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A method for calculating a strong tracking fading factor in a distributed fusion structure
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A technology of fading factor and calculation method, which is applied in the field of strong tracking fading factor calculation, and can solve the problems of computational burden and hindering the application of strong tracking filtering technology.
Active Publication Date: 2019-03-08
ZHEJIANG UNIV
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Since the calculation of the global strong tracking fading factor of the fusion center requires the residual information of all local sensors, which involves high-dimensional matrix operations, it is easy to cause a computational burden
In addition, under the framework of distributed fusion, there is no analytical formula between the global strong tracking fading factor of the fusion center and the strong tracking fading factor of local sensors, which seriously hinders the application of strong tracking filtering technology in distributed fusion systems. application
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[0025] Among them, the subscript i is the sensor label, and k is the discrete time. x(k)∈R n×1 Indicates system status (R n×1 Is the complete set of n-dimensional column vectors), F(k) is the state transition matrix of the system, w(k)∈R n×1 Is the process noise vector and is Gaussianwhite noise with a mean value of zero and a variance of Q(k). z i (k)∈R m×1 Is the measurement vector of the i-th sensor, H i (k) is the measurement matrix of the i-th sensor at time k, v i (k)∈R m×1 Is the measurement noise of the i-th sensor, and the mean value is zero variance is R i (k) Gaussianwhite noise.
[0026] Suppose the initial state of the system is: P(0|0)=p(0), and x(0) is independent of w(k) and v respectively i (k).
[0027] Below, based on figure 2 The flow chart shown details...
[0047] Suppose the state space model of the distributed nonlinear fusion system is:
[0048] x(k+1)=f(x(k))+w(k)
[0049] z i (k)=h i (x(k))+v i (k)
[0050] Where x(k)∈R n×1 Represents the state of the system, f(x(k)) is a nonlinear differentiable function, w(k)∈R n×1 Is the process noise vector and is Gaussianwhite noise with a mean value of zero and a variance of Q(k). z i (k)∈R m×1 Is the measurement vector of the i-th sensor, h i (x(k)) is the nonlinear differentiable function of the i-th sensor at time k, v i (k)∈R m×1 Is the measurement noise of the i-th sensor, and the mean value is zero variance is R i (k) Gaussian white noise.
[0051] Suppose the initial state of the system is: P(0|0)=p(0), and x(0) is independent of w(k) and v respectively i (k).
[0052] The specific implementation steps of the present invention in the nonlinear fusion system are detailed below:
[0053] Step 1: Parameter initialization
[0054] (1.1) Syste...
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Abstract
The invention relates to a method for calculating a strong tracking fading factor in a distributed fusion structure. By introducing a new parameter named fading parameter vector, the present inventionindirectly reveals the analytical relationship between the strong tracking fading factor of the local sensor and the strong tracking fading factor of the fusion center, thereby effectively reducing the calculation amount of the global fading factor calculated by the fusion center. At the same time, the method of the invention is applicable to a linear system and a non-linear system, so that the method is a universal strong tracking fading factor calculation method under the distributed fusion framework, and promotes the application of the strong tracking filter technology in the distributed fusion system.
Description
Technical field [0001] The invention belongs to the field of signalprocessing, and specifically relates to a method for calculating a strong tracking fade factor in a distributed fusion structure. Background technique [0002] The rapid development of sensor technology and computer technology has greatly promoted the research of information fusion technology, information fusion technology application and target detection and tracking, inertial navigation, pattern recognition, robots and intelligent instrument systems, intelligent manufacturing systems, image analysis and understanding, and many other fields . Commonly used information fusionsystem architectures are centralized and distributed. Compared with the centralized structure, the distributed structure has low requirements for communication bandwidth, fast calculation speed, good reliability and continuity. Kalman filter (KF) and a series of derivative methods such as extended Kalman filter (EKF), unscented Kalman filt...
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