Robust Poisson Multi-Bernoulli Mixture Filter With Unknown Detection Probability

This paper proposes a robust Poisson multi-Bernoulli mixture (R-PMBM) filter immune to the unknown detection probability. In a majority of multi-object scenarios, the prior knowledge of detection probability is usually uncertain, which is often estimated offline from the training data. In such cases, online filtering is always unfeasible or unrealistic, otherwise, significant parameter mismatches will result in biased estimates (e.g., state and cardinality of objects). As a consequence, the ability of adaptively estimating the detection probability for a sensor is essential in practice. Based on the analysis, we detail how the detection probability can be estimated accompanied with the state estimates. Besides, the closed-form solutions to the proposed method are derived by means of approximating the intensity of Poisson random finite set (RFS) to a Beta-Gaussian mixture (BGM) form and density of Bernoulli RFS to a single Beta-Gaussian form, named BGM-PMBM filter. Simulation results demonstrate the effectiveness and robustness of the proposed method.