Investigating And Extending The Quanta Tracking Algorithm

Abstract

The traditional tracking approach of forming detections and then associating these detections together is known to perform poorly at low signal-to-noise ratios (SNR). Track-before-detect (TBD) approaches, where the sensor data is used directly as opposed to forming detections, has been shown to perform better than traditional approaches at low SNRs.

One recently introduced TBD algorithm is the Quanta Tracking Algorithm that is formed by applying maximum likelihood estimation to the histogram probabilistic multi-target tracker (HPMHT). Quanta has shown promising performance for low SNR scenarios, but the body of literature is small and the evaluations that have been done so far do not consider several practical aspects of using the algorithm in real applications and are difficult to compare to other algorithms due to the SNR definitions used. This paper seeks to address these deficiencies in the literature. A re-derivation of Quanta that corrects some issues present in the original derivation while also integrating extensions from the HPMHT literature will also be presented. These extensions are shown to make Quanta able to correct for errors in the assumed size targets as well as improve estimating the SNR of fluctuating targets.