High Resolution Ranging Method Based on Low-Rate Parallel Random Sampling

Lin Jie; Shi Guangming; Chen Xuyang; Qi Fei; Zhang Li

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High Resolution Ranging Method Based on Low-Rate Parallel Random Sampling

Lin Jie #,Shi Guangming *,Chen Xuyang,Qi Fei,Zhang Li

Key Lab. of IPIU of Ministry of Education, Xidian University

*Correspondence author

#Submitted by

Subject:

Funding: NSF of China（No.No.60776795, 60902031）, Research Fund for the Doctoral Program of Higher Education of China (RFDP)（No.No.200807010004）, Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) （No.2007070102）

Opened online:31 May 2010

Accepted by: none

Citation: Lin Jie,Shi Guangming,Chen Xuyang.High Resolution Ranging Method Based on Low-Rate Parallel Random Sampling[OL]. [31 May 2010] http://en.paper.edu.cn/en_releasepaper/content/4374120

In this paper, we propose a low-rate high-resolution ranging method for UWB (up to several GHz of sampling rate) ranging system. It exploits compressed sensing (CS) theory and a parallel sampling ADCs structure based on random projection (PSRP). To guarantee the effective application of CS on the received signal, we construct a dictionary in which the atoms are time-shifted versions of the transmitted signal. Hence the received signal can be low-rate sampled by PSRP. For an UWB ranging system using PSRP instead of the newly pro- posed analog-to-information converter, it possesses the universality of dictionary atoms, lower sampling rate and better performance for noisy signal. Addition- ally, since the dictionary size in this work can be adjusted flexibly, a desired high resolution can be achieved. The simulation results confirm these advantages via a noisy received signal (SNR=16dB) which contains five target echoes. Though the received signal is sampled at less 10% of Nyquist rate, the probability of echo detection is over 95% and the distance resolution reaches the optimal of the conventional ranging method.

Keywords:High resolution;ranging method;low-rate random sampling, compressed sensing;sparse representation

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