Robust constrained waveform design for MIMO radar with uncertain steering vectors

Yu, Xianxiang; Cui, Guolong; Piezzo, Marco; Iommelli, Salvatore; Kong, Lingjiang

doi:10.1186/s13634-016-0437-9

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…The proposed method starts from introducing the augmented matrix ˆ[ , ] = − A G h and vector [ 1] T T = v u ， so that the cost function mentioned above can be rewritten as…”

Section: B) Proposed Methodsmentioning

confidence: 99%

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An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

Liu

Gao

Zhao

et al. 2020

Preprint

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By utilizing time delay (TD) and angle of arrival (AOA) measurements, three dimensional (3D) target localization based on multi-input and multi-output (MIMO) radar systems is studied in this letter. For such a positioning problem, the typical closed-form solution firstly establishes pseudolinear equation and then applies the weighted least squares (WLS) to determine the final position.However, the WLS solution often suffers performance degradation when the noise is large. To alleviate this problem, the cost function of the WLS is re-represented and a quadratic constraint is imposed such that the expectation of the cost function can attain the minimum value at the true position. Moreover, simulation experiments show that the proposed method performs better than the state-of art algorithms.

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“…The proposed method starts from introducing the augmented matrix ˆ[ , ] = − A G h and vector [ 1] T T = v u ， so that the cost function mentioned above can be rewritten as…”

Section: B) Proposed Methodsmentioning

confidence: 99%

“…Research on multiple-input multiple-output (MIMO) systems has been drawing more and more attention from both the communication and radar communities [1][2][3][4]. Especially, MIMO radar systems with distributed antennas [5] can provide spatial diversity, leading to improve localization and detection performance.…”

Section: Introductionmentioning

confidence: 99%

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

Liu

Gao

Zhao

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…, α 2 v , ξ. Output: s opt and w opt 0: Set n = 0, initialize the transmit waveform s (n) = s 0 (use Equation (13) to calculate the reference waveform S 0 , s 0 = vec(S 0 )) and the receive filter w (n) is calculated by (12). Then, compute SI NR (n) by Equation (10).…”

Section: Algorithm 1 Jd-sfrmentioning

confidence: 99%

“…Waveform design is involved in improving the performance of radar in all aspects and has always been the focus of research [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. The radar waveform design aims to focus the power on the target direction adaptively and to suppress the signal-dependent interferences from different directions in space to improve the output SINR.…”

Section: Introductionmentioning

confidence: 99%

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss

Huang

Deng

Zheng

et al. 2021

Sensors

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In this paper, we aim at the problem that MIMO radar’s target detection performance is greatly reduced in the complex multi-signal-dependent interferences environment. We propose a joint design method based on semidefinite relaxation (SDR), fractional programming and randomization technique (JD-SFR) and a joint design method based on coordinate descent (JD-CD) to solve the actual transmit waveform and receive filter bank directly to reduce the loss of strong interference to the output signal-to-interference-plus-noise ratio (SINR) of the radar system. Therefore, the maximization of output SINR is taken as the criterion of the optimization problem. The designed waveforms take into account the radar transmitter’s hardware requirements for constant envelope waveforms and impose similarity constraints on the waveforms. JD-SFR uses SDR, fractional programming and randomization technique to deal with the non-convex optimization problems encountered in the solution process. JD-CD transforms the optimization problem into a function of the phase of the waveform and then solves the transmit waveform based on CD. Compared with other methods, the proposed method has lower output SINR loss under strong power interference and forms deep nulls on the direction beampattern of multiple interference sources, which indicates that it has better anti-interference performance.

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“…Since the concept of multiple-input multiple-output (MIMO) radar was first proposed in [1], it has been receiving considerable attention for its superiority in spatial diversity and available degrees of freedom (DoFs) compared with its single-input multiple-output (SIMO) counterpart [1][2][3][4]. More recently, the researches on the airborne MIMO radar have been extended to the spacetime adaptive processing (STAP) [5][6][7][8], which is an important technique in airborne radars due to the remarkable ability of clutter suppression that facilitates the downstream signal processing tasks suffering from high clutter rates such as multi-target detecting and tracking [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar

Zhou

Chen

et al. 2019

EURASIP J. Adv. Signal Process.

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In general, the space-time adaptive processing (STAP) can achieve excellent clutter suppression and moving target detection performance in the airborne multiple-input multiple-output (MIMO) radar for the increasing system degrees of freedom (DoFs). However, the performance improvement is accompanied by a dramatic increase in computational cost and training sample requirement. As one of the most efficient dimension-reduced STAP methods, the extended factored approach (EFA) transforms the full-dimension STAP problem into several small-scale adaptive processing problems, and therefore alleviates the computational cost and training sample requirement. However, it cannot effectively work in the airborne MIMO radar since sufficient training samples are unavailable. Aiming at the problem, a fast iterative method using persymmetry covariance matrix estimation in the airborne MIMO radar is proposed. In this method, the clutter covariance matrix is estimated by the original data and the constructed data. Then, the spatial weight vector in EFA is decomposed into the Kronecker product of two short-weight vectors. The bi-iterative algorithm is exploited to obtain the desired weight vectors. Simulation results demonstrate the effectiveness of our proposed method.

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Robust constrained waveform design for MIMO radar with uncertain steering vectors

Cited by 12 publications

References 29 publications

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss

A fast STAP method using persymmetry covariance matrix estimation for clutter suppression in airborne MIMO radar

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