Critical Angle for Optimal Correlation Assignment to Control Memory and Computational Load Requirements in a Densely Populated Target Environment

The research presents a simulation study on the performance of a target tracker using a critical angle selection technique for optimal correlation assignment of a target track with the incoming observation(s) for the track splitting filter (TSF) algorithm. In a typical TSF all the observations falling inside a likelihood ellipse are used for update. However, our proposed optimal correlation procedure TSF algorithm uses only those observations for track update that fall within the critical angle sector made inside the prediction ellipse. This kind of approach is particularly important if the computational and memory requirements are limited relative to the amount of input data (number of objects) that can potentially saturate the system.

Previous performance work [1] has been done on specific (deterministic) scenarios. One of the reasons for considering the specific scenarios, which were normally crossing targets, was to test the efficiency of the track splitting algorithm. This approach gives a measure of performance for a specific, possibly unrealistic scenario. However, such investigation procedures help in designing tracking systems that can select high-value targets based on particular attributes.

In order to develop procedures that would enable a more general performance assessment compared with deterministic scenarios, our study adopted a random target motion scenario. Its implementation for testing the proposed technique using a track splitting Kalman filter algorithm is investigated. A number of performance parameters that give the activity profile of the tracking scenario are also investigated. This kind of performance evaluation can provide in-depth knowledge of tracking activity for developing possibly better and more appropriate target tracking systems. The complete prototype system is implemented using a TMS320C6416 digital signal processor (DSP).