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

Abstract

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.

The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

“…Commonly used fusion strategies include the generalized covariance intersection (GICI), sequential inverse covariance intersection (SICI), parallel inverse covariance intersection (PICI), etc. ; Wang, L. (2023) [ 16 , 17 ] proposed a parallel inverse covariance intersection Gaussian mixture cardinality probability hypothesis density (PICI-GM-CPHD) fusion strategy in their research, which utilizes the generalization ability of the PICI-GM-CPHD algorithm to effectively reduce the nonlinear complexity of the system. Liu, Y.…”

A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking

“…The theory of RFS provides a unified and comprehensive theoretical framework for multi-target tracking problems in complex and variable monitoring environments and is widely applied in the distributed fusion of multiple sensors [6][7][8][9][10][11][12][13][14][15]. The Cardinalized Probability Hypothesis Density (CPHD) filter in random finite set theory is widely used due to its low computational cost and ability to avoid inconsistent label spaces [16][17][18][19][20].…”