A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge

Abstract

In this work, we propose a new ground moving target indicator (GMTI) radar based ground vehicle tracking method which exploits domain knowledge. Multiple state models are considered and a Monte-Carlo sampling based algorithm is preferred due to the manoeuvring of the ground vehicle and the non-linearity of the GMTI measurement model. Unlike the commonly used algorithms such as the interacting multiple model particle filter (IMMPF) and bootstrap multiple model particle filter (BS-MMPF), we propose a new algorithm integrating the more efficient auxiliary particle filter (APF) into a Bayesian framework. Moreover, since the movement of the ground vehicle is likely to be constrained by the road, this information is taken as the domain knowledge and applied together with the tracking algorithm for improving the tracking performance. Simulations are presented to show the advantages of both the new algorithm and incorporation of the road information by evaluating the root mean square error (RMSE).

Classifying the weights of particle filters in nonlinear systems

“…Particle filter methods reduce computational complexity and increase accuracy of the estimations by choosing reliable samples from numerous available samples [3][4][5][6][7][8][9][10][11]. It should be noted that Bayesian estimation algorithm is used during the Monte Carlo procedure to calculate an estimation of the prior probability density function for k=1,2, …, instead of calculating the state k x [12][13][14].…”

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

“…After the state estimation, for every particle i whose r i k = on-road, the vehicle state vector x projection,i k is converted back to the local coordinate for the VSMM prediction at the next time instance using Eq. (9). The outline of the proposed C-VSMM-PSO-PF algorithm is presented in Algorithm 2:…”

“…The most widelyused terrain information is the road network, and representative work is shown in [6][7][8][9]. In [6], the road constraint was treated as a pseudo-measurement and incorporated into the extended Kalman filter (EKF) scheme for state estimation of the target moving on the road.…”

“…Cheng et al in [8] applied a multiple model framework to describe the movement of a vehicle, and the unscented particle filtering scheme was proposed for more effective particle generation. In [9], an interactive multiple model auxiliary particle filtering (IMMAPF) algorithm was applied while considering the road width and multiple state models which describe the different vehicle movements. Arulampalam et al [10] presented a GMTI vehicle tracking problem in which a vehicle could move on or off road, and a variable structure multiple model (VS-MM) particle filter was applied.…”

New Environmental Dependent Modelling with Gaussian Particle Filtering Based Implementation for Ground Vehicle Tracking

“…The most apparent domain knowledge is the road constraint information such as the constrained region imposed by a road map. Studies on road network-aided ground vehicle tracking have been reported in different works such as [2], [3] and [4]. In these works, different state estimation algorithms (such as the Gaussian (s) approximation filtering method in [2] and [4], and particle filtering method [3]) have been applied together with the road constraint information for the state estimation.…”