Adaptive filter

Statistics

Filter (signal processing)

Algorithm

Mathematical optimization

Mathematical analysis

Philosophy

Minification

Artificial intelligence

Psychology

Matching (statistics)

Kullback–Leibler divergence

Pedagogy

Divergence (linguistics)

Tracking (education)

Computer vision

Mathematics

Computer science

Linguistics

Upper and lower bounds

IEEE Transactions on Signal Processing

In this paper, in order to improve the Student's t-matching accuracy, a novel Kullback-Leibler divergence (KLD) minimization-based matching method is firstly proposed by minimizing the upper bound of the KLD between the true Student's t-density and the approximate Student's t-density. To improve the Student's t-modelling accuracy, a novel KLD minimization-based adaptive method is then proposed to estimate the scale matrices of Student's t-distributions, in which the modified evidence lower bound is maximized. A novel KLD minimization-based adaptive Student's t-filter is derived via combining the proposed Student's t-matching technique and the adaptive method. A manoeuvring target tracking example is provided to demonstrate the effectiveness and potential of the proposed filter.

A Novel Kullback–Leibler Divergence Minimization-Based Adaptive Student's t-Filter