A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks

K. Punithakumar; T. Kirubarajan; A. Sinha

doi:10.1117/12.667772

17 May 2006 A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks

K. Punithakumar, T. Kirubarajan, A. Sinha

Author Affiliations +

Proceedings Volume 6235, Signal Processing, Sensor Fusion, and Target Recognition XV; 62350L (2006) https://doi.org/10.1117/12.667772
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

This paper presents a Sequential Monte Carlo (SMC) Probability Hypothesis Density (PHD) algorithm for decentralized state estimation from multiple platforms. The proposed algorithm addresses the problem of communicating and fusing track information from a team of multiple sensing platforms detecting and tracking multiple targets in the surveillance region. Each sensing platform makes multiple, noisy measurements of an underlying, time-varying state that describes the monitored system. The monitored system involves potentially nonlinear target dynamics described by Markovian state-space model, nonlinear measurements, and non-Gaussian process and measurement noises. Each sensing platform reports measurements to a node in the network, which performs sequential estimation of the current system state using the probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior of the multi-target state. A sequential Monte Carlo method is used to implement the filter. The crucial consideration is what information needs to be transmitted over the network in order to perform online estimation of the current state of the monitored system, whilst attempting to minimize communication overhead. Simulation results demonstrate the efficiency of the proposed algorithm for a team of bearing only sensors.

Citation Download Citation

K. Punithakumar, T. Kirubarajan, and A. Sinha "A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350L (17 May 2006); https://doi.org/10.1117/12.667772

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available