{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:19:27Z","timestamp":1780323567760,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276204"],"award-info":[{"award-number":["62276204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JM-340"],"award-info":[{"award-number":["2022JM-340"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023-JC-QN-0710"],"award-info":[{"award-number":["2023-JC-QN-0710"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020T130494"],"award-info":[{"award-number":["2020T130494"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018M633470"],"award-info":[{"award-number":["2018M633470"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["62276204"],"award-info":[{"award-number":["62276204"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2022JM-340"],"award-info":[{"award-number":["2022JM-340"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2023-JC-QN-0710"],"award-info":[{"award-number":["2023-JC-QN-0710"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2020T130494"],"award-info":[{"award-number":["2020T130494"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2018M633470"],"award-info":[{"award-number":["2018M633470"]}]},{"name":"China Postdoctoral Science Foundation","award":["62276204"],"award-info":[{"award-number":["62276204"]}]},{"name":"China Postdoctoral Science Foundation","award":["2022JM-340"],"award-info":[{"award-number":["2022JM-340"]}]},{"name":"China Postdoctoral Science Foundation","award":["2023-JC-QN-0710"],"award-info":[{"award-number":["2023-JC-QN-0710"]}]},{"name":"China Postdoctoral Science Foundation","award":["2020T130494"],"award-info":[{"award-number":["2020T130494"]}]},{"name":"China Postdoctoral Science Foundation","award":["2018M633470"],"award-info":[{"award-number":["2018M633470"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors\u2019 measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distributed deep neural network (DNN) track fusion, named the MAMPS-MAF-DNN algorithm. Firstly, to reduce the impact of the dense group clutter, a clutter pre-processing algorithm is elaborated, which combines the advantages of the CFDP (cluster by finding density peaks) and double threshold screening algorithms. Then, for the single-active and multi-passive sensor (SAMPS) system, a centralized MAF algorithm based on angle information is developed, called the SAMPS-MAF algorithm. Finally, the SAMPS-MAF algorithm is extended to the MAMPS system within the DNN framework, and the complete MAMPS-MAF-DNN algorithm is proposed. Experimental results indicate that, compared to the existing MAF and covariance intersection (CI) fusion algorithms, the proposed MAMPS-MAF-DNN algorithm can fully combine the advantages of multi-active and multi-passive sensors, efficiently reduce the computational complexity, and obviously improve the tracking accuracy.<\/jats:p>","DOI":"10.3390\/rs16224120","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T06:30:48Z","timestamp":1730788248000},"page":"4120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments"],"prefix":"10.3390","volume":"16","author":[{"given":"Yongquan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, P.O. BOX 133, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, P.O. BOX 133, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, P.O. BOX 133, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aomen","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, P.O. BOX 133, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhibin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, P.O. BOX 133, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Intelligent Spectrum Sensing and Information Fusion, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1109\/TSP.2020.3009836","article-title":"Radar adaptive detection architectures for heterogeneous environments","volume":"68","author":"Liu","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, W., Li, G., Chen, Q., Ju, M., and Qu, J. (2021). CF2PN: A cross-scale feature fusion pyramid network based remote sensing target detection. Remote Sens., 13.","DOI":"10.3390\/rs13050847"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, B., Gan, Z., Chen, D., and Aleksandrovich, D.S. (2020). 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