Maneuvering target tracking method and device and computer readable storage medium

A technology of maneuvering target tracking and covariance, applied in the field of target tracking, can solve the problems of poor maneuvering target tracking effect and stability, and achieve good target tracking effect, strong stability, and good estimation performance

Pending Publication Date: 2022-08-05
SHENZHEN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the embodiments of the present invention is to provide a maneuvering target tracking method, device and computer-readable storage medium, which can at least solve the problem of poor maneuvering target tracking effect and stability of the filtering algorithm provided in the related art

Method used

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  • Maneuvering target tracking method and device and computer readable storage medium
  • Maneuvering target tracking method and device and computer readable storage medium
  • Maneuvering target tracking method and device and computer readable storage medium

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no. 1 example

[0030] In order to solve the problem of poor maneuvering target tracking effect and stability of the filtering algorithm provided in the related art, the present embodiment proposes a maneuvering target tracking method, such as: figure 1 Shown is a schematic flowchart of the maneuvering target tracking method provided by the present embodiment, and the maneuvering target tracking method proposed by the present embodiment includes the following steps:

[0031] Step 101: Based on the fuzzy membership degrees of indicators representing the influence of different error samples on the state estimation result, construct a minimum fuzzy error entropy criterion.

[0032] Rayleigh entropy is derived from information theory learning and is usually used to measure the difference, randomness and uncertainty of random variables. For error e=X-Y, X, Y are continuous random variables, the error entropy can be measured by Rayleigh entropy as follows:

[0033]

[0034] Among them, α repres...

no. 2 example

[0137] In order to further illustrate the technical effect of the maneuvering target tracking method provided by the embodiment of the present invention, in the following three experiments in this embodiment, the square root error (Root Mean Square Error, RMSE) will be used as the performance index of the filter, which is defined as follows :

[0138]

[0139] Among them, M is the number of Monte Carlo runs, and K is the number of sampling points.

[0140] The parameter settings are as follows:

[0141] The setting of the core width σ will have a significant impact on the performance of MFEE-UF. If the core width is too small, the robustness will be improved, and the convergence speed will be too slow, time-consuming, and there is a risk of falling into a divergent state; if the core width is too large, the convergence speed will be accelerated. At the same time, the performance will be weakened; in addition, considering the complexity and randomness of noise, the (e j -e...

no. 3 example

[0183] In order to solve the problem of poor maneuvering target tracking effect and stability of the filtering algorithm provided in the related art, the present embodiment shows a maneuvering target tracking device. For details, please refer to. Figure 5 , the mobile target tracking device of this embodiment includes:

[0184] A construction module 501 is used to construct the minimum fuzzy error entropy criterion based on the fuzzy membership degree representing the influence index of different error samples on the state estimation result;

[0185] Obtaining module 502 is used to obtain a priori estimation of state and corresponding covariance by utilizing the unscented transformation framework, and obtains a priori estimation of observation and corresponding covariance;

[0186] The reconstruction module 503 is used to construct a linear regression model based on a priori estimation of state and corresponding covariance, observation and a priori estimation of corresponding...

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Abstract

The invention discloses a maneuvering target tracking method and device and a computer readable storage medium, and the method comprises the steps: constructing a minimum fuzzy error entropy criterion based on a fuzzy membership degree of an index representing the influence of different error samples on a state estimation result; obtaining state and prior estimation of the corresponding covariance by using an unscented transformation framework, and observing and prior estimation of the corresponding covariance; constructing a linear regression model based on state and prior estimation of corresponding covariance, observation and prior estimation of corresponding covariance, and obtaining error information through system reconstruction; based on a minimum fuzzy error entropy criterion and error information, a posteriori estimate of the state and the corresponding covariance is calculated. Through the implementation of the method, the fuzzy membership degree is introduced to adaptively adjust the weight and derive the minimum fuzzy error entropy unscented filtering model according to different error sample conditions, and a nonlinear system under a complex non-Gaussian noise signal shows good estimation performance, has a good target tracking effect and shows relatively high stability.

Description

technical field [0001] The present invention relates to the technical field of target tracking, and in particular, to a mobile target tracking method, device and computer-readable storage medium. Background technique [0002] The problem of state estimation has attracted much attention for many years, and it plays an important role in the fields of precision guidance, target tracking, and aviation navigation. For linear systems under Gaussian distribution, Kalman Filter (KF) is a classic method. However, for nonlinear systems, KF has the risk of easy divergence. For this, nonlinear filtering methods such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UF) have been proposed one after another. Extended Kalman filtering linearizes nonlinear problems through Taylor expansion, and then uses Kalman filtering for estimation. Extended Kalman filter is suitable for weak nonlinearity, but when nonlinearity is strong, simple linearization estimation will bring large err...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18G06F17/16
CPCG06F17/18G06F17/16
Inventor 李良群陈咏茵
Owner SHENZHEN UNIV
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