A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Nested Superpositional Measurements

This paper proposes the Trajectory-Information Exchange Multi-Bernoulli (T-IEMB) filter to estimate sets of alive and all trajectories in track-before-detect applications with nested superpositional measurements. This measurement model has a nested architecture with two layers. The first layer includes superpositional hidden variables which are then mapped in the second layer to the conditional mean and covariance of the measurement, enabling it to model a broad range of measurement models. This paper also presents a Gaussian implementation of the T-IEMB filter, which performs the update by approximating the conditional moments of the measurement model, a computationally light filtering solution. Simulation results for a non-Gaussian radar-based tracking scenario demonstrate the performance of two Gaussian T-IEMB implementations, which provide improved tracking performance compared to state-of-the-art particle filters for track-before-detect, at a reduced computational cost.