  The process of association and tracking of sensor detections is a key element
in providing situational awareness. When the targets in the scenario are dense
and exhibit high maneuverability, Multi-Target Tracking (MTT) becomes a
challenging task. The conventional techniques to solve such NP-hard
combinatorial optimization problem involves multiple complex models and
requires tedious tuning of parameters, failing to provide an acceptable
performance within the computational constraints. This paper proposes a model
free end-to-end approach for airborne target tracking system using sensor
measurements, integrating all the key elements of multi target tracking --
association, prediction and filtering using deep learning with memory. The
challenging task of association is performed using the Bi-Directional Long
short-term memory (LSTM) whereas filtering and prediction are done using LSTM
models. The proposed modular blocks can be independently trained and used in
multitude of tracking applications including non co-operative (e.g., radar) and
co-operative sensors (e.g., AIS, IFF, ADS-B). Such modular blocks also enhances
the interpretability of the deep learning application. It is shown that
performance of the proposed technique outperforms conventional state of the art
technique Joint Probabilistic Data Association with Interacting Multiple Model
(JPDA-IMM) filter.
