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Filtering point targets in highly cluttered and noisy data frames can be verychallenging, especially for complex target motions. Fixed motion models canfail to provide accurate predictions, while learning based algorithm can bedifficult to design (due to the variable number of targets), slow to train anddependent on separate train/test steps. To address these issues, this paperproposes a multi-target filtering algorithm which learns the motion models, onthe fly, using a recurrent neural network with a long short-term memoryarchitecture, as a regression block. The target state predictions are thencorrected using a novel data association algorithm, with a low computationalcomplexity. The proposed algorithm is evaluated over synthetic and real pointtarget filtering scenarios, demonstrating a remarkable performance over highlycluttered data sequences.

Filtering Point Targets via Online Learning of Motion Models