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Bayesian dynamic estimation algorithm of nonlinear or non-Gaussian distribution system

A non-Gaussian distribution, dynamic estimation technology, applied in the field of Bayesian dynamic estimation algorithm, can solve the problem of particle degradation and increase the invalid operation of the system.

Pending Publication Date: 2022-04-12
FUDAN UNIV
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Problems solved by technology

However, in the particle filter algorithm, there will be a problem of particle degradation, which increases the invalid operation of the system

Method used

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  • Bayesian dynamic estimation algorithm of nonlinear or non-Gaussian distribution system
  • Bayesian dynamic estimation algorithm of nonlinear or non-Gaussian distribution system
  • Bayesian dynamic estimation algorithm of nonlinear or non-Gaussian distribution system

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Embodiment Construction

[0031] The present invention is described below through specific implementation examples.

[0032] As an application of this algorithm, we combine range and Doppler shift based target position and velocity estimates. There is a moving target to be estimated, and the position and velocity information of the target is unknown. At each time i, there is a known position information as The sensor observes the target and obtains the relative distance d between the target and the sensor i and Doppler shift Δf i . We will use the above information to estimate the actual position x of the target i with velocity y i Make an estimate.

[0033] make Then the relative distance and Doppler frequency shift between the target and the sensor are:

[0034]

[0035]

[0036] Among them, f c is the carrier frequency, c is the propagation velocity of the carrier, and Both are Gaussian white noise and independent of each other.

[0037] due to f cand c are both constants in a...

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Abstract

The invention belongs to the technical field of state tracking and prediction of a dynamic system, and particularly relates to a Bayesian dynamic estimation algorithm of a nonlinear or non-Gaussian distribution system. Firstly, a state model and an observation model are established for a to-be-estimated target; secondly, performing maximum likelihood estimation on the target by using the model to obtain an estimated value of the target state at the current moment; taking an estimation result and a corresponding Cramer-Rao limit as intermediate quantities, and substituting the intermediate quantities into a state model at the next moment, thereby completing a round of estimation and prediction; the iteration process is carried out; and dynamic estimation of the target is realized. The algorithm is suitable for a nonlinear signal model or non-Gaussian noise. Simulation results show that the algorithm can effectively track a target in a nonlinear state, and the estimation precision of the algorithm is superior to that of a standard extended Kalman filtering and particle filtering algorithm.

Description

technical field [0001] The invention belongs to the technical field of state tracking and prediction of dynamic systems, and in particular relates to a Bayesian dynamic estimation algorithm. Background technique [0002] The state prediction and tracking of dynamic systems are widely used. The most typical application is the positioning and navigation of vehicles or aircraft, including the automatic driving technology that has been widely concerned in recent years. The most commonly used state estimator is the extended Kalman filter [1]. However, for systems with highly nonlinear or non-Gaussian distributions, the standard extended Kalman filter limits the estimation results. In recent years, the particle filter algorithm has been widely used [2], which not only can better solve nonlinear and non-Gaussian problems, but also has robustness. However, in the particle filter algorithm, there will be a problem of particle degradation, which increases the invalid operation of th...

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Application Information

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IPC IPC(8): G06F17/18G06N7/00
Inventor 蒋轶梁鑫
Owner FUDAN UNIV
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