Practical Solutions to Tracking Problems - Schonborn, David (My University, 2022)
Tracking systems are already encountered in everyday life in numerous applications, but many algorithms from the existing literature rely on assumptions that do not always hold in realistic scenarios, or can only be applied in niche circumstances. Therefor this thesis is motivated to develop new approaches that relax assumptions and restrictions, improve tracking performance, and are applicable in a broad range of scenarios. In the area of terrain-aided tracking this an algorithm is proposed to track targets using a Gaussian mixture measurement distribution to better represent multimodal distributions that can arise due to terrain conditions. This allowed effective use in a wider range of terrain conditions than existing approaches, which assume a unimodal Gaussian measurement distribution. Next, the problem of estimating and compensating for sensor biases is considered in the context of terrain-aided tracking. Existing approaches to bias estimation cannot be easily reconciled with the nonlinear converted measurement model applied in terrain-aided tracking. To address this, a novel efficient bias estimation algorithm is proposed that can be applied to a wide range of measurement models and operational scenarios, allowing for effective bias estimation and measurement compensation to be performed in situations that cannot be handled by existing algorithms. Finally, to address scenarios where converted measurement tracking is not possible or desired, the problem of sensor motion compensation when tracking in pixel coordinates is considered. Existing approaches compensate for sensor motion by transforming state estimates between frames, but are only able to achieve partial transformation of the state estimate and its covariance matrix. This thesis proposes a novel algorithm used to transform the full state estimate and its covariance matrix, improving tracking performance when tracking with a low frame rate and when tracking targets moving with a nearly coordinated turn motion model. Each of the proposed algorithms are evaluated in several simulated scenarios and compared against existing approaches and baselines to demonstrate their efficacy.
Keywords: tracking; bias estimation; information fusion