A new target tracking filter based on deep learning

Abstract

At present, current filters can basically solve the filtering problem in target tracking, but there are still many problems such as too many filtering variants, too many filtering forms, loosely coupled with the target motion model, and so on. To solve the above problems, we carry out cross-application research of artificial intelligence theory and methods in the field of tracking filters. We firstly analyze the computation graphs of typical α-β and Kalman. Through analysis, it is concluded that α-β and Kalman have the same computation structures analogous to a typical recurrent neural network and can be considered as a kind of recurrent neural network with constrained weights. Then, given this and considering that a recurrent neural network has the recognition capability for target motion patterns, a new filter is developed in a unified neural network architecture and specifically constructed using feedforward neural network, recurrent neural network, and attention mechanism. And the unified tracking filter proposed in this paper can generate three aspects of unity: a unified target motion model, an adaptive filter method, and an overall track filtering framework. Finally, Simulation results show that the proposed filter is effective and useful, of which the overall performance is superior to those of compared filters.