In this paper, a sparse representation approach based on fourth-order cumulants (FOC) is proposed for direction of arrival (DOA) estimation in monostatic multiple-inputmultiple-output (MIMO) radar with unknown mutual...
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In this paper, a sparse representation approach based on fourth-order cumulants (FOC) is proposed for direction of arrival (DOA) estimation in monostatic multiple-inputmultiple-output (MIMO) radar with unknown mutual coupling. For applying the sparse representation theory successfully, exploiting the special banded symmetric Toeplitz structure of mutual coupling matrices (MCM) in both transmit array and receive array, the unknown MCM in received data can be turned into a diagonal one to eliminate the mutual coupling. Then based on the new received data, a reduced dimensional transformation matrix is formulated, and the proposed method further constructs a FOC matrix with special formation, which reduce the computational complexity of sparse signal reconstruction. Finally a reweighted l(1)-norm constraint minimization sparse representation framework is designed, and the DOAs can be obtained by finding the non-zero rows in the recovered matrix. Owing to utilizing the fourth-order cumulants and reweighted sparse representation framework, compared with ESPRIT-Like, FOC-MUSIC and l(1)-SVD algorithms, the proposed method performs well in both white and colored Gaussian noise conditions, meanwhile it has higher angular resolution and better angle estimation performance. Simulation results verify the effectiveness and advantages of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.
A multipleinputmultipleoutput (MIMO) radar allows its antenna elements to transmit multiple signal waveforms. This waveform diversity offers enhanced flexibility in transmit beampattern synthesis and signal design....
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A multipleinputmultipleoutput (MIMO) radar allows its antenna elements to transmit multiple signal waveforms. This waveform diversity offers enhanced flexibility in transmit beampattern synthesis and signal design. Two algorithms where the transmit is synthesised beampattern by optimising transmitted signal covariance matrix is proposed. In the first algorithm, a method to achieve a desired beampattern by reformulating the design problem as an unconstrained semi-definite programming problem is proposed. Second, a method for beampattern synthesis such that the cross correlation beampattern, which is the correlation between the signals at different target locations, is minimised and at the same time the sidelobe levels are constrained to be lower than a threshold value proposed. These design problems are modelled as convex optimisation problem and are solved using Matlab based toolbox CVX.
In this paper, a covariance vector sparsity-aware DOA estimation method is proposed for monostatic multiple-inputmultiple-output (MIMO) radar with unknown mutual coupling. The new method firstly utilizes the banded s...
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In this paper, a covariance vector sparsity-aware DOA estimation method is proposed for monostatic multiple-inputmultiple-output (MIMO) radar with unknown mutual coupling. The new method firstly utilizes the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM) in both of the transmit and receive arrays to eliminate the unknown mutual coupling. Then a sparse representation framework of the array covariance vector is formulated for obtaining the coarse DOA estimation. Finally, a refined maximum likelihood estimation procedure is introduced to estimate the DOA based on the recovered result. Compared with conventional algorithms, the proposed method provides higher angular resolution and better angle estimation performance. Furthermore, the computational complexity of the proposed method is reasonable, because it only involves single measurement vector (SMV) problem and does not require a dense discretized sampling grid for the recovered procedure. Simulation results are used to verify the effectiveness and advantages of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.
We address the problem of direction-of-arrival (DOA) estimation for compressive sensing based multiple-inputmultiple-output (CS-MIMO) radar. The spatial sparsity of the targets enables CS to be desirable for DOA esti...
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ISBN:
(纸本)9783319483900;9783319483894
We address the problem of direction-of-arrival (DOA) estimation for compressive sensing based multiple-inputmultiple-output (CS-MIMO) radar. The spatial sparsity of the targets enables CS to be desirable for DOA estimation. By discretizing the possible target angles, a overcomplete dictionary is constructed for DOA estimation. A structural sparsity Bayesian learning framework is presented for support recovery. To improve the recovery accuracy and speed up the Bayesian iteration, a subspace sparse Bayesian learning algorithm is developed. The proposed scheme, which needs less iteration steps, can provides high precision DOA estimation performance for CS-MIMO radar, even at the condition of low signal-to-noise ratio and coherent sources. Simulation results verify the usefulness of our scheme.
Adopting the eigen threshold(ET) criterion, a multi-target detection algorithm for MIMO radar with arbitrarily correlated observation channels is analyzed. Firstly, we derive the close-form of the eigenvalue, and the ...
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ISBN:
(纸本)9781509007684
Adopting the eigen threshold(ET) criterion, a multi-target detection algorithm for MIMO radar with arbitrarily correlated observation channels is analyzed. Firstly, we derive the close-form of the eigenvalue, and the eigenvectors for the MIMO radar system having an arbitrary distributed array-target configuration based on power spectral density function, then, we establish the MIMO radar detector Adopting the eigen threshold(ET) criterion. The Method proposed avoids the Multidimensional Matrix inversion and alleviates the computational burden. Simulation results show that the correlation of the observation channels will reduce the detection performance. The algorithm can be utilized to compute and analyze the detection performance of a MIMO radar system for any given transmitter/receiver geometry, the computer simulation results also show that the MIMO ET has better performance than phase array detectors.
This paper addresses the problem of adaptive multiple-inputmultiple-output (MIMO) radar detection in heterogeneous environment with compound-Gaussian clutter. The clutter covariances are assumed to be random and diff...
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ISBN:
(纸本)9781509008636
This paper addresses the problem of adaptive multiple-inputmultiple-output (MIMO) radar detection in heterogeneous environment with compound-Gaussian clutter. The clutter covariances are assumed to be random and different from one transmit/receive pair to another with a priori knowledge about the environment. A two-step strategy is employed to design adaptive detector. Firstly, we obtain the generalized likelihood ratio test (GLRT) detector by assuming the known covariance matrices. Then, we derive the maximum a posteriori (MAP) estimator of the matrices by exploiting the Bayesian technique, and replace the given covariance matrices in the obtained GLRT detector with MAP estimates. Finally, we evaluate the proposed adaptive Bayesian detector via numerical simulations.
With developments in the field of radar and communications, spectrum is a scarce resource nowadays. multiple-inputmultiple-output (MIMO) radars, especially those operating in UHF and HF bands, are increasingly influe...
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ISBN:
(纸本)9781509048281
With developments in the field of radar and communications, spectrum is a scarce resource nowadays. multiple-inputmultiple-output (MIMO) radars, especially those operating in UHF and HF bands, are increasingly influenced by narrow band interferences. Sparse frequency waveform with narrow notches sparsely located among a wide frequency band is a preferred solution to this problem. This paper deals with the orthogonal sparse frequency waveforms design for MIMO radar. The optimal waveforms are determined according to the following criteria: matching the demanded power spectrum density (PSD) and minimizing the cross-correlation energy and off-peak auto-correlation energy. In addition, the constraints of the finite transmitting energy and low peak-to-average power ratio (PAPR) which are highly desirable in practice are also taken into consideration. An efficient two-stage alternating projection algorithm is proposed to solve the optimization problem. The optimal frequency spectrums satisfying the PSD and correlation property (CP) requirements are figured out in Stage 1. And then the optimum waveforms are fitted under the transmitting energy and PAPR constraints in Stage 2. Simulations are performed to verify the effectiveness and superiority of the proposed approach.
The main waveform design features in multiple-inputmultiple-output (MIMO) radars include (a) signal transmission with full rank covariance matrix in order to use the maximum waveform diversity and to suppress more nu...
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The main waveform design features in multiple-inputmultiple-output (MIMO) radars include (a) signal transmission with full rank covariance matrix in order to use the maximum waveform diversity and to suppress more number of interferers, (b) constant envelope in order to have simplicity in deployment and to reduce the destructive effect of nonlinear amplifiers and (c) small side lobe level (SLL) in order to reduce the effect of interferers with unknown location. Therefore, in order to maximize the signal-to-interference-plus-noise ratio (SINR) and to exploit the advantages of MIMO radars, in this paper we have proposed two full rank transmit covariance matrices and maximum achievable SINR is calculated analytically for both. We have shown that the two proposed covariance matrices can be used to generate BPSK waveforms which satisfy constant modulus constraint. Simulation results show that when the angle location of interferences is known, the first proposed matrix achieves a higher level of SINR compared to the second one, while the second proposed matrix has a lower SLL compared with the first one. Also we have shown that the two proposed covariance matrices can handle more interferences compared to phased-array and the recently proposed methods in MIMO radars. (C) 2015 Elsevier B.V. All rights reserved.
Recently, discrete frequency coding waveform with linear frequency modulation (DFCW-LFM) has been proved to have good autocorrelation sidelobe peak and cross ambiguity peak. This paper derives the multiple-input multi...
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ISBN:
(纸本)9781509048281
Recently, discrete frequency coding waveform with linear frequency modulation (DFCW-LFM) has been proved to have good autocorrelation sidelobe peak and cross ambiguity peak. This paper derives the multiple-inputmultiple-output (MIMO) radar ambiguity function (AF) of DFCW-LFM. Based on the AF, a bistatic MIMO high-frequency surface wave radar (HFSWR) system is considered to investigate the resolution performance improvement introduced by waveform diversity and MIMO technique. Simulation results showed that MIMO radar can provide superior location resolution than traditional bistatic HFSWR, and the variation of carrier series of DFCW-LFM influences the sidelobe level of AF.
Distributed multiple-input multiple-output radar has been intensively studied recently and an important issue is to design orthogonal phase coded waveforms. In this paper, we firstly design orthogonal phase coded wave...
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ISBN:
(纸本)9781509048281
Distributed multiple-input multiple-output radar has been intensively studied recently and an important issue is to design orthogonal phase coded waveforms. In this paper, we firstly design orthogonal phase coded waveforms with expanded mainlobe width, and the criterion is to minimize autocorrelation peak sidelobe levels (APSL) and peak cross correlation levels (PCCL) of the orthogonal waveforms, and to match a desirable mainlobe. Then, to further suppress the APSL and PCCL, a method to design mismatched filters is raised, which can be solved by the convex optimization algorithm. Numerical results verify the effectiveness of the proposed method.
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