Name
Title | ||
|---|---|---|
High-Resolution Sparse Representation of Micro-Doppler Signal in Sparse Fractional Domain |
Abstract | ||
|---|---|---|
In order to effectively improve radar detection ability of moving target under the conditions of strong clutter and complex motion characteristics, the principle framework of Short-Time sparse Time-Frequency Distribution (ST-TFD) is established combing the advantages of TFD and sparse representation. Then, Short-Time Sparse FRactional Ambiguity Function (ST-SFRAF) method is proposed and applied to radar micro-Doppler (m-D) detection and extraction. It is verified by real radar data that the proposed methods can achieve high-resolution and low complexity TFD of time-varying signal in time-sparse domain, and has the advantages of good time-frequency resolution, anti-clutter, and so on. It can be expected that the proposed methods can provide a novel solution for time-varying signal analysis and radar moving target detection. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-73447-7_26 | international conference on machine learning |
Field | DocType | Citations |
Ambiguity function,Radar,Radar detection,Signal processing,Pattern recognition,Computer science,Clutter,Sparse approximation,Artificial intelligence,Combing,Doppler effect | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolong Chen | 1 | 99 | 11.22 |
| Xiaohan Yu | 2 | 2 | 7.79 |
| GUAN Jian | 3 | 47 | 15.77 |
| You He | 4 | 72 | 23.11 |