Small UAV localization based strong tracking filters augmented with interacting multiple model

Elzoghby, Mostafa; Fu, Li; Arif, Usman; Arafa, Ibrahim I.

doi:10.1109/ibcast.2018.8312241

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Soccer tracking is a typical human visual motion tracking process. erefore, we can classify soccer tracking into two phases, soccer tracking and tracking maintenance, which are each corresponding to target acquisition and motion tracking of human visual tracking [14]. At the stage of target acquisition, the visual system aims to enter into the visual system to select visual stimulation and determine tracking objects.…”

Section: Introductionmentioning

confidence: 99%

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

Zhou

Huang

2021

Mathematical Problems in Engineering

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The paper studied the problems of soccer detection and tracking in soccer tracking, in soccer detection; as the size of the soccer is too small to extract distinguishable feature, it is difficult to detect the soccer automatically. To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed. Firstly, the target function of the spatial Fuzzy C-means was improved. Subsequently, a bi-threshold strategy was proposed to detect the soccer automatically. In the aspect of soccer tracking, existing methods fail to detect the soccer when it was occluded by several players successively. To solve this problem, the motion state of soccer of broadcast soccer video was analyzed, which is inspired by the contextual cueing effect of human visual search. According to the motion state of the soccer, parameters updating function of dynamic Kalman filter (DKF) were improved. Thus, a soccer tracking algorithm based on multiple search regions dynamic Kalman filter (MDKF) was proposed, which enhances the robustness of soccer tracking by extending the search area. The experiments show that the proposed algorithm can automatically detect soccer in images with high detection accuracy and can track the soccer more robustly, with better occlusion handle ability.

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Section: Introductionmentioning

confidence: 99%

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

Zhou

Huang

2021

Mathematical Problems in Engineering

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“…To verify the effectiveness of the proposed algorithm, first of all, this paper proves its advantages through simulation comparison experiments and then uses the actual observation data of the UAV to prove the engineering applicability of the proposed algorithm. In the simulation comparison experiment, the IMM algorithm in (Li and Bar-Shalom, 1993) (hereinafter referred to as IMM1) and the STF-IMM algorithm in (Elzoghby et al , 2018) (hereinafter referred to as IMM2) were compared with the AIMM-UKF algorithm proposed in this paper. The number of simulations is set to 50 times.…”

Section: Angular Velocity Estimation Of Constant Speed Rate Constant ...mentioning

confidence: 99%

“…Li and Bar-Shalom (1993) used the CV model and the CT model for air maneuvering target tracking and analyzed different IMM schemes. Based on the CV model, the CA model, the horizontally coordinated turn model (HCT) and the three-dimensional coordinated turn model, Elzoghby et al (2018) proposed a new small UAV tracking algorithm based on IMM.…”

Section: Introductionmentioning

confidence: 99%

A small UAV tracking algorithm based on AIMM-UKF

Hou

2021

AEAT

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Purpose The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs. Design/methodology/approach In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model. Findings Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value. Originality/value This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

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Active switching multiple model method for tracking a noncooperative gliding flight vehicle

Zheng

Yao

et al. 2020

Sci. China Inf. Sci.

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Small UAV localization based strong tracking filters augmented with interacting multiple model

Cited by 5 publications

References 32 publications

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

A small UAV tracking algorithm based on AIMM-UKF

Active switching multiple model method for tracking a noncooperative gliding flight vehicle

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