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This section, we focus on to the systematic generalization of the Single-target Bayes Recursion (SBR) to the Multi-target Bayes Recursion (MBR). Let’s consider the traditional single-target Bayes recursion where \(x_k\) is the state of the single-target system and \(z^{1:k} \triangleq \{z_1,\ldots,z_k\}\) is the measurement sequence up to time k. \(p_{k|k}(\cdot), p_{k+1|k}(\cdot) \) and \(p_k(\cdot)\) are the probability density functions of state update, state one-step prediction and measurement likelihood respectively.

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Probability Hypothesis Density (PHD) Filter

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  • 发表日期：

    2025年

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  • Ming Lei

摘要

This section, we focus on to the systematic generalization of the Single-target Bayes Recursion (SBR) to the Multi-target Bayes Recursion (MBR). Let’s consider the traditional single-target Bayes recursion where \(x_k\) is the state of the single-target system and \(z^{1:k} \triangleq \{z_1,\ldots,z_k\}\) is the measurement sequence up to time k. \(p_{k|k}(\cdot), p_{k+1|k}(\cdot) \) and \(p_k(\cdot)\) are the probability density functions of state update, state one-step prediction and measurement likelihood respectively.