Abstract
We propose a sequential Monte Carlo-based cardinalized probability hypothesis density (SMC-CPHD) filter with adaptive survival probability for multiple frequency tracking to enhance the tracking performance. The survival probability of the particles in the filter is adjusted using the pre-designed exponential function related to the distribution of the estimated particle points. In order to ensure whether the proposed survival probability affects the stability of the filter, the error bounds in the prediction process are analyzed. Moreover, an inverse covariance intersection-based compensation method is added to enhance cardinality tracking performance by integrating two types of cardinality information from the CPHD filter and data clustering process. To evaluate the proposed method’s performance, MATLAB-based simulations are performed. As a result, the tracking performance of the multiple frequencies has been confirmed, and the accuracy of cardinality estimates are improved compared to the existing filters.
Highlights
Academic Editor: Ernesto LimitiResearch on multiple target tracking filters has been actively conducted in various fields as well as in the target tracking field [1,2,3,4,5,6,7,8,9,10,11,12,13]
We have recently conducted research to estimate and to mitigate multiple frequencies using the cardinalized probability hypothesis density (CPHD) filter among the random finite set (RFS)-based filters when a global navigation satellite system (GNSS) signal is received with multiple interference signals [22]
Based on the existing research results, we propose an adjustment technique of adaptive survival probability to set the appropriate value of survival probability for the multiple frequency tracking system, where the probability of survival is changed to be inversely proportional to the exponential function according to the Euclidean distance between the posterior state and the particles
Summary
Research on multiple target tracking filters has been actively conducted in various fields as well as in the target tracking field [1,2,3,4,5,6,7,8,9,10,11,12,13]. The performance is still not perfect, and additional methods are needed to enhance the estimate performance of the filter and the cardinality estimate performance in real situations, especially when clutter is present As part of these studies, in this paper, the adaptive survival probability is applied to the sequential Monte Carlo-based CPHD (SMC-CPHD). Based on the existing research results, we propose an adjustment technique of adaptive survival probability to set the appropriate value of survival probability for the multiple frequency tracking system, where the probability of survival is changed to be inversely proportional to the exponential function according to the Euclidean distance between the posterior state and the particles.
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