Radar Target Identification with Adaptive Clustering Parameters
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Solution Overview
Problem
Existing millimeter-wave radar systems face challenges in accurately distinguishing multiple reflection points of targets due to fixed algorithm parameters, leading to target merging or splitting issues during identification.
Innovation Solution
Adaptive adjustment of algorithm parameters such as neighborhood radius and density threshold based on radar data characteristics, using coefficients derived from amplitude and detection distance to enhance target identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the neighborhood radius and density threshold are set too large, then the clustering process is simpler, but multiple adjacent targets are merged into one target
Solution Approach 1:
The patent dynamically adjusts the neighborhood radius and density threshold parameters based on the actual radar data characteristics. Instead of using fixed parameters, the system calculates adaptive parameters for each clustering operation, allowing the clustering behavior to change according to the specific scene being analyzed. This resolves the contradiction by making the clustering process adaptable rather than statically simple or complex.
Solution Approach 2:
The patent changes the parameter values (neighborhood radius and density threshold) based on the radar data being processed. By computing parameters that adapt to the specific distribution and characteristics of the radar points, the system can accurately separate adjacent targets when needed while maintaining computational efficiency. This directly addresses the contradiction by making parameters variable rather than fixed.
2Measurement precision
If the neighborhood radius and density threshold are set too small, then the target identification is more precise, but the same target is split into multiple targets
Solution Approach 1:
The system employs dynamic parameter adjustment where the neighborhood radius and density threshold are computed based on the actual radar point distribution. This allows the clustering process to be precise when needed (when targets are well-separated) while avoiding unnecessary complexity in unified target scenarios. The dynamic nature enables the system to adapt its precision level to the specific situation.
Solution Approach 2:
The patent computes optimal parameter values based on the radar data characteristics before performing clustering. By changing the parameters adaptively rather than using small fixed values, the system achieves precise target identification without incorrectly splitting unified targets. This resolves the contradiction by making parameter selection data-driven rather than statically conservative.
3Productivity
If fixed algorithm parameters are used, then the processing is faster, but targets of different types and sizes cannot be accurately differentiated
Solution Approach 1:
The patent performs preliminary calculation of adaptive parameters before the actual clustering operation. By pre-computing the optimal neighborhood radius and density threshold based on radar data characteristics, the system prepares the appropriate parameters in advance, enabling both fast processing and accurate differentiation. This preliminary action resolves the contradiction by avoiding parameter recalculation during clustering while still achieving adaptability.
Solution Approach 2:
The system changes the algorithm parameters based on the specific radar data being processed, computing values that are appropriate for the current scene. This allows the processing to be optimized for each situation rather than using universal fixed parameters, achieving both efficiency and adaptability. The parameter changes are computed once per scene rather than during clustering, maintaining productivity.
Data Source
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AI summary
Provided are a method and an apparatus for identifying a radar target, a device, and a storage medium. The method includes acquiring radar data of at least one identification point; determining an algorithm parameter corresponding to each identification point according to the radar data of the at least one identification point; and determining, according to the algorithm parameter and in combination with a set algorithm, an identified radar target according to an algorithm output result. The method for identifying a radar target provided by the present application solves the problem of target merging or splitting caused by fixed algorithm parameters in the related art by adaptively adjusting the size of algorithm parameters according to the radar data of identification points. The method can be applied to the identification of targets of different types and sizes, thereby improving the accuracy of target identification.