You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
17 May 2006A distributed implementation of a sequential Monte Carlo probability hypothesis density filter for sensor networks
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.
K. Punithakumar,T. Kirubarajan, andA. 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
The alert did not successfully save. Please try again later.
K. Punithakumar, T. Kirubarajan, 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