{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:05:13Z","timestamp":1760231113960,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foreign Scholars in University Research and Teaching Programs","award":["B18039"],"award-info":[{"award-number":["B18039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared to the Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, the proposed algorithm improves the tracking accuracy of multiple weak targets without pruning operation to preserve the relevant association information. The superior performance in tracking accuracy of the algorithm is validated for simulated scenarios using OSPA(2) metric. More importantly, the simulation results demonstrate that the proposed algorithm outputs both the Gibbs sampling-based version and Murty algorithm-based version in computational cost due to linear complex in the number of both Bernoulli components and measurements.<\/jats:p>","DOI":"10.3390\/rs14174209","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief Propagation"],"prefix":"10.3390","volume":"14","author":[{"given":"Chenghu","family":"Cao","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6453-0786","authenticated-orcid":false,"given":"Yongbo","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1049\/iet-rsn.2017.0102","article-title":"Efficient joint probabilistic data association filter based on Kullback-Leibler divergence for multi-target tracking","volume":"11","author":"Zhu","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MAES.2004.1263228","article-title":"Multiple hypothesis tracking for multiple target tracking","volume":"19","author":"Blackman","year":"2004","journal-title":"IEEE Trans. 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