Linear multi-target IPF algorithm for automatic tracking

Abstract

The radar tracking applications perform single and multiple object detections from noise-corrupted signal. These detections are used as measurements for target tracking. Tracking in cluttered environments requires false track discrimination and data association. However, data association for tracking closely located multiple targets in heavy clutter is prohibitive due to the excessive computational requirement. This results from exponential growth of mutually exclusive and exhaustive feasible joint events for track-to-measurement assignment. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. The probability of target existence is used to discriminate the false tracks. The extended simulations showed the effectiveness of this approach in two different multi-target tracking scenarios.

Keywords

• radar tracking
• target designation
• target tracking
• moving target
• automatic target tracking
• algorithm