Abstract
Multi-target tracking is a common problem with many applications. In most of these the expected number of targets is not known a priori, so that it has to be estimated from the measured data. Poisson point processes (PPPs) are a very useful theoretical model for diverse applications. One of those is multi-target tracking of an unknown number of targets, leading to the intensity filter (iFilter) and the probability hypothesis density (PHD) filter. This chapter presents a sequential Monte Carlo (SMC) implementation of the iFilter. In theory it was shown that the iFilter can estimate a clutter model from the measurements and thus does not need it as a priori knowledge, like the PHD filter does. Our studies show that this property holds not only in simulations but also in real world applications. In addition it can be shown that the performance of the PHD filter decreases substantially if the a priori knowledge of the clutter intensity is chosen incorrectly.
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Schikora, M., Koch, W., Streit, R., Cremers, D. (2013). A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter. In: Georgieva, P., Mihaylova, L., Jain, L. (eds) Advances in Intelligent Signal Processing and Data Mining. Studies in Computational Intelligence, vol 410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28696-4_3
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DOI: https://doi.org/10.1007/978-3-642-28696-4_3
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