A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment

In order to conduct risk assessment for collision-free decision making of intelligent vehicles in a complex road traffic environment, it is essential to conduct stable tracking and state estimation of multiple vehicle targets. Therefore, this paper proposes a multitarget tracking method in line with the priority data association rule. Firstly, a standard coordinate turn process model is deduced as the existence of translation and rotation of the vehicle on the two-dimensional ground plane. Secondly, an unscented Kalman filter algorithm is developed due to the nonlinear radar measurement model. Thirdly, a priority data association rule, which puts the targets in a priority queue according to the number of times they are associated, is designed to filter out noise, given that it is unlikely for a vehicle target as an inertial system to appear or disappear instantly in practice. In addition, the data association rule specifies that the closer the target is to the front of the queue, the more prioritized the association with the newly observed target would be. Finally, the track management algorithm is constructed. On this basis, a series of real vehicle tests were conducted on real roads with four typical scenarios. According to the test results, the proposed algorithm is effective in filtering out noise and is suboptimal as the nearest neighbor data association.

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Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203820359 ◽

2020 ◽

Vol 38 (2) ◽

pp. 359-365

Author(s):

Xiaohua Li ◽

Ya'an Li ◽

Xiaofeng Lu ◽

Chenxu Zhao ◽

Jing Yu

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Data Association ◽

Estimation Accuracy ◽

Multitarget Tracking ◽

Single Observation ◽

Multiple Observations ◽

Multiple Hypothesis Tracking ◽

Kalman Filter Algorithm ◽

Present Algorithm

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

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A new insight to the ANN-based multitarget tracking data association

1993 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.1993.394253 ◽

2002 ◽

Author(s):

S. Mao ◽

P. Lin ◽

W. Yu

Keyword(s):

Data Association ◽

Multitarget Tracking ◽

Tracking Data

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The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context

Cybernetics and Information Technologies ◽

10.1515/cait-2015-0092 ◽

2015 ◽

Vol 15 (7) ◽

pp. 88-98

Author(s):

J. Dezert ◽

A. Tchamova ◽

P. Konstantinova

Keyword(s):

Quality Assessment ◽

Data Association ◽

New Method ◽

Multitarget Tracking ◽

Belief Functions ◽

Tracking Systems ◽

Optimal Assignment ◽

The Individual ◽

The Impact

Abstract The main purpose of this paper is to apply and to test the performance of a new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the optimal data association solution for improving the performances of multisensor-multitarget tracking systems. The advantages of its implementation in an illustrative realistic surveillance context, when some of the association decisions are unreliable and doubtful and lead to potentially critical mistake, are discussed. A comparison with the results obtained on the base of Generalized Data Association is made.

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Trajectory Optimization for Multitarget Tracking Using Joint Probabilistic Data Association Filter

Journal of Guidance Control and Dynamics ◽

10.2514/1.g004249 ◽

2020 ◽

Vol 43 (1) ◽

pp. 170-178 ◽

Cited By ~ 2

Author(s):

Shaoming He ◽

Hyo-Sang Shin ◽

Antonios Tsourdos

Keyword(s):

Trajectory Optimization ◽

Data Association ◽

Multitarget Tracking ◽

Probabilistic Data ◽

Probabilistic Data Association

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Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Sensors ◽

10.3390/s19112467 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2467 ◽

Cited By ~ 2

Author(s):

Hery Mwenegoha ◽

Terry Moore ◽

James Pinchin ◽

Mark Jabbal

Keyword(s):

Unmanned Aerial Vehicles ◽

Navigation System ◽

Process Model ◽

Unscented Kalman Filter ◽

Low Cost ◽

Moment Of Inertia ◽

Model Parameters ◽

Main Process ◽

Model Based ◽

Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

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A Novel Framework for Road Traffic Risk Assessment with HMM-Based Prediction Model

Sensors ◽

10.3390/s18124313 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4313 ◽

Cited By ~ 4

Author(s):

Xunjia Zheng ◽

Di Zhang ◽

Hongbo Gao ◽

Zhiguo Zhao ◽

Heye Huang ◽

...

Keyword(s):

Risk Assessment ◽

Road Traffic ◽

Research Effort ◽

Assessment Method ◽

Intelligent Vehicles ◽

Steering Angle ◽

The Road ◽

Risk Assessment Method ◽

Significant Research ◽

Assessment Strategies

Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies.

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