"I love being wrong because that means in that instance, I learned something new that day" ~ Neil DeGrass Tyson
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 com- putational 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 imple- menting 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. For more details please refer to our article: https://ieeexplore.ieee.org/abstract/document/10448128?casa_token=0HNUM4QmmtoAAAAA:BKj-tEwqSShzuUW9ysLUilGsGc1xek7XmZWojjp4uPQRruHXBiAMHuHJBYpLIHQZFqReHsv6K2A
For recreation purposes you can download the data used in our article: https://drive.google.com/drive/folders/1YaE393DvkFBww7lniLDosAo4I0RkGdw7?usp=sharing