Adaptive weighting for estimation of the mean of the merged measurement for multi‐target bearing tracking
Quanrui Li, Longhao Qiu, Bin Qi, Guolong Liang · Electronics Letters · 2021
Abstract In multi‐target tracking, sensor with finite resolution generates merged measurements, which means that a group of targets might produce only one measurement, and such phenomenon could lead to degraded tracking performance if it is not considered. The generalised labelled multi‐Bernoulli filter for merged measurement (GLMB‐M) provides a promising solution for such problem. However, the merged measurement likelihood it used was modelled as a Gaussian density with its mean being the uniform weighted mean of the measurements generated by the merging targets. Such uniform weighting strategy might fail if the merged measurements deviate severely from the uniform weighted position due to significant difference of the strengths of the merging targets. To avoid such deviation, an adaptive weighting strategy for estimation of the mean of the merged bearing measurement is proposed, which uses the bearing measurement error to obtain the adaptive weighting factors. Simulation results show that the proposed adaptive weighting strategy outperforms the uniform weighting strategy when the strengths of the targets vary greatly, although both strategies provide similar performance when the strengths of the target are equal.