Multi-sensor multi-target tracking error estimation method

A multi-target tracking and multi-sensor technology, which is applied in the field of multi-sensor multi-target tracking bias estimation to achieve the effect of effectively estimating and realizing bias

Active Publication Date: 2016-10-12
HARBIN INST OF TECH
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AI Technical Summary

Problems solved by technology

At present, there is no effective method for estimating the sensor bias for the case where there is no local sensor Kalman gain in the fusion center

Method used

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Embodiment approach

[0032] figure 1 A flow chart of a multi-sensor multi-target tracking bias estimation method according to an embodiment of the present invention is shown. Such as figure 1 As shown, according to an embodiment of the present invention, using the multi-sensor multi-objective bias estimation method to realize k=1, 2, the specific steps of the bias estimation at the time of L are:

[0033]First, in the first step S101, use the inverse Kalman filter to restore the local sensor measurement value u s (k|k) and its covariance matrix U s (k|k), the specific formula is as follows:

[0034]

[0035]

[0036]

[0037] A s (k|k)=P s (k|k')[D s (k|k)] -1 (4)

[0038] D. s (k|k)=P s (k|k')-P s (k|k) (5)

[0039]

[0040] Since this article involves some formulas, in order to facilitate understanding, the meanings of the symbols involved are summarized as follows: In this article, b refers to the deviation vector, f refers to the meaning of fusion in English, t refers ...

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Abstract

The invention discloses a multi-sensor multi-target tracking error estimation method, comprising: a reducing step: reducing a measured value us(k / k) of a local sensor and a covariance matrix Us(k / k) thereof; a Kalman gain acquisition step: acquiring a Kalman gain Ws, k via the acquired covariance matrix of the measured value of the local sensor; an error virtual measurement and acquisition step: acquiring a virtual measurement (the formula is as shown in the specification) of an error vector via the Kalman gain; a fusion error vector virtual measurement and acquisition step: acquiring fusion error vector virtual measurement according to the virtual measurement of the error vector; and an error estimation vector and error estimation covariance matrix acquisition step: acquiring an error estimation vector and an error estimation covariance matrix according to the virtual measurement (the formula is as shown in the specification) of the error vector, the fusion error vector virtual measurement (the formula is as shown in the specification) and the error estimation vector and the error estimation covariance matrix at the last moment. By adopting the method disclosed by the invention, the error can be effectively estimated.

Description

technical field [0001] The invention relates to a target tracking deviation estimation method, in particular to a multi-sensor multi-target tracking deviation estimation method. Background technique [0002] Generally speaking, since the biased sensor cannot register its own deviation, it needs a comparison object, so the registration of the sensor requires two or more sensors. [0003] In Friedland B.Treatment of bias in recursive filtering[J].IEEE Transactions on Automatic Control,1969,14(4):359-367. Treat the bias registration problem as a two-sensor problem and extend the state vector with an unknown bias , the disadvantage of this method is that the amount of calculation will increase significantly when the dimension of the expansion vector increases. As described in Nabaa N, Bishop R H.Solution to a multisensor tracking problem with sensor registration errors[J].IEEE Transactions on Aerospace&Electronic Systems,1999,35(1):354-363. Most bias estimation algorithms act d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/17G06F17/16G06K9/62
CPCG06F17/16G06F17/17G06F18/251
Inventor 周共健谢青青
Owner HARBIN INST OF TECH
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