Automotive Tracking Technique Using a New IMM Based PDA-SVSF

Abstract

Car tracking algorithms are important for a number of applications, including self-driving cars and vehicle safety systems. The probabilistic data association (PDA) algorithm, in conjunction with Kalman Filter (KF), and interacting multiple model (IMM) are well studied, specifically in the aero-tracking applications. This paper studies single targets while performing maneuvers in the presence of clutter, which is a common scenario for road vehicle tracking applications. The relatively new smooth variable structure filter (SVSF) is demonstrated to be robust and stable filtering strategy under the presence of modeling uncertainties. In this paper, SVSF based PDA technique is combined with IMM method. The new method, referred to as IMM-PDA-SVSF is simulated under several possible car motion scenarios. Also, the algorithm is tested on a real experimental data acquired by GPS device.