Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments

Area of Science:

• Radiation detection
• Signal processing
• Statistical modeling

Background:

• Existing algorithms struggle with detecting multiple, closely spaced radiation sources, especially without prior knowledge of source number or intensity.
• Limitations include poor dimensional scalability and a unimodal characteristic, hindering accurate multi-point source identification.

Purpose of the Study:

• To develop a robust algorithm for detecting and estimating parameters of multiple radiation sources in mixed multi-modal fields.
• To overcome the limitations of existing methods in handling closely spaced sources and unknown source numbers.

Main Methods:

• Proposed the Peak Suppressed Particle Filter (PSPF), a hybrid approach combining multi-layer particle filters, mean-shift clustering, and peak suppression.
• Incorporated a distance correction module and configuration maintenance for enhanced prediction stability and convergence.

Main Results:

• The PSPF algorithm successfully performs sequential estimation of multi-point sources in cross-mixed radiation fields.
• It non-parametrically determines the number of sources by automatically dispersing invalid particle swarms.
• Validated effectiveness and robustness through simulations and physical experiments under various noise levels and large-scale scenarios.

Conclusions:

• The PSPF algorithm offers a significant advancement in multi-point radiation source detection and parameter estimation.
• It effectively addresses challenges related to source proximity, unknown source counts, and noisy measurement conditions.
• Demonstrated superior performance and robustness compared to existing methods.