Abstract. The extended target is characterized by common centroid kinematic state and extended information, extended forms not only can be treated as a state to be estimated separately, including the size, shape and direction information will effectively enhance the performance of filter with proper use. For this reason, a new algorithm of the extended target particle probability hypothesis density filter modeling for Star-Convex is proposed, the algorithm take local clustering trend analysis into account and propose a method of extended target track initiation based on Star-Convex gate, then, according to the different characteristics of measurement sets, we propose an adaptive measurement partition algorithm based on extended information of Star-Convex. Simulation results show that the false initiation and computational cost both reduce significantly. In the intersection or the neighbor target tracking scenario, the proposed partition algorithm can maintain a better performance and improve the stability of the filter.
In the actual detection, due to bandwidth, channel fading and channel noise, the ideal channel isn't assured from the local detector to fusion center, so the optimal detection arithmetic is accordingly rectified. In this paper, we study the global optimal detection algorithm based on the two kinds of non-ideal channel schemes, firstly, channel state information is known; secondly, channel statistics characteristics is known. The configuration of the system is mixed with parallel and serial structure. The decision rules of the every local detector and every local processor must be optimized jointly based on the minimum error probability. At last the stimulation favors the analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.