Sparsity-Based Direct Location Estimation Based on Two-step Dictionary Learning

Tingting Wang, Wei Ke, Gang Liu

The Jiangsu Engineering Center of Meteorological Sensor Network Technology, Nanjing University of Information Science and Technology, Nanjing, China The Jiangsu Key Laboratory on Optoelectronic Technology, School of Physics and Technology, Nanjing Norm University, Nanjing, China Key Labortory of Disaster Reduction and Emergency Rseponse Engineering of the Ministry of Civil Affairs, Beijing, Chinaa.
The Jiangsu Key Laboratory on Optoelectronic Technology, School of Physics and Technology, Nanjing Norm University, Nanjing, China.
The Jiangsu Key Laboratory on Optoelectronic Technology, School of Physics and Technology, Nanjing Norm University, Nanjing, China Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs, Beijing, China.

DOI: 10.4236/cn.2013.53B2077                

Abstract

This paper proposes an adaptive sparsity-based direct position determination (DPD) appoach to locate multiple targets in the case of time-varying channels. The novel feature of this method is to dynamically adjust both the overcomplete basis and the sparse solution based on a two-step dictionary learning (DL) framework. The method first performs supervised offline DL by using the quadratic programming approach, and then the dictionary is continuously updated in an incremental fashion to adapt to the time-varying channel during the online stage. Furthermore, the method does not need the number of emitters a prior. Simulation results demonstrate the performance of the proposed algorithm on the location estimation accuracy.

                                                                         

Keywords

Dictionary Learning; Compressive Sensing; Direct Location; Time-Varying Channel; Quadratic Programming

Conflicts of Interest

The authors declare no conflicts of interest.

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