Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Kim, Taeklim; Park, Tae Hyoung

doi:10.3390/s20154126

Cited by 77 publications

(47 citation statements)

References 39 publications

(50 reference statements)

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“…In practical applications, the transition distribution p(x k |x k−1 ) was usually used as the proposal distribution, i.e., q(x k |y 1:k ) = p(x k |x k−1 ). In this case, the weight update in Equation (4) was simplified as…”

Section: Generic Particle Filtermentioning

confidence: 99%

“…There were six ultrawideband (UWB) sensors installed on the wall of the building. Compared to the traditional wireless positioning techniques (such as WiFi), UWB transmits information based on a non-sinusoidal narrow pulse (nanosecond-level), but not carrier phase, over a wide portion of the frequency spectrum [4]. Inherently, the extremely high time resolution, as well as the large bandwidth of UWB, enables it to have the advantages such as high ranging accuracy, high penetrating power [39], less interference from multipath effect [40], high-speed data transmission [41], etc.…”

Section: Test B: Three-dimensional Trackingmentioning

confidence: 99%

“…For the tracking problems, which are generally with non-linear state-space models, two variants of KF called extended Kalman filter (EKF) [2] and unscented Kalman filter (UKF) [3] are used instead. Some research on using EKF and UKF in target tracking is described in [4][5][6]. However, these two estimation methods have some limitations.…”

Section: Introductionmentioning

confidence: 99%

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A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

et al. 2021

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In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications.

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Section: Generic Particle Filtermentioning

confidence: 99%

Section: Test B: Three-dimensional Trackingmentioning

confidence: 99%

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A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

et al. 2021

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“…Some mapping and localisation systems using similar-but not identical-radars as that used in this work are described in Reference [26,27].…”

Section: Radar-based Mapping and Localisationmentioning

confidence: 99%

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

Martini

Gadd

Newman

2020

Sensors

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This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions.

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“…In [24], heuristic fusion with adaptive gating and track to track fusion were applied to a forwarding vehicle tracking system using camera and radar sensors, and the two algorithms were compared. In [25], the authors presented an EKF that reflects the distance characteristics of LiDAR and radar sensors. In [26], the fusion of radar and camera sensor data with a neural network was studied to improve object detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Cited by 77 publications

References 39 publications

A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

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

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