Design and Development of Probabilistic Data Association Filters for Complete Object Refinement

Over the recent past, significant attention has been focused on the use of multiple sensors for target tracking over a large geographic area, as using a single sensor with a very large range is highly impractical. In addition, data from multiple sensors can be combined to provide improved estimation. Such Multi Sensor Data Fusion (MSDF) is similar to how humans and animals use multiple senses for perception. Probabilistic Data Association algorithms for Multiple Target Tracking (MTT), such as PDA and JPDA, use the statistical knowledge of the target dynamics and sensor measurement to obtain improved tracking.

In this work, Probabilistic Data Association filters (i.e., both PDA and JPDA filters) for Complete Object Refinement have been designed, developed and studied. The designed PDA algorithms have been developed using a Track Oriented approach. For JPDA, association matrices are determined at each scan based on the current targets and measurements; from this the set of event matrices are formed which is observed to be the most computationally intensive part of the JPDA. A radar simulator for generating synthetic 3D target measurements along with false measurements is developed. The system has been developed using an Object Oriented Development scheme and implemented as a plug-in architecture in which new features can be added.

The developed PDA and JPDA filters are extensively tested using synthetic track measurement data produced using the radar simulator. It was observed from the test results that PDA has lesser computation load in comparison to JPDA, but fails in the case of multiple interfering targets in a dense clutter. JPDA is successful in tracking such interfering targets due its joint association between targets and measurements. Due to JPDA's computation load only about 10 targets could be tracked, hence, clustering has to be used to decrease the computational load. PDA on the other hand is able to track more than 150 targets per scan. The work carried out can be used as a foundation for further development of target tracking systems with complex tracking needs.