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Waveform Optimization of Compressed Sensing Radar without Signal Recovery

by 1 and 2,*
1
School of Information Engineering, Lingnan Normal University, Zhanjiang 524000, China
2
HuaWei Technologies CO., LTD., Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(9), 271; https://doi.org/10.3390/info10090271
Received: 19 July 2019 / Revised: 25 August 2019 / Accepted: 25 August 2019 / Published: 29 August 2019
(This article belongs to the Section Information Processes)
Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation. View Full-Text
Keywords: compressed sensing radar; waveform optimization; compressive signal processing; transmit waveform compressed sensing radar; waveform optimization; compressive signal processing; transmit waveform
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Wang, Q.; Sun, Y. Waveform Optimization of Compressed Sensing Radar without Signal Recovery. Information 2019, 10, 271.

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