Title
MI-Based Robust Waveform Design in Radar and Jammer Games.
Abstract
Due to the uncertainties of the radar target prior information in the actual scene, the waveform designed based on the radar target prior information cannot meet the needs of parameter estimation. To improve the performance of parameter estimation, a novel transmitted waveform design method under the hierarchical game model of radar and jammer, which maximizes the mutual information (MI) between the radar target echo and the random target spectrum response, is proposed. In the hierarchical game model of radar and jammer, the radar is in a leading position while the jammer is in a following position. The strategy of the jammer is optimized based on the radar transmitted waveform of previous moment, then the radar selects its own strategy based on the strategy of the jammer. It is generally assumed that the radar and the jammer have intercepted the real target spectrum and then the optimal jamming and the optimal transmitted waveform spectrum are obtained. However, the exact characteristic of the real target spectrum is hard to capture accurately in actual scenes. To simulate this, the real target spectrum is considered to be within an uncertainty range which is confined by known upper and lower bounds. Then, the minimax robust jamming and the maximin robust transmitted waveform are designed successively based on the MI criteria, which optimizes the performance under the most unfavorable condition of the radar and the jammer, respectively. Simulation results demonstrate that the robust transmitted waveform design method guarantees the parameter estimation performance effectively and provides useful guidance for waveform energy allocation.
Year
DOI
Venue
2019
10.1155/2019/4057849
COMPLEXITY
Field
DocType
Volume
Radar,Minimax,Upper and lower bounds,Control theory,Waveform,Mutual information,Estimation theory,Jamming,Energy allocation,Mathematics
Journal
2019
ISSN
Citations 
PageRank 
1076-2787
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Bin Wang100.34
Chen Xu28917.69
Fengming Xin312.38
Xin Song4156.88