An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

Yu, Miao; Oh, Hyondong; Chen, Wenhua

doi:10.1016/j.ast.2016.02.016

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…x k and _ y k are the corresponding velocities. When a target moves on the road, its motion state is mainly affected by the road topology [43]. For the off-road target, its motion is relatively free.…”

Section: Ground Target Motion Modelmentioning

confidence: 99%

Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

et al. 2022

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Tracking ground moving target with sensors proves to be a challenge due to the uncertainty of target motion area, the existence of Doppler blind zone (DBZ), and the complex terrain. In this paper, a multisensor management method based on DBZ information is presented, in which the available sensors are selected to obtain the best operational revenues for ground moving target tracking. First, the ground target motion model is established considering the off-road/on-road state based on road topology information. Second, the sensor measurement model is given combined with the DBZ information, and a decorrelation method of measurement noise is proposed. Third, a target state estimation algorithm is derived using particle filter, in which the DBZ information is regarded as prior information. Then, combined with the variable structure interacting multiple model method, an estimation algorithm for tracking maneuvering target is proposed. Furthermore, an optimization model of nonmyopic sensor management is constructed to obtain the best sensor management scheme. Finally, the advancement and effectiveness of the proposed management method are verified in the simulations.

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Section: Ground Target Motion Modelmentioning

confidence: 99%

Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

et al. 2022

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“…Then the prior probability P(x k |x k−1 ) was obtained. The equation used to predict the object state is expressed as follows [19]:…”

Section: State Predictionmentioning

confidence: 99%

Developing an On-Road Object Detection System Using Monovision and Radar Fusion

et al. 2019

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In this study, a millimeter-wave (MMW) radar and an onboard camera are used to develop a sensor fusion algorithm for a forward collision warning system. This study proposed integrating an MMW radar and camera to compensate for the deficiencies caused by relying on a single sensor and to improve frontal object detection rates. Density-based spatial clustering of applications with noise and particle filter algorithms are used in the radar-based object detection system to remove non-object noise and track the target object. Meanwhile, the two-stage vision recognition system can detect and recognize the objects in front of a vehicle. The detected objects include pedestrians, motorcycles, and cars. The spatial alignment uses a radial basis function neural network to learn the conversion relationship between the distance information of the MMW radar and the coordinate information in the image. Then a neural network is utilized for object matching. The sensor with a higher confidence index is selected as the system output. Finally, three kinds of scenario conditions (daytime, nighttime, and rainy-day) were designed to test the performance of the proposed method. The detection rates and the false alarm rates of proposed system were approximately 90.5% and 0.6%, respectively.

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“…In the second, multiple models, which are either superposed or switched, have been used to estimate more varying motion behavior [ 21 , 22 , 23 , 24 , 25 ]. The multiple-model (MM) estimation methods extend existing techniques to handle multiple models and cover a wider range of motion behavior [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of a Human-Maneuvered Target Incorporating Human Intention

Qin

Kumon

Furukawa

2021

Sensors

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This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

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An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

Cited by 17 publications

References 17 publications

Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

Multisensor Management Method for Ground Moving Target Tracking Based on Doppler Blind Zone Information

Developing an On-Road Object Detection System Using Monovision and Radar Fusion

Estimation of a Human-Maneuvered Target Incorporating Human Intention

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