  Filtering point targets in highly cluttered and noisy data frames can be very
challenging, especially for complex target motions. Fixed motion models can
fail to provide accurate predictions, while learning based algorithm can be
difficult to design (due to the variable number of targets), slow to train and
dependent on separate train/test steps. To address these issues, this paper
proposes a multi-target filtering algorithm which learns the motion models, on
the fly, using a recurrent neural network with a long short-term memory
architecture, as a regression block. The target state predictions are then
corrected using a novel data association algorithm, with a low computational
complexity. The proposed algorithm is evaluated over synthetic and real point
target filtering scenarios, demonstrating a remarkable performance over highly
cluttered data sequences.
