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A real-time incremental and adaptive clustering method based on automobile radar data

A technology of adaptive clustering and automotive radar, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of low clustering efficiency of automotive radar data, inability to deal with data density clusters, unevenness, etc., and achieve a solution The effect of uneven density of automotive radar data clusters, improving clustering efficiency, and saving time

Active Publication Date: 2019-03-08
GUILIN UNIV OF ELECTRONIC TECH
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AI Technical Summary

Problems solved by technology

[0006] The present invention aims to solve the shortcomings of low clustering efficiency of automotive radar data working in a multi-target complex environment, and the inability to deal with the problem of uneven data density clusters

Method used

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  • A real-time incremental and adaptive clustering method based on automobile radar data
  • A real-time incremental and adaptive clustering method based on automobile radar data
  • A real-time incremental and adaptive clustering method based on automobile radar data

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Embodiment

[0028] Refer to attached figure 1 , a real-time incremental and self-adaptive clustering method based on automotive radar data in the present invention, the innovation point is: improving the DBSCAN algorithm, combining EKF and DBSCAN to realize real-time clustering of automotive radar data.

[0029] attached figure 1 Among them, the improved DBSCAN algorithm of S3, the clustering standard is distance-angle two-dimensional data. In this embodiment, the pseudocode of the improved DBSCAN algorithm is as follows:

[0030] Algorithm 1 Improved Pseudocode of DBSCAN Algorithm

[0031]

[0032] Algorithm 2 Expand_cluster function

[0033]

[0034] Algorithm 1 gives the pseudocode of the improved DBSCAN algorithm, and Algorithm 2 gives the pseudocode of the subfunction Expand_cluster. The input data set is D, the initial distance radius is ε, the point threshold is MinPts, the Kalman prediction set is K, and the real-time data of the automobile 77Ghz millimeter-wave radar ...

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Abstract

The invention discloses a real-time incremental and adaptive clustering method based on automobile radar data, in order to solve the problem of low clustering efficiency and inability to cope with theuneven data density clustering problem, a real-time clustering algorithm based on EKF and DBSCAN is proposed for automotive radar data in multi-target complex environment. The method of the inventiontakes into account the characteristics of EKF which is often used by automobile radar in tracking and predicting targets, improves DBSCAN algorithm, and the improved DBSCAN algorithm can ensure thatthe clustering result is not affected by track coincidence to a great extent. Moreover, the Kalman filter parameters can be continuously iterated in the same target, which saves the time needed from the initial parameter iteration and improves the clustering efficiency. The method of the invention simultaneously realizes the incremental and adaptive DBSCAN clustering, can keep lower time memory overhead, and can be used for solving the situation of uneven density of automobile radar data clusters.

Description

technical field [0001] The present invention relates to the real-time cluster processing method of automobile radar data, specifically a kind of extended Kalman filter algorithm (EKF) based on automobile radar data, combined with density-based clustering algorithm (DBSCAN) to improve the real-time incremental and self-adaptive clustering method. Background technique [0002] Automotive Advanced Driver Assistance System (ADAS) uses various sensors installed on the car to sense the surrounding environment at any time during the driving process of the car, collect data, identify, detect and track static and dynamic objects, and Combined with the map data of the navigator, the system is calculated and analyzed, so that the driver can be aware of possible dangers in advance, and the comfort and safety of driving can be effectively increased. [0003] In recent years, automotive advanced driver assistance systems (ADAS) have become a field of research that major automakers and te...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23
Inventor 蒋留兵温和鑫车俐盘敏容
Owner GUILIN UNIV OF ELECTRONIC TECH
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