Author: Kim, Jin Won; Mehta, Prashant G.
Title: Feedback Particle Filter for Collective Inference Cord-id: 4b1vg6jt Document date: 2020_10_13
ID: 4b1vg6jt
Snippet: The purpose of this paper is to describe the feedback particle filter algorithm for problems where there are a large number ($M$) of non-interacting agents (targets) with a large number ($M$) of non-agent specific observations (measurements) that originate from these agents. In its basic form, the problem is characterized by data association uncertainty whereby the association between the observations and agents must be deduced in addition to the agent state. In this paper, the large-$M$ limit i
Document: The purpose of this paper is to describe the feedback particle filter algorithm for problems where there are a large number ($M$) of non-interacting agents (targets) with a large number ($M$) of non-agent specific observations (measurements) that originate from these agents. In its basic form, the problem is characterized by data association uncertainty whereby the association between the observations and agents must be deduced in addition to the agent state. In this paper, the large-$M$ limit is interpreted as a problem of collective filtering. This viewpoint is used to derive the equation for the empirical distribution of the hidden agent states. A feedback particle filter algorithm for this problem is presented and illustrated via numerical simulations. The simulations help show that the algorithm well approximates the empirical distribution of the hidden states for large $M$.
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