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Radar Imaging with Quantized Measurements Based on Compressed Sensing
Dong, Xiao; Zhang, Yunhua
Source Publication2015 Sensor Signal Processing for Defence, SSPD 2015
AbstractIn this paper, we consider the problem of radar imaging with quantized data. The quantized CS (QCS) method is used to reconstruct the radar image of sparse targets from quantized data. The reconstruction problem is derived in the maximum a posteriori (MAP) estimation framework and formulated as a convex optimization problem. We compare the proposed method with the traditional l1-regularization method using 1-D simulated data with different quantization bits. For coarse quantization with 1 or 2 bits, the simulation results show that the QCS method outperforms the l1- regularization method in high SNR situations. For high- resolution quantization with more bits, we derive the conditions under which the l1-regularization method and the QCS method are equivalent. This statement is explained theoretically and confirmed by simulation results. © 2015 IEEE.
Conference Name5th Sensor Signal Processing for Defence, SSPD 2015
Conference DateSeptember 9, 2015 - September 10, 2015
Conference PlaceEdinburgh, United kingdom
Indexed ByEI ; CPCI
Document Type会议论文
Recommended Citation
GB/T 7714
Dong, Xiao,Zhang, Yunhua. Radar Imaging with Quantized Measurements Based on Compressed Sensing[C],2015.
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