Through‐the‐wall radar imaging algorithm for moving target under wall parameter uncertainties. Issue 11 (30th July 2019)

• Record Type: Journal Article
• Authors: Sun, Yanpeng; Chen, Li; Qu, Lele
• Is Part Of: IET image processing. Volume 13:Issue 11(2019)
• Page Start: 1903
• Page End: 1908
• Publication Date: 2019-07-30
• Subjects: Newton method -- particle swarm optimisation -- gradient methods -- radar imaging -- iterative methods -- object detection -- image motion analysis -- image reconstruction; data analysis -- through‐the‐wall radar imaging algorithm -- traditional BFGS algorithm -- stationary target -- block orthogonal matching pursuit algorithm -- particle swarm algorithm -- quasiNewton algorithm -- slow calculation speed -- LBFGS‐PSO algorithm model -- improved limited Broyden–Fletcher–Goldfarb–Shanno‐particle swarm optimisation algorithm -- wall parameter fuzziness -- wall parameters -- poor reconstruction accuracy -- slow imaging speed -- wall parameter uncertainties -- moving target; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5670
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In order to solve the problems of slow imaging speed and poor reconstruction accuracy of wall parameters under the condition of wall parameter fuzziness, an improved limited Broyden–Fletcher–Goldfarb–Shanno‐particle swarm optimisation (LBFGS‐PSO) algorithm was proposed. The LBFGS‐PSO algorithm model solves the problems of slow calculation speed and large errors of the traditional quasi‐Newton algorithm and particle swarm algorithm. The algorithm combined with block orthogonal matching pursuit algorithm can not only accurately reconstruct the position of the sidewall, but also can use the multi‐path information to accurately reconstruct the moving target and the stationary target. Compared with the traditional BFGS algorithm and PSO algorithm, the proposed algorithm can reduce the calculation time and provide more accurate estimation results. Simulation results and data analysis verify the performance of the proposed algorithm.