Waveform Optimization for MIMO Radar in Colored Noise: Further Results for Estimation-Oriented Criteria

Tang, Bo; Tang, Jun; Peng, Yingning

doi:10.1109/tsp.2011.2177262

Cited by 35 publications

(62 citation statements)

References 22 publications

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“…The solutions to Eqs. (19), (20), (24), and (25) can be acquired very effectively by the CVX optimization toolbox in Ref. 32.…”

Section: Diagonal Loading-based Iterative Methodsmentioning

confidence: 99%

“…Extensive researches have illustrated that, by designing the transmitted waveforms elaborately, MIMO radar can significantly outperform its phased radar counterpart such as improved spatial resolution, parameter identifiability, be optimized, the existing design methods can be classified as the following two categories: (1) only the transmitter to be designed [6][7][8][9][10][11][12][13][14] and (2) the transmitter and receiver to be designed jointly. [15][16][17][18][19][20][21][22][23] Specific issues that have been considered in first category include the transmit beam pattern design and radar ambiguity function design. In Refs.…”

Section: Introductionmentioning

confidence: 99%

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Robust design of multiple-input multiple-output radar waveform covariance matrix in the presence of clutter

Guo

Wang

2016

J. Appl. Remote Sens

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Abstract. In this work, the issue of robust waveform optimization is addressed in the presence of clutter to improve the worst-case estimation accuracy for collocated multiple-input multipleoutput (MIMO) radar. Robust design is necessary due to the fact that waveform design may be sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, the robust waveform covariance matrix design is formulated here on the basis of Cramér-Rao Bound to ease this sensitivity systematically for improving the worst-case accuracy. To tackle the resultant complicated and nonlinear problem, a new diagonal loading (DL)-based iterative approach is developed, in which the inner optimization problem can first be decomposed to some independent subproblems by using the Hadamard's inequality, and then these subproblems can be reformulated into convex issues by using DL method, as well as the outer optimization problem can also be relaxed to a convex issue by translating the nonlinear function into a linear one, and, hence, both of them can be solved very effectively. An optimal solution to the original problem can be obtained via the least-squares fitting of the solution acquired by the iterative approach. Numerical simulations show the efficiency of the proposed method. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…The solutions to Eqs. (19), (20), (24), and (25) can be acquired very effectively by the CVX optimization toolbox in Ref. 32.…”

Section: Diagonal Loading-based Iterative Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust design of multiple-input multiple-output radar waveform covariance matrix in the presence of clutter

Guo

Wang

2016

J. Appl. Remote Sens

View full text Add to dashboard Cite

show abstract

“…guarantee nondecreasing SINR in each iteration step. In order to guarantee convergence, a new iterative algorithm was derived in [9].The mutual information between the received waveforms and the target radar signatures was employed to design the transmit waveform for extended target [10]. In [11], MIMO waveform was devised by minimizing the estimation error of the minimum mean squared error (MMSE).…”

Section: Introductionmentioning

confidence: 99%

Robust MIMO Radar Waveform Design with Imperfect Prior Knowledge of Targets

Liu¹,

Zhu²,

Wang³

2017

IJSIP

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Waveform design for multi-input multi-output (MIMO) radar, which usually relies on the initial parameter estimates (i.e., some prior information on the target of interest and scenario), is often sensitive to estimation errors in these parameters. To systematically alleviate this sensitivity, robust waveform design has been studied in recent years by explicitly incorporating a parameter uncertainty model into the optimization problem. In this paper, the robust MIMO radar waveform optimization is investigated to improve the worst-case performance of parameter estimation over a convex uncertainty model, which is based on the Cramer-Rao bound (CRB). To solve the resultant complicated nonlinear optimization problem, an iterative algorithm is proposed to optimize the waveform covariance matrix (WCM) such that the worst-case performance can be improved. Each iteration step in the proposed

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“…Multi-input multi-output (MIMO) radar has been intensively researched recently [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. MIMO radar is defined as a radar system with multiple antennas to simultaneously transmit arbitrary waveforms other than coherent waveforms in traditional phased-array radars, which is the so-called waveform diversity [1].…”

Section: Introductionmentioning

confidence: 99%

MIMO Radar Waveform Design with Prior Information of Targets in the Presence of Signal-Dependent Noise

Wang¹

2016

IJSIP

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Waveform Optimization for MIMO Radar in Colored Noise: Further Results for Estimation-Oriented Criteria

Cited by 35 publications

References 22 publications

Robust design of multiple-input multiple-output radar waveform covariance matrix in the presence of clutter

Robust design of multiple-input multiple-output radar waveform covariance matrix in the presence of clutter

Robust MIMO Radar Waveform Design with Imperfect Prior Knowledge of Targets

MIMO Radar Waveform Design with Prior Information of Targets in the Presence of Signal-Dependent Noise

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