Abstract
The estimation algorithm plays an important role in a radar tracking system. An improved estimation approach using both quantity data and target feature is investigated in this article. The advantage of this approach is that the system will have better estimation based on more target information. A data association denoted one-step conditional maximum likelihood algorithm is applied to match between radar measurements and existing target tracks. Moreover, an adaptive estimator is applied to combine the quantity data and target feature for estimation problems. According to the simulation results, this approach can enhance the performance of multiple-target tracking systems.
Highlights
In the tracking procedure, estimation algorithm is the key technique for multiple-target tracking systems
(4) Similarity measurement After operating the segmentation and coordinate transformation, the similarity between the image of measurement and image of existing target can be obtained by using the computation logic denoted zero mean sum of absolute differences (ZSAD) [10]
A fusion algorithm denoted as the adaptive estimator is applied to combine the different information
Summary
Estimation algorithm is the key technique for multiple-target tracking systems. In order to accurately estimate the targets, an image processing method [8-12] is applied to determine the features of the target and the tracking filter is applied to obtain the quantity data. In order to combine these two different attributes, an adaptive estimator is applied to match between radar measurements and existing target tracks.
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