An Efficient Implementation of the Multiple-Model Generalized Labeled Multi-Bernoulli Filter for Track-Before-Detect of Point Targets Using an Image Sensor

In this article, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter based on track-before-detect (TBD) measurement model, called as MM-GLMB-TBD filter, is presented for tracking maneuvering targets with low signal-to-noise rate (SNR) by integrating the prediction and update into a single step. Based on Gibbs sampling solution to truncating the GLMB densities, the convergence behavior is taken into consideration to reduce computational burden of MM-GLMB-TBD filter. In this article, the lattice-reduction Gibbs sampling with flexible proposal is presented to effectively truncate the filtering densities in the MM-GLMB-TBD filter with geometric ergodicity and better exponential convergence rate. The simulation results demonstrate that the proposed method is particularly suitable for multiple weak targets tracking solution based TBD measurement model due to faster convergence rate. Finally, it is verified from the results that the proposed method is highly robust to variance in different low SNRs.