Non-LOS target localization via millimeter-wave automotive radar

Zhaoyu, Liu; Wenli, Zhang; Jingyue, Zheng; Shisheng, Guo; Guolong, Cui; Lingjiang, Kong; Kun, Liang

doi:10.23919/jsee.2023.000070

J. of Syst. Eng. Electron.

2023

DOI: 10.23919/jsee.2023.000070

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Non-LOS target localization via millimeter-wave automotive radar

Liu Zhaoyu,

Zhang Wenli,

Zheng Jingyue

et al.

Abstract: This paper considers the non-line-of-sight (NLOS) vehicle localization problem by using millimeter-wave (MMW) automotive radar. Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results. Firstly, an electromagnetic (EM) wave NLOS multipath propagation model for vehicle scene is established. Subsequently, with the help of available multipath echoes, a complete NLOS vehicle localization algorithm is proposed. Finally, simulation and experimental results validate the effecti… Show more

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Cited by 3 publications

(1 citation statement)

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“…이러한 자율 주행 차량의 안정성을 높이기 위해 다양 한 표적 탐지 기술이 활용되고 있다. 이 기술들은 차량이 주변 환경을 인식하고, 운전자에게 조기에 경고를 제공함 으로써 충돌 위험을 줄이는 데 중추적인 역할을 한다 [2] . 최근에는 미국, 유럽, 일본 등에서 자동 긴급 제동 시스템 이 의무화되고 있으며 [3] , 이러한 능동 안전 시스템과 인 지 센서는 자동차 기술의 핵심 요소로 자리 잡고 있다.…”

unclassified

Pedestrian Trajectory Prediction Using LSTM Regression Model in Automotive Radar Systems

Jeon,

Kwon,

Kwak

et al. 2024

J. Korean Inst. Electromagn. Eng. Sci.

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In this study, we propose a two-step method to enhance pedestrian detection and driver safety in automotive radar systems. First, we assumed information regarding the range, velocity, and angle of pedestrians behind stationary vehicles using a frequency-modulated continuous wave radar system, with the cell-averaging constant false alarm rate algorithm employed to effectively distinguish between the target signals and noise components. Additionally, a multiple signal classification algorithm was used for the high-resolution angle estimation of the target. Subsequently, a long short-term memory network is applied to predict the movement of a pedestrian, with the results indicating an average error of 0.09 m between the predicted and actual points along the trajectory.

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unclassified

Pedestrian Trajectory Prediction Using LSTM Regression Model in Automotive Radar Systems

Jeon,

Kwon,

Kwak

et al. 2024

J. Korean Inst. Electromagn. Eng. Sci.

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A Survey of Vehicle Localization: Performance Analysis and Challenges

Shan,

Cabani,

Chafouk

2023

IEEE Access

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Vehicle localization plays a crucial role in ensuring the safe operation of autonomous vehicles and the development of intelligent transportation systems (ITS). However, there is insufficient effort to compare the performance and challenges of different vehicle localization algorithms. This paper aims to address this gap by analyzing the comprehensive performance of existing advanced vehicle localization techniques and discussing their challenges. Firstly, we analyze the self-localization methods based on active and passive sensors. The results show that, the light detection and ranging (LiDAR) and vision-based techniques can reach high accuracy. However, they have high computational complexity. And only using the inertial measurement unit (IMU), global positioning system (GPS), radar, and ultrasonic sensors may not realize localization result with high accuracy. Then, we discuss V2X-based cooperative methods and analyze the multi-sensor based localization techniques and compare the comprehensive performance among all methods. Although the artificial intelligence (AI) techniques can effectively enhance the efficiency of vision-based localization algorithms, the high computational complexity still should be considered. In addition, since the IMU, GPS, radar, and ultrasonic sensors have good performance in terms of the availability, scalability, computational complexity, and cost-effectiveness, they can be used as auxiliary sensors to achieve good comprehensive performance through data fusion techniques. Finally, we propose the challenges of different techniques and look forward to future work.

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Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

Si,

Xu,

et al. 2023

Drones

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Swarming is one of the important trends in the development of small multi-rotor UAVs. The stable operation of UAV swarms and air-to-ground cooperative operations depend on precise relative position information within the swarm. Existing relative localization solutions mainly rely on passively received external information or expensive and complex sensors, which are not applicable to the application scenarios of small-rotor UAV swarms. Therefore, we develop a relative localization solution based on airborne monocular sensing data to directly realize real-time relative localization among UAVs. First, we apply the lightweight YOLOv8-pose target detection algorithm to realize the real-time detection of quadcopter UAVs and their rotor motors. Then, to improve the computational efficiency, we make full use of the geometric properties of UAVs to derive a more adaptable algorithm for solving the P3P problem. In order to solve the multi-solution problem when less than four motors are detected, we analytically propose a positive solution determination scheme based on reasonable attitude information. We also introduce the maximum weight of the motor-detection confidence into the calculation of relative localization position to further improve the accuracy. Finally, we conducted simulations and practical experiments on an experimental UAV. The experimental results verify the feasibility of the proposed scheme, in which the performance of the core algorithm is significantly improved over the classical algorithm. Our research provides viable solutions to free UAV swarms from external information dependence, apply them to complex environments, improve autonomous collaboration, and reduce costs.

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Non-LOS target localization via millimeter-wave automotive radar

Cited by 3 publications

References 32 publications

Pedestrian Trajectory Prediction Using LSTM Regression Model in Automotive Radar Systems

Pedestrian Trajectory Prediction Using LSTM Regression Model in Automotive Radar Systems

A Survey of Vehicle Localization: Performance Analysis and Challenges

Relative Localization within a Quadcopter Unmanned Aerial Vehicle Swarm Based on Airborne Monocular Vision

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