Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR

Abstract

In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multiple-target state estimation by obtaining more accurate target and false-alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as probabilistic data association and multiple hypothesis tracking, and results in better tracking performance of low signal-to-noise ratio (SNR) targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements, and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multiple targets for different SNRs in the framework of finite set statistics (FISST). Furthermore, we illustrate how this can be incorporated into approximate multiple-object filters derived from FISST, including probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. We present simulation results for Gaussian mixture implementations of the filters that demonstrate a significant improvement in performance over just using location measurements.