CN108303683A - Single not rounded signal angle methods of estimation of base MIMO radar real value ESPRIT - Google Patents
Single not rounded signal angle methods of estimation of base MIMO radar real value ESPRIT Download PDFInfo
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Abstract
The invention belongs to Radar Technology fields, disclose a kind of list not rounded signal angle methods of estimation of base MIMO radar real value ESPRIT, and array element is received data carries out matched filtering with transmitting signal respectively, obtains observation data vector；Dimensionality reduction pretreatment is carried out to observation data, lower dimensional space is obtained and receives data vector；Data vector is received using the real value that the not rounded characteristic and Euler formula construction array apertures of signal double；Construct the invariable rotary relationship of the virtual array of aperture extension；The covariance matrix that extension receives data is calculated, Eigenvalues Decomposition is carried out to it, estimation obtains real-valued signal subspace；New real-valued signal subspace is defined, the invariable rotary equation of new real-valued signal subspace is solved；The DOA estimated values of target are calculated.The computation complexity of ESPRIT algorithms can be greatly lowered in the present invention while significantly improving DOA estimated accuracies, be suitable for low signal-to-noise ratio and low number of snapshots occasion.
Description
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 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):1692-1694) utilizes the characteristics of non-circular signals to construct a virtual array with doubled array aperture, which can obviously improve the angle estimation precision, but the calculation complexity also obviously increases along with the doubling of matrix dimension, thus being not beneficial to the real-time realization of the estimation algorithm. 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:
firstly, 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) after dimension reduction as 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) data covariance matrix RyDecomposing the characteristic value of the signal, estimating to obtain the real value signalNumber space Us；
Step six, defining new signal subspaceSolving new rotation invariant equations of real-valued signal subspace using total least squaresCalculating 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,for transmitting-receiving joint steering vector matrix, theta1,θ2,…,θPFor the direction of arrival of the P targets,is used for transmitting array steering vectors, M is the number of transmitting antennas,for receiving array steering vectors, N is the number of receive antennas,is a Kronecker product operator; s (t) ═ s1(t),s2(t),…,sP(t)]TIs a signal vector; n (t) is belonged to 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 the condition that r (t) is r*(t),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)]，NeM + N-1 is the effective array element number of the virtual linear array; n isT＝V1/2FHn (t) is a noise vector after dimensionality reduction;for a diagonal matrix, diag (·) represents an element diagonal matrixing operation, and the transformation matrix F is defined as:
further, the aperture-doubled received data vector y constructed in said third stepr(t) is:
wherein, yc(t) and ys(t) is the real and imaginary parts of y (t), respectively; 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；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;
wherein, T1And T2Is defined as:
the rotation invariant equation J2Gr＝J1GrIn the range of omega, the number of the main chain,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 asThe new real-valued signal subspace has a rotation invariant equation of
Further, the DOA estimated values of the P targets in step seven can be calculated by the following formula:
wherein λ is1,λ2,......,λpFor the P eigenvalues of the real-valued matrix Ψ,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,for transmitting-receiving joint steering vector matrix, theta1,θ2,…,θPFor the direction of arrival of the P targets,in order to transmit the array-directed vector,in order to receive the array steering vector,is a Kronecker product operator; s (t) ═ s1(t),s2(t),…,sP(t)]TIs a signal vector; n (t) is belonged to 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 the condition that r (t) is r*(t),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) after dimension reduction as 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)]，NeM + N-1 is the effective array element number of the virtual linear array;for a diagonal matrix, diag (·) represents an element diagonal matrixing operation, and the transformation matrix F is defined as:
nT＝V1/2FHn (t) is the noise vector after dimensionality 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:
the extended received data vector y involvedr(t) is:
wherein,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；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:
wherein, T1And T2Is defined as:
the rotation invariant equation of the extended virtual array steering vector matrix involved is:
J2Gr＝J1GrΩ；
wherein,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 subspaceSolving 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 isThe rotation invariant equation of the involved signal subspace is
(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:
wherein,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:
wherein K is the total Monte-Carlo experiment times,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 (10)
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; 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.
2. The method of claim 1, wherein the method of estimating the arrival direction of the monostatic MIMO radar real-valued ESPRIT non-circular signal comprises the steps of:
firstly, 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) after dimension reduction as 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) 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 subspaceSolving new rotation invariant equations of real-valued signal subspace using total least squaresCalculating 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.
3. The method of claim 2, wherein the observation data vector in the first step is:
x(t)＝As(t)+n(t)；
wherein,for transmitting-receiving joint steering vector matrix, theta1,θ2,…,θPFor the direction of arrival of the P targets,is used for transmitting array steering vectors, M is the number of transmitting antennas,for receiving array steering vectors, N is the number of receive antennas,is a Kronecker product operator; s (t) ═ s1(t),s2(t),…,sP(t)]TIs a signal vector; n (t) is belonged to 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 Representing an additional phase shift of the P-th reflected signal.
4. The method for estimating the angle of the real-valued ESPRIT non-circular signal of the monostatic MIMO radar according to claim 2, 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)]，NeM + N-1 is the effective array element number of the virtual linear array; n isT＝V1/2FHn (t) is a noise vector after dimensionality reduction;for a diagonal matrix, diag (·) represents an element diagonal matrixing operation, and the transformation matrix F is defined as:
5. the method of claim 2, 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:
wherein, yc(t) and ys(t) is the real and imaginary parts of y (t), respectively; 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；to spread the noise vector, nc(t)＝Re[n(t)]，ns(t)＝Im[n(t)]。
6. The method of claim 2, wherein the step four expands a rotation invariant equation J of a virtual array steering vector matrix2Gr＝J1GrOmega, select matrix J1And J2Is defined as:
wherein, T1And T2Is defined as:
the rotation invariant equation J2Gr＝J1GrIn the range of omega, the number of the main chain,is a diagonal matrix whose diagonal elements contain DOA information of the object.
7. The method of claim 2, 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.
8. The method of claim 2, wherein the new real-valued signal subspace of step six is defined asThe new real-valued signal subspace has a rotation invariant equation of
9. The method of claim 2, wherein the DOA estimates for the P targets in step seven are calculated by:
wherein λ is1,λ2,......,λpFor the P eigenvalues of the real-valued matrix Ψ,is the DOA estimate for P targets.
10. A MIMO radar using the method for estimating real-valued ESPRIT non-circular signal angles of monostatic MIMO radar according to any one of claims 1 to 9.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109407048A (en) * | 2018-11-12 | 2019-03-01 | 华南理工大学 | Underwater DOA estimation method and device based on non-circular signal and the adjustable battle array of angle |
| 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 |
| CN109521392A (en) * | 2018-10-24 | 2019-03-26 | 华南理工大学 | Underwater one-dimensional DOA estimation method and device based on non-circular signal and L-type linear array |
| CN109975775A (en) * | 2019-04-03 | 2019-07-05 | 西安邮电大学 | A kind of half measured data emulation mode of radar return |
| CN110929371A (en) * | 2019-09-18 | 2020-03-27 | 中国电子科技集团公司第三十八研究所 | Virtual interpolation subarray calculation method and system based on least square estimation |
| CN112327264A (en) * | 2020-10-14 | 2021-02-05 | 海南大学 | Bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method |
| CN112731303A (en) * | 2020-12-21 | 2021-04-30 | 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)
| 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 |
-
2018
- 2018-01-29 CN CN201810081868.7A patent/CN108303683B/en not_active Expired - Fee Related
Patent Citations (5)
| 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)
| Title |
|---|
| JIAN GONG,SHUNTIAN LOU,YIDUO GUO: "ESPRIT-like algorithm for computational-efficient angle estimation in bistatic multiple-input multiple-output radar", 《JOURNAL OF APPLIED REMOTE SENSING》 * |
Cited By (14)
| 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 |
| CN109521392A (en) * | 2018-10-24 | 2019-03-26 | 华南理工大学 | Underwater one-dimensional DOA estimation method and device based on non-circular signal and L-type 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 |
| CN109407048A (en) * | 2018-11-12 | 2019-03-01 | 华南理工大学 | Underwater DOA estimation method and device based on non-circular signal and the adjustable battle array of angle |
| CN109975775B (en) * | 2019-04-03 | 2022-12-23 | 西安邮电大学 | Radar echo semi-actual measurement data simulation method |
| CN109975775A (en) * | 2019-04-03 | 2019-07-05 | 西安邮电大学 | A kind of half measured data emulation mode of radar return |
| CN110929371A (en) * | 2019-09-18 | 2020-03-27 | 中国电子科技集团公司第三十八研究所 | Virtual interpolation subarray calculation method and system based on least square estimation |
| CN112327264A (en) * | 2020-10-14 | 2021-02-05 | 海南大学 | Bistatic FDA-MIMO radar multi-dimensional parameter joint estimation method |
| CN112327264B (en) * | 2020-10-14 | 2023-10-13 | 海南大学 | Bistatic FDA-MIMO radar multidimensional parameter joint estimation method |
| CN112731303A (en) * | 2020-12-21 | 2021-04-30 | 南昌工程学院 | Interference array meter wave radar under non-Gaussian noise, and steady height measurement method and application |
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