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State estimation method based on high-order unscented Kalman filtering

An unscented Kalman and state estimation technology, applied in the field of nonlinear filtering and target tracking, can solve the problems of small amount of calculation and limited precision

Inactive Publication Date: 2013-09-04
HARBIN ENG UNIV
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  • Abstract
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Problems solved by technology

Studies have shown that the estimation results given by UKF are more accurate than EKF, and it can reach the estimation accuracy of second-order EKF, but the calculation amount is much smaller than that of second-order EKF
[0004] At present, the UKF used in the field of target tracking is the second-order UKF method. For Gaussian nonlinear systems, the estimation accuracy of the second-order UKF can only reach the cubic Taylor expansion of the nonlinear function, and the accuracy is limited. For some estimation accuracy requirements For higher occasions, the second-order UKF is no longer applicable

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specific Embodiment approach 1

[0078] Specific implementation mode 1. Combination figure 1 This embodiment is specifically described. A state estimation method based on a high-order unscented Kalman filter described in this embodiment includes the following steps:

[0079] Step 1: Establish state equations and measurement equations describing the target tracking system;

[0080] Step 2: Select the high-order UKF performance parameter κ according to the state dimension of the target tracking system;

[0081] Step 3: one-step state prediction, obtain the sigma point for one-step state prediction through high-order unscented transformation (Unscented Transformation, UT), and propagate the sigma point through the state equation to obtain the sample point of one-step state prediction, for one-step The sample points of the state prediction are weighted and calculated to obtain the one-step state prediction estimation and the one-step state prediction estimation error covariance matrix;

[0082] Step 4: One-step...

specific Embodiment approach 2

[0084] Embodiment 2. The difference between this embodiment and the state estimation method based on high-order unscented Kalman filter described in Embodiment 1 is that the establishment of the state equation and measurement describing the target tracking system described in Step 1 The equation is:

[0085] x k = f ( x k - 1 ) + n k - 1 z k = ...

specific Embodiment approach 3

[0087] Specific implementation mode three, combination figure 2 Describe this embodiment in detail. The difference between this embodiment and the state estimation method based on high-order unscented Kalman filter described in Embodiment 1 or Embodiment 2 is that in step 2, according to the dimension of the state of the target tracking system The specific process of numerically selecting the high-order UKF performance parameter κ is as follows:

[0088] When obtaining the optimal value of the performance parameter κ, the sigma point must be able to capture the 4th order moment information of the prior random vector to obtain the quadratic equation of the performance parameter κ:

[0089] (n-1)κ 2 +(2n 2 -14n)κ+n 3 -13n 2 +60n-60=0

[0090] Solve the performance parameter κ, when the dimension n=2 of the state of the system, the performance parameter κ=0.835; when the dimension n=3 of the state of the system, the performance parameter κ=1.417; now the method provided by ...

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Abstract

The invention relates to a state estimation method based on high-order unscented Kalman filtering. A high-order unscented Kalman filter is used for finishing the state estimation task in the target tracking process. According to the state estimation method based on the high-order unscented Kalman filtering, the state estimation task in the target tracking process is finished by the high-order unscented Kalman filter. In the target tracking process, the state equation and the measurement equation of target tracking are established; a sigma point required for the target tracking filter is obtained by high-order unscented transformation, and the weight of the sigma point is calculated; and the state estimation is obtained by iterating the sigma point and the weight of the sigma point to realize the real-time tracking of the target. The tracking precision of the state estimation method is higher than those of the existing target tracking methods based on other filters, a proper performance parameter k is selected to further improve the precision of the proposed high-order unscented Kalman filtering (UKF) target tracking method, and the high-precision real-time tracking to the target is realized. The state estimation method disclosed by the invention is applied to the technical field of the target tracking.

Description

technical field [0001] The invention belongs to the technical field of nonlinear filtering and target tracking, and in particular relates to a state estimation method based on high-order unscented Kalman filtering. Background technique [0002] In the process of target positioning and tracking, the observation station can usually only obtain target azimuth information containing noise. For example, when using radar to observe air targets, the radar can obtain the azimuth angle of the air target relative to itself, but the observation contains noise. Assuming that the target is flying at a constant speed, it is necessary to estimate the position of the target so as to realize the tracking of the target. At this time, the azimuth observation of the radar in the observation equation is a nonlinear function of the target position parameters to be estimated, and the motion state of the target cannot be obtained directly by the linear filtering method. Common problems in research...

Claims

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Application Information

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IPC IPC(8): G01S13/66
Inventor 张勇刚黄玉龙武哲民李宁王程程周广涛王国臣高伟
Owner HARBIN ENG UNIV
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