Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery
-
Published:2024-06-23
Issue:13
Volume:16
Page:2294
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Cui Kai1ORCID, Wang Changlong1, Zhou Feng1, Liu Chunheng1, Gao Yongchan1, Feng Weike2ORCID
Affiliation:
1. Key Laboratory of Electronic Information Countermeasure and Simulation Technology, Ministry of Education, Xidian University, Xi’an 710071, China 2. Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China
Abstract
In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.
Funder
National Natural Science Foundation of China
Reference47 articles.
1. Zheng, H., Wang, J., Jiang, S., Wu, X., and Gu, X. (2017). Passive Bistatic Radar, National Defense Industry Press. [1st ed.]. 2. Liu, R., Dai, W., and Zhang, C. (April, January 29). Multi-target Detection by Distributed Passive Radar Systems without Reference Signals. Proceedings of the 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China. 3. Li, J.C., Zhang, Y.D., Zhang, Y.K., and Li, X.D. (2013, January 5–8). Direct Path Wave Purification for Passive Radar with Normalized Least Mean Square Algorithm. Proceedings of the 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013), Kunming, China. 4. Clutter Cancellation Along the Clutter Ridge for Airborne Passive Radar;Yang;IEEE Geosci. Remote Sens. Lett.,2017 5. Sui, J., Wang, J., Gao, J., and Fan, X. (2021, January 15–19). A Novel Strong Clutter Suppression Algorithm for Airborne Passive Radar. Proceedings of the 2021 CIE International Conference on Radar (Radar), Haikou, China.
|
|