matrix U, and reducing the dimension of the observation data vector x (t) to obtain an observation data vector y (t) which is subjected to dimension reduction and is Ux (t);
step three, decomposing the observation data vector y (t) into a real part y by using an Euler formulac(t) and imaginary part ys(t) using the non-circular nature of the signal to concatenate the real and imaginary components of the observed data to construct a real valued received data vector y with doubled array aperturer(t)；
Step four, defining two selection matrixes J1And J2Constructing a rotation invariant relation J of the extended virtual array2Gr＝J1GrΩ；
Step five, calculating yr(t) dataCovariance matrix RyCarrying out characteristic value decomposition on the signal, and estimating to obtain a real-value signal subspace Us；
Step six, defining new signal subspace
Figure BDA0001561245190000031
Solving new rotation invariant equations of real-valued signal subspace using total least squares
Figure BDA0001561245190000032
Calculating to obtain a real-value matrix psi;
and seventhly, performing eigenvalue decomposition on the real value matrix psi to obtain P eigenvalues, and further obtaining the direction of arrival estimation of the P targets.
Further, the observation data vector in the first step is:
x(t)＝As(t)+n(t)；
wherein the content of the first and second substances,
Figure BDA0001561245190000033
for transmitting-receiving joint steering vector matrix, theta1,θ2,…,θPFor the direction of arrival of the P targets,
Figure BDA0001561245190000034
is used for transmitting array steering vectors, M is the number of transmitting antennas,
Figure BDA0001561245190000035
for receiving array steering vectors, N is the number of receive antennas,
Figure BDA0001561245190000036
is a Kronecker product operator; s (t) ═ s1(t),s2(t),…,sP(t)]TIs a signal vector; n (t) CMN×1Is zero mean with a covariance matrix of σ2Complex white gaussian noise vector of I. Under non-circular signal conditions, s (t) can be expressed as:
s(t)＝Λr(t)；
wherein r (t) is non-circularA signal satisfying r (t) ═ r*(t),
Figure BDA0001561245190000041
Representing an additional phase shift of the P-th signal.
Further, the observation data vector after the dimensionality reduction in the second step is as follows:
y(t)＝V1/2GΛr(t)+nT(t)；
wherein G ═ G (θ)1),g(θ2),…,g(θP)]，
Figure BDA0001561245190000042
NeM + N-1 is the effective array element number of the virtual linear array; n isT＝V1/2FHn (t) is the noise vector after dimension reduction;
Figure BDA0001561245190000043
for a diagonal matrix, diag (·) represents an element diagonal matrixing operation, and the transformation matrix F is defined as:
Figure BDA0001561245190000044
further, the aperture-doubled received data vector y constructed in said third stepr(t) is:
Figure BDA0001561245190000045
wherein, yc(t) and ys(t) real and imaginary parts of y (t), respectively;
Figure BDA0001561245190000051
Figure BDA0001561245190000052
Gc＝[gc(θ1),...,gc(θP)]，gc(θp)＝[cosβp,...,cos((Ne-1)πsinθp+βp)]T，Gs＝[gs(θ1),...,gs(θP)]，gs(θp)＝[sinβp,...,sin((Ne-1)πsinθp+βp)]T；
Figure BDA0001561245190000053
to spread the noise vector, ns(t)＝Im[n(t)]，nc(t)＝Re[n(t)]。
Further, the step four expands the rotation invariant equation J of the virtual array steering vector matrix2Gr＝J1GrOmega, select matrix J1And J2Is defined as
Figure BDA0001561245190000054
;
Wherein, T1And T2Is defined as:
Figure BDA0001561245190000055
the rotation invariant equation J2Gr＝J1GrIn the range of omega, the number of the main chain,
Figure BDA0001561245190000056
a diagonal matrix whose diagonal elements contain DOA information of the target.
Further, the expanded received data vector y in step fiver(t) has a covariance matrix of Ry＝E{y(t)ryr(t)HThe eigenvalues decompose into:
Ry＝UsΣsUs H+UnΣnUn H；
wherein, sigmasIs represented by RyP large eigenvalues of UsIs the signal subspace corresponding to it; sigmanIs composed of the rest (2N)e-1-P) diagonal matrices of small eigenvalues, UnIs the noise subspace corresponding thereto.
Further, the new real-valued signal subspace is defined as
Figure BDA0001561245190000061
The new real-valued signal subspace has a rotation invariant equation of
Figure BDA0001561245190000062
Further, the DOA estimated values of the P targets in step seven can be calculated by the following formula:
Figure BDA0001561245190000063
wherein λ is1,λ2,......,λpFor the P eigenvalues of the real-valued matrix Ψ,
Figure BDA0001561245190000064
is the DOA estimate for P targets.
Another objective of the present invention is to provide a MIMO radar applying the real-valued ESPRIT non-circular signal angle estimation method for monostatic MIMO radar.
In summary, the advantages and positive effects of the invention are: the invention adopts dimension reduction transformation to carry out dimension reduction pretreatment on the observation data, can greatly reduce the dimension of the operation data on the whole, ensures that the subsequent calculation of the algorithm is carried out in a low-dimensional space, and then constructs a real-valued received data vector by using an Euler formula, so that the subsequent calculation is real-valued calculation, thereby having lower calculation complexity and being beneficial to the real-time realization of the algorithm. The invention constructs an expanded real value receiving data vector by using the characteristics of non-circular signals, expands the aperture of the array to twice of the original aperture, and then performs DOA estimation on the target by using a rotation invariant structure of the expanded array, thereby obviously improving the angle estimation precision of the ESPRIT algorithm and being suitable for occasions with low signal-to-noise ratio and low snapshot number. Therefore, the present invention can provide significantly improved angle estimation accuracy with lower computational complexity.
Drawings
Fig. 1 is a flowchart of a method for estimating an actual ESPRIT non-circular signal angle of a monostatic MIMO radar according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of target DOA estimated values obtained by performing 100 simulation experiments under the conditions that M is 8, N is 6, L is 100, and SNR is 10dB according to an embodiment of the present invention.
Fig. 3 is a graph showing the root mean square error of the angle estimation as a function of the signal-to-noise ratio under the conditions that M is 8, N is 6, and L is 100.
Fig. 4 is a graph showing the root mean square error of the angle estimation with the fast beat number under the conditions of M8, N6 and SNR 10 dB.
Fig. 5 is a graph showing the operation complexity varying with the number of array elements under the conditions that M is N, L is 200, and P is 3.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the method for estimating the real-valued ESPRIT non-circular signal angle of the monostatic MIMO radar according to the embodiment of the present invention includes the following steps:
s101: carrying out matched filtering on array element receiving data and a transmitting waveform to obtain an observation data vector;
s102: carrying out dimensionality reduction pretreatment on observation data to obtain a low-dimensional space receiving data vector;
s103: constructing a real-valued received data vector with doubled array aperture by using the non-circular characteristic of the signal and an Euler formula;
s104: constructing a rotation invariant relationship of the aperture extended virtual array;
s105: calculating a covariance matrix of received data, decomposing an eigenvalue, and estimating to obtain a signal subspace;
s106: defining a new real-value signal subspace and solving a rotation invariant equation of the real-value signal subspace;
s107: and calculating to obtain the DOA of the target.
The method for estimating the real-valued ESPRIT non-circular signal angle of the single-base MIMO radar specifically comprises the following steps:
(1) the MIMO radar transmits mutually orthogonal pulse coding signals by using M transmitting antennas, assumes that P incoherent narrow-band targets exist in a far-field space, receives target reflection signals at a receiving end by using N receiving antennas, and performs matched filtering on each received data by using a matched filter to obtain an observation data vector;
the observation vectors involved are:
x(t)＝As(t)+n(t)；
wherein the content of the first and second substances,
Figure BDA0001561245190000081
for transmitting-receiving joint steering vector matrix, theta1,θ2,…,θPFor the direction of arrival of the P targets,
Figure BDA0001561245190000082
in order to transmit the array-directed vector,
Figure BDA0001561245190000083
in order to receive the array steering vector,
Figure BDA0001561245190000084
is a Kronecker product operator; s (t) ═ s1(t),s2(t),…,sP(t)]TIs a signal vector; n (t) CMN×1Is zero mean with a covariance matrix of σ2Complex white gaussian noise vector of I. Under non-circular signal conditions, s (t) can be expressed as:
s(t)＝Λr(t)；
wherein r (t) is a non-circular signal and satisfies r (t) r*(t),
Figure BDA0001561245190000085
Representing an additional phase shift of the P-th signal. Thus, the observation data vector may be expressed as:
x(t)＝AΛr(t)+n(t)；
(2) selecting a dimension-reducing transformation matrix U as V-1/2FHReducing the dimension of the observation data vector x (t) to obtain a data matrix y (t) which is subjected to dimension reduction and is Ux (t);
the observation data vector after the dimensionality reduction is as follows:
y(t)＝V1/2GΛr(t)+nT(t)；
wherein G ═ G (θ)1),g(θ2),…,g(θP)]，
Figure BDA0001561245190000086
NeM + N-1 is the effective array element number of the virtual linear array;
Figure BDA0001561245190000087
for a diagonal matrix, diag (·) represents an element diagonal matrixing operation, and the transformation matrix F is defined as:
Figure BDA0001561245190000091
nT＝V1/2FHn (t) is the noise vector after dimension reduction.
(3) Decomposing the observation data vector y (t) into real parts y by using Euler formulac(t) and imaginary part ys(t) constructing a reception data vector y with two parts in series and with double aperture by using non-circular characteristic of signalr(t)；
The real part y of the data vector y (t) concernedc(t) and imaginary part ys(t) are respectively:
Figure BDA0001561245190000092
the extended received data vector y involvedr(t) is:
Figure BDA0001561245190000093
wherein the content of the first and second substances,
Figure BDA0001561245190000094
to extend the steering vector matrix of the virtual array, Gc＝[gc(θ1),...,gc(θP)]，gc(θp)＝[cosβp,...,cos((Ne-1)πsinθp+βp)]T，
Gs＝[gs(θ1),...,gs(θP)]，gs(θp)＝[sinβp,...,sin((Ne-1)πsinθp+βp)]T；
Figure BDA0001561245190000101
To spread the noise vector, ns(t)＝Im[n(t)]，nc(t)＝Re[n(t)]。
(4) Two selection matrices J are defined1And J2Constructing a rotation invariant equation of the extended virtual array steering vector matrix;
the selection matrix J involved1And J2Comprises the following steps:
Figure BDA0001561245190000102
wherein, T1And T2Is defined as:
Figure BDA0001561245190000103
the rotation invariant equation of the extended virtual array steering vector matrix involved is:
J2Gr＝J1GrΩ；
wherein the content of the first and second substances,
Figure BDA0001561245190000104
a diagonal matrix whose diagonal elements contain DOA information of the target.
(5) Computing an extended received data vector yr(t) autocorrelation matrix RyCarrying out characteristic value decomposition on the signal, and estimating to obtain a signal subspace Us；
Reference to yr(t) has an autocorrelation matrix of Ry＝E{y(t)ryr(t)HIts eigenvalue decomposition can be expressed as:
Ry＝UsΣsUs H+UnΣnUn H；
wherein, sigmasIs represented by RyP large eigenvalues of UsIs the signal subspace corresponding to it; sigmanIs composed of the rest (2N)e-1-P) diagonal matrices of small eigenvalues, UnIs the noise subspace corresponding thereto.
(6) Computing a new real-valued signal subspace
Figure BDA0001561245190000111
Solving a new rotation invariant equation of the signal subspace by using a least square method or a total least square method, and calculating to obtain a real value matrix psi;
the new signal subspace involved is
Figure BDA0001561245190000112
The rotation invariant equation of the involved signal subspace is
Figure BDA0001561245190000113
(7) Carrying out eigenvalue decomposition on psi to obtain P eigenvalues lambda1,λ2,......,λpAnd then calculating to obtain the DOA estimation of the target.
The DOAs of the P targets involved can be estimated by:
Figure BDA0001561245190000114
wherein the content of the first and second substances,
Figure BDA0001561245190000115
is the DOA estimate for P targets.
The application effect of the present invention will be described in detail with reference to the simulation.
Simulation conditions and contents
Considering a single-ground MIMO radar system composed of uniform linear arrays, the number M of transmitting array elements is 8, the number N of receiving array elements is 6, and the distance between each array element is half wavelength. Supposing that 3 incoherent narrow-band targets exist in far-field space, and the azimuth angle of each target is theta1＝100,θ2＝150,θ3＝200. To verify the effectiveness of the present invention, the present invention was compared to the U-ESPRIT algorithm, RV-ESPRIT algorithm, and C-ESPRIT algorithm. The root mean square error of the angle estimate is defined as:
Figure BDA0001561245190000116
wherein K is the total Monte-Carlo experiment times,
Figure BDA0001561245190000117
represents the DOA estimate, θ, for the p-th target in the kth Monte-Carlo experimentpThe true angle value of the p-th target.
(II) simulation results
1. MIMO radar target positioning performance
Fig. 2 shows target DOA estimated values obtained by performing 100 simulation experiments according to the present invention under the conditions of M being 8, N being 6, L being 100, and SNR being 10 dB. As can be seen from FIG. 2, the algorithm of the present invention can be used to accurately locate multiple targets simultaneously.
2. Variation relation of root mean square error of MIMO radar angle estimation along with signal-to-noise ratio
Fig. 3 is a plot of root mean square error as a function of signal-to-noise ratio for angle estimation obtained by performing 500 Monte-Carlo experiments under conditions of M8, N6, and L100. As can be seen from FIG. 2, under the condition of low signal-to-noise ratio, the estimation accuracy of the invention, the C-ESPRIT algorithm and the RV-ESPRIT algorithm is better than that of the U-ESPRIT algorithm, wherein the angle estimation accuracy of the invention and the C-ESPRIT algorithm is obviously improved. The estimation precision of each algorithm is improved along with the increase of the signal-to-noise ratio, and the algorithm has the same gradual estimation performance as the C-ESPRIT algorithm.
3. Variation relation of root mean square error of MIMO radar angle estimation along with fast beat number
FIG. 4 is a graph of the root mean square error of the angle estimation with the fast beat number obtained from 500 Monte-Carlo experiments under the conditions of M8, N6 and SNR 10 dB. As can be seen from FIG. 3, the estimation accuracy of the invention, the C-ESPRIT algorithm and the RV-ESPRIT algorithm is better than that of the U-ESPRIT algorithm. Because the invention and the C-ESPRIT algorithm both utilize the received data with doubled virtual aperture, the angle estimation precision of the two is greatly improved, and the angle estimation precision is basically close.
4. Relation of operation complexity of MIMO radar angle estimation along with variation of number of transmitting and receiving antennas
As can be seen from fig. 5, the operation complexity (real-valued multiplication times) of each algorithm increases as the number of array elements increases. The operation complexity of the U-ESPRIT algorithm, the C-ESPRIT algorithm and the RV-ESPRIT algorithm all sharply rises along with the increase of the number of the array elements, and the operation complexity of the algorithm is slowly changed along with the number of the array elements and is lowest. The invention adopts dimension reduction transformation and real-valued operation at the same time, thereby greatly reducing the operation complexity of the ESPRIT algorithm.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (9)

1. A single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method is characterized in that the single-base MIMO radar real-value ESPRIT non-circular signal angle estimation method carries out matched filtering on array element receiving data and a transmitting waveform to obtain an observation data vector, and carries out dimensionality reduction preprocessing on the observation data to obtain a low-dimensional space receiving data vector; constructing a real-value received data vector with doubled array aperture by using the non-circular characteristic of the signal and an Euler formula, constructing a rotation invariant relation of a virtual array with expanded aperture, calculating a covariance matrix of received data, decomposing a characteristic value, and estimating to obtain a signal subspace; defining a new real-value signal subspace, solving a rotation invariant equation of the real-value signal subspace, and calculating to obtain the DOA of the target;
the method for estimating the direction of arrival of the real-valued ESPRIT non-circular signal of the single-base MIMO radar comprises the following steps:
step one, performing matched filtering on the received data of each receiving array element and a transmitting waveform to obtain an observation data vector x (t) after matched filtering;
selecting a dimension reduction transformation matrix U, and reducing the dimension of the observation data vector x (t) to obtain an observation data vector y (t) which is subjected to dimension reduction and is Ux (t);
step three, decomposing the observation data vector y (t) into a real part y by using an Euler formulac(t) and imaginary part ys(t) using the non-circular nature of the signal to concatenate the real and imaginary components of the observed data to construct a real valued received data vector y with doubled array aperturer(t)；
Step four, defining two selection matrixes J1And J2Constructing a rotation invariant relationship for the extended virtual array
Figure FDA0003241279090000011
A diagonal matrix whose diagonal elements contain DOA information of the object,
Figure FDA0003241279090000012
to extend the steering vector matrix of the virtual array, Gc＝[gc(θ1),...,gc(θP)]，gc(θp)＝[cosβp,...,cos((Ne-1)πsinθp+βp)]T，Gs＝[gs(θ1),...,gs(θP)]，gs(θp)＝[sinβp,...,sin((Ne-1)πsinθp+βp)]T；
Step five, calculating yr(t) data covariance matrix RyCarrying out characteristic value decomposition on the signal, and estimating to obtain a real-value signal subspace Us；
Step six, defining new real-value signal subspace
Figure FDA0003241279090000013
Solving new rotation invariant equations of real-valued signal subspace using total least squares
Figure FDA0003241279090000014
Calculating to obtain a real-value matrix psi;
and seventhly, performing eigenvalue decomposition on the real value matrix psi to obtain P eigenvalues of the real value matrix psi, and further estimating the arrival directions of the P targets.
2. The method of claim 1, wherein the observation data vector in the first step is:
x(t)＝As(t)+n(t)；
wherein the content of the first and second substances,
Figure FDA0003241279090000021
for transmitting-receiving joint steering vector matrix, theta1,θ2,…,θPFor the direction of arrival of the P targets,
Figure FDA0003241279090000022
is used for transmitting array steering vectors, M is the number of transmitting antennas,
Figure FDA0003241279090000023
for receiving array directorThe quantity N is the number of receiving antennas,
Figure FDA0003241279090000024
is a Kronecker product operator; s (t) ═ s1(t),s2(t),…,sP(t)]TIs a signal vector; n (t) CMN×1Is zero mean with a covariance matrix of σ2I complex gaussian white noise vector, s (t) can be expressed as:
s(t)＝Λr(t)；
wherein r (t) is a non-circular signal and satisfies r (t) r*(t),
Figure FDA0003241279090000025
Figure FDA0003241279090000026
Representing an additional phase shift of the P-th reflected signal.
3. The method for estimating the angle of the real-valued ESPRIT non-circular signal of the monostatic MIMO radar according to claim 1, wherein the observation data vector after the dimension reduction in the second step is:
y(t)＝V1/2GΛr(t)+nT(t)；
wherein G ═ G (θ)1),g(θ2),…,g(θP)]，
Figure FDA0003241279090000027
NeM + N-1 is the effective array element number of the virtual linear array; n isT＝V1/2FHn (t) is the noise vector after dimension reduction;
Figure FDA0003241279090000028
for a diagonal matrix, diag (·) represents an element diagonal matrixing operation, and the transformation matrix F is defined as:
Figure FDA0003241279090000031
4. the method of claim 1, wherein the aperture-doubled received data vector y constructed in step three is a real-valued ESPRIT non-circular signal angle estimation method for monostatic MIMO radarr(t) is:
Figure FDA0003241279090000032
wherein, yc(t) and ys(t) real and imaginary parts of y (t), respectively;
Figure FDA0003241279090000033
Figure FDA0003241279090000034
Gc＝[gc(θ1),...,gc(θP)]，gc(θp)＝[cosβp,...,cos((Ne-1)πsinθp+βp)]T，Gs＝[gs(θ1),...,gs(θP)]，gs(θp)＝[sinβp,...,sin((Ne-1)πsinθp+βp)]T；
Figure FDA0003241279090000035
to spread the noise vector, nc(t)＝Re[n(t)]，ns(t)＝Im[n(t)]。
5. The method of claim 1, wherein the step four expands a rotation invariant equation J of a virtual array steering vector matrix2Gr＝J1GrOmega, select matrix J1And J2Is defined as:
Figure FDA0003241279090000036
wherein, T1And T2Is defined as:
Figure FDA0003241279090000041
the rotation invariant equation J2Gr＝J1GrIn the range of omega, the number of the main chain,
Figure FDA0003241279090000042
is a diagonal matrix whose diagonal elements contain DOA information of the object.
6. The method of claim 1, wherein the extended received data vector y in step five is the received data vector yr(t) has a covariance matrix of Ry＝E{y(t)ryr(t)HThe eigenvalues decompose into:
Ry＝UsΣsUs H+UnΣnUn H；
wherein, sigmasIs represented by RyP large eigenvalues of UsIs the signal subspace corresponding to it; sigmanIs composed of the rest (2N)e-1-P) diagonal matrices of small eigenvalues, UnIs the noise subspace corresponding thereto.
7. The method of claim 1, wherein the new real-valued signal subspace of step six is defined as
Figure FDA0003241279090000043
The new real-valued signal subspace has a rotation invariant equation of
Figure FDA0003241279090000044
8. The method of claim 1, wherein the DOA estimates for the P targets in step seven are calculated by:
Figure FDA0003241279090000045
wherein λ is1,λ2,......,λpFor the P eigenvalues of the real-valued matrix Ψ,
Figure FDA0003241279090000046
is the DOA estimate for P targets.
9. A multi-input multi-output radar applying the real-valued ESPRIT non-circular signal angle estimation method of the monostatic MIMO radar according to any one of claims 1 to 8.
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Publication number Priority date Publication date Assignee Title
CN109521392B (en) * 2018-10-24 2022-12-16 华南理工大学 Underwater one-dimensional DOA estimation method and device based on non-circular signal and L-shaped linear array
CN109521393A (en) * 2018-11-05 2019-03-26 昆明理工大学 A kind of DOA estimation algorithm based on signal subspace revolving property
CN109407048B (en) * 2018-11-12 2023-09-29 华南理工大学 Underwater DOA estimation method and device based on non-circular signal and included angle adjustable array
CN109975775B (en) * 2019-04-03 2022-12-23 西安邮电大学 Radar echo semi-actual measurement data simulation method
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CN112731303B (en) * 2020-12-21 2024-04-09 南昌工程学院 Interference array meter wave radar under non-Gaussian noise and steady height measurement method and application
CN112965059A (en) * 2021-02-06 2021-06-15 西安邮电大学 Real-value root-finding multi-signal classification method, system, computer equipment and application

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8238496B1 (en) * 2010-05-18 2012-08-07 Applied Micro Circuits Corporation Multiuser multiple-input multiple-output (MU-MIMO) channel estimation for multicarrier communications
CN103901417A (en) * 2014-04-02 2014-07-02 哈尔滨工程大学 Low-complexity space target two-dimensional angle estimation method of L-shaped array MIMO radar
CN104730517A (en) * 2015-03-23 2015-06-24 西安电子科技大学 Bistatic MIMO radar multi-target tracking method
CN105093185A (en) * 2015-08-23 2015-11-25 哈尔滨工程大学 Sparse representation-based single-base multi-output multi-input radar target direction of arrival estimation method
CN107576940A (en) * 2017-07-10 2018-01-12 西安邮电大学 A kind of not rounded signal angle method of estimation of low complex degree list base MIMO radar

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8238496B1 (en) * 2010-05-18 2012-08-07 Applied Micro Circuits Corporation Multiuser multiple-input multiple-output (MU-MIMO) channel estimation for multicarrier communications
CN103901417A (en) * 2014-04-02 2014-07-02 哈尔滨工程大学 Low-complexity space target two-dimensional angle estimation method of L-shaped array MIMO radar
CN104730517A (en) * 2015-03-23 2015-06-24 西安电子科技大学 Bistatic MIMO radar multi-target tracking method
CN105093185A (en) * 2015-08-23 2015-11-25 哈尔滨工程大学 Sparse representation-based single-base multi-output multi-input radar target direction of arrival estimation method
CN107576940A (en) * 2017-07-10 2018-01-12 西安邮电大学 A kind of not rounded signal angle method of estimation of low complex degree list base MIMO radar

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ESPRIT-like algorithm for computational-efficient angle estimation in bistatic multiple-input multiple-output radar;Jian Gong,Shuntian Lou,Yiduo Guo;《Journal of Applied Remote Sensing》;20160415;第10卷(第2期);第1-9页 *

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