.png){kind=icon}
.png){kind=icon}
Return
Robust Poisson Multi-Bernoulli Mixture Filter With Unknown Detection Probability
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2020.3047107.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Radio frequency
Standards
Clutter
Radar tracking
Training data
Simulation
Robustness
Beta-Gaussian mixture
detection probability
Poisson multi-Bernoulli mixture
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
Organization
No organization information available