Radar Accuracy Modeling and Its Application to Object Vehicle Tracking

Choi, Woo Young; Kang, Chang Mook; Lee, Seung Hi; Chung, Chung Choo

doi:10.1007/s12555-019-1066-5

Cited by 21 publications

(11 citation statements)

References 30 publications

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“…Example of object vehicle estimation by radar: the measurement uncertainty occurs due to insufficient point cloud and classification errors. This error occurs because the radar estimation algorithm (e.g., point cloud clustering, segmentation, single sensor tracking, multilateration, classification, and filtering) can only estimate an object vehicle's size with a virtual polygon box with partial information [7,8,14,35]. Furthermore, the latency that causes position errors occurs due to the tracking algorithm of a commercial radar.…”

Section: Problem Statementmentioning

confidence: 99%

“…To improve radar accuracy, we first developed a model. Previous research analyzing radar accuracy [14,15] found that the error characteristics differ according to the mounting angle and detection area of the radar observing the object [16]. Since the radar error differs depending on the angle and the detection area, it is difficult to obtain an error characteristic solution for a radar's detection area.…”

Section: Data-driven Radar Accuracy Modelingmentioning

confidence: 99%

“…However, there is an error according to the object vehicle's velocity due to the radar's latency (the results of the analysis of the experimental data are shown in Section 4). This is caused by the object tracking algorithm of commercial radars [14,29]. This is a problem for anyone designing high-level applications for radar.…”

Section: Data-driven Radar Accuracy Modelingmentioning

confidence: 99%

“…For reasonable object tracking of a radar system, it has been found that the multiple model approach provides better filtering performance than a single model [13]. Radar tracking performance is improved through IMM [14] and convex interpolation [15] by using different radar accuracies depending on the object vehicle position [16]. In [9], the authors proposed an IMM algorithm using extended Kalman filters (EKF) for multi-target state estimation.…”

Section: Introductionmentioning

confidence: 99%

“…However, radar latency (processing delay) increases with the use of a filter [16,29,30]. This latency increases further depending on the tracking algorithm used (e.g., point cloud clustering, segmentation, single sensor tracking, multilateration, classification, and filtering) in vehicle applications [7,14,31,32]. In this regard, the radar sensor was evaluated for the effect of processing latency on the efficiency of detecting, acquiring, and tracking a target [29].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation

Choi

Yang

Chung

2021

Sensors

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For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.

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Section: Problem Statementmentioning

confidence: 99%