Target Estimation Using Sparse Modeling for Distributed MIMO Radar

Gogineni, Sandeep; Nehorai, Arye

doi:10.1109/tsp.2011.2164070

Cited by 178 publications

(132 citation statements)

References 40 publications

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“…Due to the complexity of evaluating the CRLB under sparse models, the power allocation problem is treated differently in this context. Examples of such studies include [8], [13]. In [8] widely separated MIMO radars are considered, and an adaptive power allocation scheme is proposed.…”

Section: Introductionmentioning

confidence: 99%

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An approach to power allocation in MIMO radar with sparse modeling for coherence minimization

Ajorloo

Amini

Bastani

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we aim at improving the estimation performance of the direction of arrival (DOA) in a colocated MIMO radar through power allocation under the sparsity constraints. Specifically, by considering the sparse recovery techniques, we try to minimize the coherence of associated sensing matrix by optimally distributing the power among transmit antennas. To determine the optimal power distribution, we reformulate the coherence minimization problem and derive a convex optimization constrained by the total power budget. This helps us to efficiently evaluate and simulate the optimal power distribution policy. Simulation results confirm superiority of the proposed method compared to the existing techniques.

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Section: Introductionmentioning

confidence: 99%

“…Examples of such studies include [8], [13]. In [8] widely separated MIMO radars are considered, and an adaptive power allocation scheme is proposed. In this method, after obtaining an estimate of the targets, the powers of the next set of transmitting pulses are determined so as to maximize the minimum target return.…”

Section: Introductionmentioning

confidence: 99%

An approach to power allocation in MIMO radar with sparse modeling for coherence minimization

Ajorloo

Amini

Bastani

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…CS techniques offer a framework for the detection and allocation of sparse signals for radar with a reduced number of samples [8,9]. The application of compressive sensing to MIMO radar system was investigated in [10][11][12]. The problem discussed in [10] is of the targets angular separation and reduction of the physical array elements required for the system.…”

Section: Introductionmentioning

confidence: 99%

“…The transmitted waveforms in MIMO radar are known at each receive antennas, so that each receive antenna can construct the basis matrix locally, without the knowledge of the received signal at other antennas. In [12], CS approach to accurately estimate properties (position, velocity) of multiple targets was exploited for MIMO radar. The sampled outputs of the matched filter at the receivers are used to estimate the positions and velocities of multiple targets using MIMO radar systems with widely separated antennas by employing sparse modeling and CS.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the scenario considered in [10][11][12], this paper capitalizes on chaotic coded waveform to construct measurement operator at the transmitter and presents a new method of how the influence of deviation and noise in the MIMO radar can be reduced through the use of CS. Further, we provide SVD method and interpolation method for the noise suppression and robustness respectively for the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Identifiability of Monostatic Mimo Chaotic Radar Using Compressed Sensing

Yang¹,

Zhang²

2012

PIER B

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Abstract-Compressed sensing (CS) has attracted significant attention in the radar community. The better understanding of CS theory has led to substantial improvements over existing methods in CS radar. But there are also some challenges that should be resolved in order to benefit the most from CS radar, such as radar signal with low signal to noise ratio (Low-SNR). In this paper, we will focuses on monostatic chaotic multiple-inputmultiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsityexploiting algorithms for the parameter estimation of targets at Low-SNR. The novelty of this paper is that it capitalizes on chaotic coded waveform to construct measurement operator incoherent with noise and singular value decomposition (SVD) to suppress noise. In order to improve the robustness of azimuth estimation interpolation method is applied to construction of sparse bases. The gradient pursuit (GP) algorithm for reconstruction is implemented at Low-SNR. Finally, the conclusions are all demonstrated by simulation experiments.

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Coherent Mimo Radar and Waveform Diversity

Gianelli

Stoica

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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Multiple‐input multiple‐output (MIMO) radar technology has gained considerable attention, from both theorists and practitioners, in the past decade due to its capability to expand radar system functionality and open previously unexplored design space. This work seeks to provide a basic understanding of coherent MIMO radar technology, the performance enhancements that can be attained by using the technology, and the associated drawbacks involved in operating a coherent MIMO radar. Attention is paid to achieve the required waveform orthogonality for a MIMO radar system, and several operating concepts are presented and described. Radar basics are discussed first, followed by a description of the MIMO radar concept. Examples of radar applications that may benefit from the application of MIMO radar are presented. Methods for achieving waveform orthogonality in practical MIMO radar systems are described, and the performance tradeoffs involved in implementing these operation modes are discussed. Information on general radar waveforms, and their suitability for use in a MIMO radar system, is provided. Two examples of MIMO radar systems are discussed, with special emphasis placed on the benefit obtained by applying MIMO radar technology.

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Target Estimation Using Sparse Modeling for Distributed MIMO Radar

Cited by 178 publications

References 40 publications

An approach to power allocation in MIMO radar with sparse modeling for coherence minimization

An approach to power allocation in MIMO radar with sparse modeling for coherence minimization

Parameter Identifiability of Monostatic Mimo Chaotic Radar Using Compressed Sensing

Coherent Mimo Radar and Waveform Diversity

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