Particle filtering algorithm for tracking multiple road-constrained targets

Abstract

We propose a particle filtering algorithm for tracking multiple ground targets in a road-constrained environment through the use of GMTI radar measurements. Particle filters approximate the probability density function (PDF) of a target's state by a set of discrete points in the state space. The particle filter implements the step of propagating the target dynamics by simulating them. Thus, the dynamic model is not limited to that of a linear model with Gaussian noise, and the state space is not limited to linear vector spaces. Indeed, the road network is a subset (not even a vector space) of R². Constraining the target to lie on the road leads to adhoc approaches for the standard Kalman filter. However, since the particle filter simulates the dynamics, it is able to simply sample points in the road network. Furthermore, while the target dynamics are modeled with a parasitic acceleration, a non-Gaussian discrete random variable noise process is used to simulate the target going through an intersection and choosing the next segment in the road network on which to travel. The algorithm is implemented in the SLAMEM simulation (an extensive simulation which models roads, terrain, sensors and vehicles using GVS). Tracking results from the simulation are presented.