This paper presents the application of a novel target tracking technique employing tensor decompositions for discretizing the target state space. The time evolution of the conditional probability density is realized by a Fokker-Planck equation solver and the measurement update, as usual, by applying Bayes’ rule. The method is applicable to non-Gaussian and non-linear system equations and enables the treatment of complex non-Gaussian target state densities. In addition, the efficient tensor decomposition scheme, in principle, allows for high-dimensional target states. The new tracking filter is applied to the problem of tracking an agile air target using bearing measurements from distributed acoustic and electromagnetic array sensors based on real data. It is shown that the new filter is able to initiate and maintain the target track with localization errors comparable to those of a standard particle filter.

Gaussian mixtures (GM) provide a flexible and numerically robust means for the treatment of nonlinearities as well as for the integration of context knowledge into target tracking algorithms. Contextual information lead to constraints on the target state which can be incorporated in the time prediction step of a tracking filter (model of the target dynamics) as well as in the measurement update step in terms of a constraint likelihood function. In this paper, we present examples for each possibility: road-map assisted target tracking and integration of terrain map data for target localization. The algorithms are applied to the problem of airborne passive emitter localization and demonstrate enhanced tracking and localization precision for moving and for stationary ground based emitters.

The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three issues: (i) we show the equivalence between the GMCPHD filter and the standard Multi Hypothesis Tracker (MHT) in the case of single targets; (ii) using a Gaussian sum approach, we extend the GMCPHD filter by employing digital road maps for road constraint targets. The utilization of such external information leads to more precise tracks and faster and more reliable target number estimates; (iii) we model the effect of Doppler blindness by a target state dependent detection probability, leading to more stable target number estimation in the case of low Doppler targets.

For analyzing dynamic scenarios with many ground moving vehicles, airborne ground moving target indicator (GMTI) radar is well-suited due to its wide-area, all-weather, day/night, and real time capabilities. The generation of GMTI tracks from these data is the backbone for producing a "recognized ground picture" as well as for analyzing traffic flows. In this paper we discuss the benefits of GMTI tracking in view of extracting road map information. The resulting tracking-generated road maps are highly up-to-date and fairly precise. Moreover, their accuracy is quantitatively described. The precision of the extracted road segments can significantly be improved using smoothed or "retrodicted" tracks. In turn, the extracted road information is exploited for the precise tracking of succeeding road targets. The proposed approach is illustrated by a simulated example including Doppler and terrain obscuration, providing hints to the achievable road map accuracies