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By Ioannis Kyriakides, Darryl Morrell, Antonia Papandreou-Suppappola, Andreas Spanias

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Additional info for Adaptive High-Resolution Sensor Waveform Design for Tracking (Synthesis Lectures on Algorith and Software in Engineering)

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A sample code for the SIRPF is provided in Appendix C. 3 LIKELIHOOD PF In the Björck CAZAC case, the likelihood is very concentrated. Although this is good for measurement accuracy, the SIRPF will not work in this case as particles sampled from the broadly spread prior will not satisfy the likelihood easily. With the LPF, we sample values from the likelihood as it is more concentrated than the prior in the Björck CAZAC case. To achieve this, we evaluate the likelihood values at discrete bins of the delay-Doppler space of size T and ν.

31 CHAPTER 4 Single Target tracking with LFM and CAZAC Sequences In this chapter, we describe a sampling importance resampling particle filter (SIRPF) and a likelihood particle filter (LPF) [4] for the radar tracking problem. The SIRPF commonly uses the prior density as the importance density. However, when the likelihood is much more concentrated than the prior, samples proposed from the prior will be spread and, thus, will receive low weights when weighted with the likelihood. Therefore, the LPF is employed that uses the likelihood as the importance density.

Therefore, a particle filtering approach reduces approximation errors and the computational expense at the matched filter stage of the receiver. 31 CHAPTER 4 Single Target tracking with LFM and CAZAC Sequences In this chapter, we describe a sampling importance resampling particle filter (SIRPF) and a likelihood particle filter (LPF) [4] for the radar tracking problem. The SIRPF commonly uses the prior density as the importance density. However, when the likelihood is much more concentrated than the prior, samples proposed from the prior will be spread and, thus, will receive low weights when weighted with the likelihood.