SMC-CPHD Filter with Adaptive Survival Probability for Multiple Frequency Tracking
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Target Tracking and Data Fusion in Sensor Networks · Structural Health Monitoring Techniques · Underwater Acoustics Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Convergence System Engineering, Kunsan National University, Gunsan 54150, Korea
Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
Department of Aerospace Engineering and Automation and System Research Institute, Seoul National University, Seoul 08826, Korea
Funding
Defense Acquisition Program Administration · National Research Foundation of Korea
References (42)
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