MIMO Radar Waveform Design With PAPR and Similarity Constraints

Cheng, Ziyang; He, Zishu; Liao, Bin; Fang, Min

doi:10.1109/tsp.2017.2780052

Cited by 140 publications

(57 citation statements)

References 47 publications

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“…It is worth noting that the envelope constraint on the transmitted waveform was not considered in any of those studies, which made it difficult to meet the hardware constraints and maximize the power efficiency [13]. For this reason, unimodular or low peak-to-average power ratio (PAR) waveform is always applied in radar systems [13,14]. Nevertheless, unimodular waveform may lead to the degradation of waveform performance [15].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

Hao

Gong

2019

Entropy

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This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization–maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform.

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Section: Introductionmentioning

confidence: 99%

“…Nevertheless, unimodular waveform may lead to the degradation of waveform performance [15]. To tackle this problem, some researchers had used a more general low-PAR constraint to replace the unimodular constraint to further improve the waveform performance [6,7,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

Hao

Gong

2019

Entropy

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“…More complicated radar models can be used, and some robust estimates, based on geometric considerations as well as statistical properties of covariance matrix can be used to improve the performance of the classic sample covariance [21][22][23]. In our MIMO radar systems, the waveforms should be designed with the low auto-correlation and cross-correlation sidelobes under the CM or lower PAPR constraint [24][25][26]. Here, we focus on designing a directional beampattern, our desired spatial covariance matrix of the DFRC transmitted signals is expressed as…”

Section: B Mimo Radar Modelmentioning

confidence: 99%

MIMO Dual-Functional Radar-Communication Waveform Design With Peak Average Power Ratio Constraint

et al. 2021

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“…Also, N in (19) can be interpreted and chosen accordingly as an approximation of the number of antennas one can afford to use. Let J 2 (p, R, α) J(p, R, α) + ρ (| p 1 − N |) denote the augmented objective function in (19). Then, with a slight Step 0: Initialize the antenna position vector p (0) = 1 M , the complex covariance matrix R (0) ∈ C N ×N , and the scaling factor α (0) ∈ R + , and the outer loop index t = 1.…”

Section: Extension To General Antenna Selection Scenariomentioning

confidence: 99%

Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar

Bose

Khobahi

Soltanalian

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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Controlling the radar beam-pattern by optimizing the transmit covariance matrix is a well-established approach for performance enhancement in multiple-input-multiple-output (MIMO) radars. In this paper, we investigate the joint optimization of the waveform covariance matrix and the antenna position vector for a MIMO radar system to approximate a given transmit beam-pattern, as well as to minimize the cross-correlation of the received waveforms reflected back from the targets. We formulate this design task as a non-convex optimization problem and then propose a cyclic optimization approach to efficiently approximate its solution. We further propose a local binary search algorithm in order to efficiently design the corresponding antenna positions. We show that the proposed method can be extended to the more general case of approximating the given beam-pattern using a minimal number of antennas as well as optimizing their positions. Our numerical investigations demonstrate a great performance both in terms of accuracy and computational complexity, making the proposed framework a good candidate for usage in real-time radar waveform processing applications such as MIMO radar transmit beamforming for aerial drones that are in motion.

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MIMO Radar Waveform Design With PAPR and Similarity Constraints

Cited by 140 publications

References 47 publications

Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

MIMO Dual-Functional Radar-Communication Waveform Design With Peak Average Power Ratio Constraint

Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar

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