Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163763
Title: An efficient track-before-detect for multi-PRF radars with range and doppler ambiguities
Authors: Li, Wujun
Yi, Wei
Kong, Lingjiang
Teh, Kah Chan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Li, W., Yi, W., Kong, L. & Teh, K. C. (2022). An efficient track-before-detect for multi-PRF radars with range and doppler ambiguities. IEEE Transactions On Aerospace and Electronic Systems, 58(5), 4083-4100. https://dx.doi.org/10.1109/TAES.2022.3158633
Journal: IEEE Transactions on Aerospace and Electronic Systems
Abstract: This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-Accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity.
URI: https://hdl.handle.net/10356/163763
ISSN: 0018-9251
DOI: 10.1109/TAES.2022.3158633
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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