Auxiliary truncation particle filtering method, device, target tracking method and device

Abstract

The invention discloses an auxiliary truncation particle filtering method, a device, a target tracking method and a device. The auxiliary truncation particle filtering method comprises the steps of adopting an original prior probability density function as a first importance density function for filtering particles to obtain a first mean value and a first covariance value corresponding to a target state; in the original prior probability density function, importing the current observation information and the target characteristic information based on the truncation theory to construct a corrected prior probability density function; adopting the corrected prior probability density function as a second importance density function for filtering particles so as to obtain a second mean value and a second covariance value corresponding to the target state; respectively conducting the weighted treatment on first mean value, the first covariance value, the second mean value and the second covariance value according to the estimated weight of the target state so as to obtain a posterior probability density function corresponding to the target state, and completing the particle filtering process. In this way, the particle filtering accuracy and the particle filtering real-time performance are improved. Meanwhile, the rapid target-tracking problem of a target model in the uncertainty condition caused by the target maneuvering condition in the non-linear and non-gaussian environment can be solved.

Description

technical field

[0001] The invention relates to the field of nonlinear filtering, in particular to an auxiliary truncation particle filter method and device, and a target tracking method and device. Background technique

[0002] As an effective method to optimally deal with nonlinear and non-Gaussian problems, particle filter has been widely used in various nonlinear filtering fields, such as image monitoring, target positioning and tracking, environmental monitoring and other fields. In terms of estimation performance, the particle filter is superior to the most widely used extended Kalman filter (EKF) and unscented Kalman filter (UKF), as well as many different filtering methods derived from them, such as iterative extended Kalman filter (IEKF ), Gauss Hermit filter (GHF), integral Kalman filter (QKF). In terms of the ability to deal with nonlinear problems, the particle filter is suitable for any nonlinear non-Gaussian system that can be represented by a state-space mode...

Examples

Embodiment Construction

[0042] Certain words are used to refer to specific components in the description and claims, and those skilled in the art should understand that manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in name as a way to distinguish components, but use the difference in function of components as a basis for distinction. The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] In many practical engineering applications such as signal processing, communication, radar, sonar, etc., dynamic space models are often used to describe many problems. Its model can be expressed as state equation (1) and observation equation (2):

[0044] x k = f k (x k-1 )+v k (1)

[0045] z k = h k (x k )+e k (2)

[0046] In the formula, and represent known nonlinear functions respectively; Indicates the system state vector at time k; is the obser...

technical field

[0001] The invention relates to the field of nonlinear filtering, in particular to an auxiliary truncation particle filter method and device, and a target tracking method and device. Background technique

[0002] As an effective method to optimally deal with nonlinear and non-Gaussian problems, particle filter has been widely used in various nonlinear filtering fields, such as image monitoring, target positioning and tracking, environmental monitoring and other fields. In terms of estimation performance, the particle filter is superior to the most widely used extended Kalman filter (EKF) and unscented Kalman filter (UKF), as well as many different filtering methods derived from them, such as iterative extended Kalman filter (IEKF ), Gauss Hermit filter (GHF), integral Kalman filter (QKF). In terms of the ability to deal with nonlinear problems, the particle filter is suitable for any nonlinear non-Gaussian system that can be represented by a state-space mode...

Examples

Embodiment Construction

[0042] Certain words are used to refer to specific components in the description and claims, and those skilled in the art should understand that manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in name as a way to distinguish components, but use the difference in function of components as a basis for distinction. The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] In many practical engineering applications such as signal processing, communication, radar, sonar, etc., dynamic space models are often used to describe many problems. Its model can be expressed as state equation (1) and observation equation (2):

[0044] x k = f k (x k-1 )+v k (1)

[0045] z k = h k (x k )+e k (2)

[0046] In the formula, and represent known nonlinear functions respectively; Indicates the system state vector at time k; is the obser...

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