A method and system for intelligent identification of pollution sources using quantum radar

By constructing a multidimensional quantum feature space and iteratively updating the search window, the positioning deviation problem of the CamShift algorithm when the pollution source diffusion pattern is irregular is solved, realizing the rapid and reliable positioning of the pollution source location and diffusion range, and improving the accuracy and real-time performance of source tracing.

CN121559480BActive Publication Date: 2026-04-03RAYTHEON OPTOELECTRONIC TECH (TIANJIN) CO LTD +2
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Patent Information

Application Number
CN202610078980.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-21
Publication Date
2026-04-03
Estimated Expiration
2046-01-21

AI Technical Summary

Technical Problem

The existing CamShift algorithm has large positioning errors when the pollution source diffusion pattern is irregular, and poor real-time performance and accuracy in source tracing in scenarios with low contrast between the target and the background or blurred boundaries.

Method used

By constructing a multidimensional quantum feature space, a quantum feature back-projection probability distribution map is generated using photon wavelength, polarization state, and arrival time. Combined with the first-order moment, second-order central moment, third-order central moment, and spatial gradient, the center position and size of the search window are iteratively updated to improve positioning accuracy and convergence speed.

Benefits of technology

It enables rapid and reliable location of pollution sources and their spread, improving the accuracy and real-time nature of pollution source tracing and enhancing its anti-interference capabilities.

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Abstract

This invention provides a method and system for intelligent identification of pollution sources using quantum radar. The method includes achieving precise positioning through preprocessing of photonic quantum data frames, construction of a multi-dimensional feature space, and iterative window optimization. Abnormal signals are filtered based on a quantum background noise Poisson statistical model to generate a preprocessed data frame. A quantum feature space is constructed by fusing photon wavelength, polarization state, and arrival time to generate a back-projected probability distribution map. A composite displacement vector is constructed to update window parameters by calculating the first, second, and third central moments and spatial gradient of the probability distribution within the window. When the magnitude of the displacement vector is less than the convergence threshold, the location, diffusion range, and direction of the pollution source are output.
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