Learn to Track-Before-Detect via Neural Dynamic Programming

The track-before-detect (TBD) paradigm can enhance radar detection and tracking of weak targets in the presence of noise and clutter. However, TBD gives rise to challenges in computational complexity and reliance on precise mathematical descriptions of the measurement model. This work presents a TBD algorithm combining dynamic programming and deep learning, augmenting the Viterbi algorithm with a dedicated deep neural network (DNN) to address these challenges. Our method alleviates the computational complexity by implementing state-aware pruning while bypassing an explicit use of a measurement model by utilizing a DNN. We demonstrate the effectiveness of our proposed algorithm using physically compliant Range-Doppler measurements.