Single out-of-order measurement centralized fusion method of arbitrary delay steps of a nonlinear system

Abstract

The invention discloses a single out-of-order measurement centralized fusion method of an arbitrary delay step of a nonlinear system. The generalized Gaussian smoothing method is used to solve a smoothing probability density function of the arbitrary nonlinear system, and the particle filter algorithm is used to achieve update. Firstly, it is determined that the measurement is sequential or not, asequential posterior state is obtained by using the particle filter algorithm if so, otherwise, out-of-order measurement fusion is performed, i.e., the generalized Gaussian smoothing method is used to solve the smoothing probability density function at the generation time of the out-of-order measurement, then the smoothing probability density function and a local likelihood function are combinedto solve an asynchronous likelihood function, and finally, the asynchronous likelihood function is used to update the sequential posterior state at the previous time to obtain the fusion posterior state, and the fusion posterior state represented by particle samples is approximately stored as Gaussian parameters, which effectively solves the problem that the out-of-order measurement data in asynchronous sensor networks are difficult to be fused due to data processing and communication delay.

Description

technical field [0001] The invention belongs to the technical field of multi-sensor data fusion, and in particular relates to a single out-of-sequence measurement centralized fusion method for arbitrary delay steps of a nonlinear system. Background technique [0002] Using multiple sensors to work together can improve system reliability and robustness, expand time and space coverage, and improve target detection performance. With the rapid development of information processing and network communication technologies, multi-sensor data fusion technology has received extensive attention . Among them, in the centralized data fusion method, each local sensor sends local measurements to the fusion center for processing, there is no information loss, and the optimal global estimation value can be obtained, so in the area monitoring, target tracking, target detection and positioning It has been widely used in many fields. [0003] In the centralized fusion system, due to the diffe...

AI Technical Summary

Problems solved by technology

[0003] In the centralized fusion system, due to the different data processing time and communication transmission time of each sensor, there is a random delay in the local measurement of each sensor arriving at the fusion center, resulting in out-of-sequence measurements from the same target, that is, out-of-sequence measurement problems

A common solution is to ignore out-of-sequence measurements, but this method usually degrades tracking performance, especially when out-of-sequence measurements occur frequently or come from local sensors with higher accuracy, directly ignoring out-of-sequence measurements will seriously affect Fusion accuracy

The literature "An optimal multi-delay out-of-order measurement processing algorithm, Systems Engineering and Electronic Technology, 2010, 31(11): 2592-2596" is based on the idea of ​​optimal linear fixed-point smoothing, directly using out-of-order measurement to update the current time State estimation effectively solves the problem that multi-step delayed out-of-sequence measurements are difficult to update and fuse, but this method is based on the Kalman filter algorithm and is only suitable for dynamic systems with weak linear or nonlinear strength; the literature "Out-of-sequencemeasurement processing for particle filter: exact bayesian solution, IEEETrans.Aersp.Electron.Syst., vol.48, no.4, pp.2818-2831, 2012" proposed a particle filter algorithm based on deterministic Bayesian method (A -PF algorithm), this method is suitable for general nonlinear systems, and when the number of particles is large enough, this method is approximately optimal and has high tracking accuracy. Large, storage requirements are too high, only suitable for low-dimensional or non-real-time scenarios

Images