Multiple fading factor-based adaptive target tracking filtering method

A fading factor and target tracking technology, applied in the field of target tracking, can solve the problems of complex adaptive estimation of fading factor and poor target tracking accuracy, and achieve high target tracking accuracy, divergence prevention, and simple calculation

Active Publication Date: 2018-01-09
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0004] The present invention provides an adaptive target tracking filtering method based on multiple fading factors in order to solve the p

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  • Multiple fading factor-based adaptive target tracking filtering method
  • Multiple fading factor-based adaptive target tracking filtering method
  • Multiple fading factor-based adaptive target tracking filtering method

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specific Embodiment approach 1

[0020] Specific implementation mode one: as figure 1 As shown, an adaptive target tracking filtering method based on multiple fading factors provided in this embodiment is specifically carried out according to the following steps:

[0021] Step 1, establish the state model and measurement model of the moving target, mark the motion state of the state model as an N-dimensional motion state vector X, and mark the measurement value of the measurement model as an M-dimensional measurement vector Z, according to the motion model Obtain the state error covariance matrix P and the process noise covariance matrix Q, and obtain the measurement noise covariance matrix R according to the measurement model;

[0022] Step 2: Obtain the motion state vector X(k) and the state error covariance matrix P(k) at the kth moment, and calculate the predicted value of the motion state vector of the moving target at the k+1 moment according to the Kalman recursion formula Measure Vector Prediction ...

specific Embodiment approach 2

[0026] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the motion model of the target in step 1 is:

[0027] X(k+1)=F(k)X(k)+G(k)V(k)

[0028] Among them, F(k) is the N×N order state transition matrix, G(k) is the N×M dimensional process noise distribution matrix, V(k) is the M dimensional process noise, which is Gaussian white noise with zero mean, The process noise covariance matrix is ​​Q(k).

specific Embodiment approach 3

[0029] Specific implementation mode three: the difference between this implementation mode and specific implementation mode two is: the measurement model of the target in step one is:

[0030] Z(k)=H(k)X(k)+W(k)

[0031] Among them, H(k) is the measurement matrix of order M×N, W(k) is the measurement noise of M dimension, which is Gaussian white noise with zero mean, and the measurement noise covariance matrix is ​​R(k).

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Abstract

The invention relates to an adaptive target tracking filtering method, in particular, a multiple fading factor-based adaptive target tracking filtering method and belongs to the target tracking field.The method includes the following steps that: step 1, the state model and measurement model of a moving object are established; step 2, a moving state vector and a state error covariance matrix are initialized, the moving state vector predicted value, measurement vector predicted value, innovation, innovation covariance and the estimated value of the innovation covariance of the target are calculated; step 3, an exponential weight factor and multiple fading factors are calculated; step 4, a state prediction covariance matrix, Kalman gain, a filtering value and filtering covariance are calculated; and step 5, the step 2 to the step 4 are executed repeatedly until target tracking terminates. According to the method, the calculation of the fading factors is simple; filter divergence can be prevented when a system model is unknown or noise statistical information is inaccurate; and target tracking accuracy is improved. The method of the invention can be applied to radar target tracking.

Description

technical field [0001] The invention relates to an adaptive target tracking filtering method, which belongs to the field of target tracking. Background technique [0002] In the field of target tracking, Kalman filter is the most widely used tracking filter algorithm. The Kalman filter obtains the optimal estimation of the target motion state in the sense of minimum mean square error by modeling the motion process of the moving target and the sensor measurement process. However, the Kalman filter is only optimal if its mathematical model is known. The establishment of the mathematical model of the Kalman filter includes the establishment of the state equation, the measurement equation and the determination of the initial state estimate, the initial covariance, and the statistical characteristics of the process noise and measurement noise. However, in practical applications, it is quite difficult to establish a filtering model accurately, and the noise statistical character...

Claims

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Application Information

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IPC IPC(8): G01S7/02G01S13/66
Inventor 位寅生王伟李宏博
Owner HARBIN INST OF TECH
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