Abstract
In this paper, we discuss multi-target tracking for a submarine model based on incomplete observations. The submarine model is a weakly interacting stochastic dynamic system with several submarines in the underlying region. Observations are obtained at discrete times from a number of sonobuoys equipped with hydrophones and consist of a nonlinear function of the current locations of submarines corrupted by additive noise. We use filtering methods to find the best estimation for the locations of the submarines. Our signal is a measure-valued process, resulting in filtering equations that can not be readily implemented. We develop Markov chain approximation approach to solve the filtering equation for our model. Our Markov chains are constructed by dividing the multi-multi-targettarget state space into cells, evolving particles in these cells, and employing a random time change approach. These approximations converge to the unnormalized conditional distribution of the signal process based on the back observaions. Finally we present some simulation results by using the refining stochastic grid (REST) filter (developed from our Markov chain approximation method).
| Original language | English |
|---|---|
| Pages (from-to) | 245-253 |
| Number of pages | 9 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 5429 |
| DOIs | |
| Publication status | Published - 2 Dec 2004 |
| Externally published | Yes |
| Event | Signal Processing, Sensor Fusion, and Target Recognition XIII - Orlando, FL, United States Duration: 12 Apr 2004 → 14 Apr 2004 |
Keywords
- Filtering equations
- Markov chain approximations
- Measure-valued process
- Multi-target tracking
- REST
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering