LS (Least square)-based multi- model adaptive state estimation method and system

Abstract

An LS (least square)-based multi- model adaptive state estimation method includes the following steps: S1, same moving object observation sequences of sensors are acquired to form moving object X, Y and Z direction observation sequences in the space rectangular coordinate system at different times; S2, the degrees and the number of time-variable polynomial equations are determined, and LS (least square)-based polynomial equation coefficient estimation of the moving object X, Y and Z direction observation sequences is performed respectively; S3, according to the polynomial equations in the step S2, regression sum of squares and residual sum of squares are respectively calculated for F-test to determine a best fit polynomial; S4, a corrected best fit polynomial is determined; S5, a first time partial derivative and a second time partial derivative are sought respectively to obtain moving object corresponding velocity and acceleration polynomials; and S6, object position state estimator coordinate conversion is performed to obtain corresponding position coordinates, and then by combining with velocity estimated values, object state estimation can be completed.

Description

technical field [0001] The invention relates to the technical field of data fusion, in particular to an LS-based multi-model self-adaptive state estimation method and system. Background technique [0002] Multi-sensor data fusion is an automatic information comprehensive processing technology formed and developed in the 1980s. It has developed into a very active and popular research field in national defense, and it is the common concern of multi-disciplinary, multi-department and multi-field High-level common key technology, many countries have listed it as the key technology for the next stage of key development. [0003] One of the main tasks of multi-sensor data fusion is to use multi-sensor data to estimate the state of the target. State estimation is to find the state vector that best fits the observed data through mathematical methods. State estimation mainly refers to position and speed estimation, which includes state estimation of moving targets based on single-s...

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Problems solved by technology

[0004] The state estimation of the maneuvering target belongs to the problem of nonlinear filtering estimation. The basic idea of ​​traditional nonlinear filtering is to approximate the nonlinearity of the system in a parametric analytical form, which inevitably introduces linearization errors. When the error increases to a certain To a certain extent, the accuracy of state estimation will be reduced, and in severe cases, it will lead to problems such as filter divergence

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