Research Article
Research on GPU Parallel Acceleration of Efficient Coherent Integration Processor for Passive Radar
@INPROCEEDINGS{10.1007/978-3-030-90196-7_35, author={Zirong Bu and Lijun Wang and Huijie Zhu}, title={Research on GPU Parallel Acceleration of Efficient Coherent Integration Processor for Passive Radar}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={Passive radar Range migration Graphics processing unit (GPU)}, doi={10.1007/978-3-030-90196-7_35} }- Zirong Bu
Lijun Wang
Huijie Zhu
Year: 2021
Research on GPU Parallel Acceleration of Efficient Coherent Integration Processor for Passive Radar
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_35
Abstract
The traditional algorithm of cross-ambiguity function requires a large amount of computation and storage capacity, which brings difficulties to real-time processing. In addition, the long-integration time will cause range migration problems, resulting in a decrease in the SNR, and reducing the weak target detection ability of the system. Based on the characteristics of the passive radar, this paper adopts the method of combining segmental frequency domain pulse compression and Keystone transform to correct the range migration in the target detection, which improves the detection ability of weak targets. However, due to the relatively large amount of data and computation of the algorithm, this paper takes the advantages of graphics processing unit (GPU) with large data throughput and strong floating-point computing capabilities to propose an efficient coherent integration method which is suitable for GPU parallel processing.
