Paper Info
Title
Waveform Optimization Of Compressed Sensing Radar Without Signal Recovery
Abstract
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.
Year
DOI
Venue
2019
10.3390/info10090271
INFORMATION
Keywords
Field
DocType
compressed sensing radar, waveform optimization, compressive signal processing, transmit waveform
Radar,Data mining,Signal processing,Inference,Computer science,Waveform,Algorithm,Filter (signal processing),Interference (wave propagation),Compressed sensing,Computational complexity theory
Journal
Volume
Issue
Citations 
10
9
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Quanhui Wang101.01
Ying Sun200.34