Vehicle-mounted radar target detection method and device based on double-threshold detection

Abstract

The invention discloses a vehicle-mounted radar target detection method and device based on double-threshold detection, and the method comprises the steps: S1, obtaining an echo signal of a target vehicle-mounted radar, carrying out the target detection through employing a first threshold, and obtaining a preliminary target detection result; S2, performing angle estimation on the preliminary target detection result detected in the step S1 to obtain an angle estimation result; S3, performing target detection on the angle estimation result obtained in the step S2 by using a second threshold which is greater than the first threshold, so as to obtain a final target detection result and output the final target detection result. The invention comprises a target preliminary detection module, an angle estimation module and a secondary target detection module. The invention has the advantages of being simple in implementation method, low in cost, good in weak target detection performance, low in omission ratio, small in calculated amount, high in detection efficiency and the like.

Description

technical field [0001] The invention relates to the technical field of vehicle radar detection, in particular to a method and device for vehicle radar target detection based on double threshold detection. Background technique [0002] In order to achieve active vehicle safety, radar is usually installed in the vehicle to achieve collision avoidance, blind spot detection, etc. Typical millimeter-wave radar sensors, because they can work around the clock, can meet the requirements of the vehicle's adaptability to all-weather climates, and are not affected by harsh weather such as light, smog, and sandstorms, and have become the mainstream choice for automotive active safety. With huge market demand, automobiles in some areas have begun to be equipped with automotive millimeter-wave radar sensors such as anti-collision radars and blind spot detection radars. Vehicle radar detection has become the current main research direction. [0003] The environment in which the vehicle ac...

AI Technical Summary

Problems solved by technology

[0006] No matter what kind of target DOA (Degree of Arrival, angle estimation) estimation algorithm (beam scanning FFT method, super-resolution algorithm such as MUSIC (Multiple SIgnal Classification) algorithm, etc.) is used for the target detection method of the above-mentioned vehicle radar, the angle estimation must It is carried out after target detection, and in CFAR detection, due to the need to avoid generating too many false targets, a higher threshold is usually set, resulting in some weak targets with a small long-distance RCS that cannot be detected in time, such as far target, resulting in missed detection of the target

Therefore, due to the weak RCS of targets such as human bodies, long-distance detection of human bodies has become an important problem for vehicle-mounted radars.

[0007] However, if it is considered to measure the target angle directly before detection, although the radar’s detection ability for weak targets can be improved to a certain extent, it will also lead to a sharp increase in the amount of calculation.

Taking the above automotive millimeter-wave radar as an example, assuming that the number of array elements is N, the number of transmitted pulses is L, and the dimension of the obtained range-Doppler matrix is ​​N×L, after performing CFAR processing on the matrix, it is possible No target, there may be several targets, but the number of targets is generally on the order of tens, while the number of array elements is generally 4 or 8, and the number of pulses may be 256 or 512, or even 1024 , that is, the number of detection targets is very small compared to the matrix dimension, and if an angle measurement operation is performed on all elements in the range-Doppler matrix, the amount of calculation is extremely large, and it is basically difficult in the actual system accomplish

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