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Unmanned aerial vehicle ground target self-adaptive tracking method based on CS model

An adaptive tracking and unmanned aerial vehicle technology, applied in the field of signal processing, can solve problems such as the decline of tracking accuracy

Inactive Publication Date: 2020-02-07
深圳市白麓嵩天科技有限责任公司
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Problems solved by technology

[0005] For this reason, the present invention proposes a maneuver frequency adaptive extended Kalman filter based on the CS model, which is fed back to the adaptive update of the maneuver frequency according to the results of each filter, effectively solving the problem of maneuver frequency when the target prior information is unknown. Inaccurate setting leads to the problem of decreased tracking accuracy, which improves the tracking accuracy of the system

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  • Unmanned aerial vehicle ground target self-adaptive tracking method based on CS model
  • Unmanned aerial vehicle ground target self-adaptive tracking method based on CS model
  • Unmanned aerial vehicle ground target self-adaptive tracking method based on CS model

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Embodiment Construction

[0034] The invention is an adaptive maneuvering frequency EKF (Extended Kalman Filter) filtering algorithm based on a CS model. combine figure 1 Shown algorithm flow chart, its specific implementation mode is:

[0035] Step 1: First determine the state vectors of the target and the UAV, and specify their initial position and attitude and other information.

[0036] The research object of the present invention is a ground target, and its state vector includes two directions of x and y:

[0037]

[0038] Among them, x y represents the current position of the target, Indicates the current speed of the target in the x y direction, Indicates the current acceleration of the target in two directions.

[0039] The kinematic equation of the target can be described as:

[0040]

[0041] Set the height of the drone to h U and airspeed v 0 Keeping it unchanged, the state vector describing the position and attitude of the UAV is:

[0042] x U =[x U ,y U ,Ω,ω] T

[0043...

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Abstract

The invention provides a maneuvering target self-adaptive tracking algorithm based on a CS (Current Statistical) model. Under the framework of an extended Kalman filter (EKF), an innovation minimization method is utilized to propose a stepping maneuver frequency change scheme to complete the self-adaptability of a filtering system. The method is suitable for an unmanned aerial vehicle or other mobile equipment carrying a direction and distance measurement sensor, and can reduce the problem of inaccurate state estimation caused by target maneuverability, thereby improving the tracking precisionof the EKF under the CS model.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a CS model-based adaptive tracking method for ground targets of unmanned aerial vehicles. Background technique [0002] An unmanned aerial vehicle is an unmanned aircraft operated by radio remote control equipment and its own program control device, or an aircraft that is completely or intermittently operated autonomously by an on-board computer. Compared with manned aircraft, unmanned aerial vehicles have the advantages of small size, low cost, and convenient use. [0003] Many applications of UAVs include the subtask of target tracking, such as the task of photographing moving targets and search tasks. The tracking behavior of UAV can be attributed to the estimation process of the maneuvering target state. The existing estimation methods mainly focus on the establishment of the target model and the filtering method suitable for computer processing. Target models ma...

Claims

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Application Information

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IPC IPC(8): G05D1/08G05D1/10
CPCG05D1/0808G05D1/101
Inventor 李一兵酒铭杨汤春瑞孙骞张羽田园叶方张思桐
Owner 深圳市白麓嵩天科技有限责任公司
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