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Entropy 2019, 21(3), 261; https://doi.org/10.3390/e21030261

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

National University of Defense Technology, Hefei 230037, China
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Received: 29 December 2018 / Revised: 14 February 2019 / Accepted: 5 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue Information Theory Applications in Signal Processing)
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Abstract

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. View Full-Text
Keywords: waveform design; mutual information (MI); peak-to-average power ratio; minorization–maximization (MM) method; cognitive radar waveform design; mutual information (MI); peak-to-average power ratio; minorization–maximization (MM) method; cognitive radar
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Hao, T.; Cui, C.; Gong, Y. Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar. Entropy 2019, 21, 261.

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