Comparison of Particle Filter and PHD Multi-Target Tracking
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In this paper, we conduct a comparative study between particle filter and PHD multi-target tracking methods. To better illustrate the differences, we implemented separate tracking procedures for three targets using both particle filter and marginalPHD filter approaches. During our analysis of the results, we observed significant differences in computational efficiency and tracking accuracy. The particle filter implementation typically involves sequential importance sampling with resampling techniques to estimate target states, while the marginalPHD filter employs probability hypothesis density propagation to handle multi-target scenarios without explicit data association. Overall, these results demonstrate the relative strengths and limitations of each method in different tracking scenarios, providing valuable insights for algorithm selection in practical applications.
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