Target location estimation for vehicle dual radar based on unscented Kalman filter

Xiang Yi; Wang Yi; Zhang Jiachen; Cai Huaiyu; Chen Xiaodong

doi:10.12086/oee.2019.180339

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Xiang Yi, Wang Yi, Zhang Jiachen, et al. Target location estimation for vehicle dual radar based on unscented Kalman filter[J]. Opto-Electronic Engineering, 2019, 46(7): 180339. doi: 10.12086/oee.2019.180339

Citation:

Xiang Yi, Wang Yi, Zhang Jiachen, et al. Target location estimation for vehicle dual radar based on unscented Kalman filter[J]. Opto-Electronic Engineering, 2019, 46(7): 180339. doi: 10.12086/oee.2019.180339

Target location estimation for vehicle dual radar based on unscented Kalman filter

1.
School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
2.
Key Laboratory of Opto-Electronics Information Technology of Ministry of Education, Tianjin University, Tianjin 300072, China

Fund Project: Supported by Research on Key Technologies of Perception, Decision and Control for Unmanned Driving Fund (17ZXRGGX00140) and Natural Science Foundation of Tianjin (15JCQNJC14200)

More Information

Corresponding author: Wang Yi, E-mail: koala@126.com

Received Date 25 June 2018

Revised Date 18 October 2018

Published Date 01 July 2019

Abstract

Abstract

In the research of unmanned vehicle, the state estimation of target detected by sensors is one of the key issues in environmental sensing technology. In this paper, an algorithm based on unscented Kalman filter is proposed to predict and update the position of the target based on the obtained radar data, which is used to estimate the target position of the unmanned vehicle dual radar system. The vehicle radar system in this paper is composed of four lines laser and millimeter wave radar. The calibrated vehicle coordinate system is a two-dimensional coordinate system parallel to the ground. On the basis of the system and coordinate system, the real radar data are collected and simulated in the experimental site. Experiments show that compared with single sensor, the measurement error of radar combination model is effectively reduced, and the accuracy of fusion data is improved. Compared with the most commonly used extended Kalman filtering algorithm, the mean square error of the moving direction of vehicle descends from 6.15 per thousand to 4.83 per thousand. The mean square error value of the average position decreases from 4.24 per thousand to 2.99 per thousand in the direction parallel to the front axle, which indicates that the estimation of the target position of this algorithm is more accurate and closer to the real value. In addition, in the same operating environment, the average time of processing 500 groups of radar data is reduced from 5.9 ms to 2.1 ms, proving that the algorithm has a higher algorithm efficiency.
- LiDAR /
- millimeter wave radar /
- Kalman filter /
- position estimation

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References

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Overview

Overview

Overview: Unmanned driving refers to the technology of installing sensors such as vehicle-borne LiDAR, millimeter-wave radar, GPS and camera in a specific position of the vehicle and combining software algorithm and artificial intelligence to realize the autonomous and safe driving of the vehicle. In the research of unmanned vehicle, the state estimation of target detected by sensors is one of the key issues in environmental sensing technology. In this paper, an algorithm based on unscented Kalman filter is proposed to predict and update the position of the target based on the obtained radar data, which is used to estimate the target position of the unmanned vehicle dual radar system. The vehicle radar system in this paper is composed of four lines laser and millimeter wave radar. The calibrated vehicle coordinate system is a two-dimensional coordinate system parallel to the ground. On the basis of the system and coordinate system, the real radar data are collected and simulated in the experimental site. Experiments show that compared with single sensor, the measurement error of radar combination model is effectively reduced, and the accuracy of fusion data is improved. Compared with the most commonly used extended Kalman filtering algorithm, the mean square error of the moving direction of vehicle descends from 6.15 per thousand to 4.83 per thousand. The mean square error value of the average position decreases from 4.24 per thousand to 2.99 per thousand in the direction parallel to the front axle, which indicates that the estimation of the target position of this algorithm is more accurate and closer to the real value. In addition, in the same operating environment, the average time of processing 500 groups of radar data is reduced from 5.9 ms to 2.1 ms, proving that the algorithm has a higher algorithm efficiency. For the unmanned driving technology, it is obvious that this algorithm has more application prospects. In a word, the problem of target state estimation in the environment sensing technology of unmanned vehicle is studied in this paper. On this basis, unscented Kalman filter based on the combined measurement model of vehicle-borne LiDAR and millimeter-wave radar is proposed to estimate the position of the target detected by an unmanned vehicle. In the future research process, the algorithm proposed in this paper can also consider more target state parameters such as speed and acceleration, and add more sensors, such as cameras, GPS and INS, to improve the practicability of the whole system.