An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 1; p. 117
• Main Authors: Wang, Liu, Chen, Guifen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.12.2023; MDPI
• Subjects: Algorithms; Communication; Convex analysis; DG-CPHD; distributed fusion; Efficiency; Hypotheses; Mathematical functions; Methods; Optimization; parallel inverse covariance intersection; Sensors; Technology application; wave filter; DG-CPHD; wave filter; parallel inverse covariance intersection; distributed fusion
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24010117

A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.