Hermite function constraint-based data fusion calculation method

Abstract

The invention provides a Hermite function constraint-based data fusion calculation method, comprising the following steps: representing a target track parameter by using a Hermite function, representing an original measurement element provided by a measurement system by taking the target track parameter as a base function so as to obtain an error equation of the original measurement element; forming combined observation equations by the obtained error equations of the original measurement element, and carrying out integrated calculation on the combined observation equations according to the least squares criterion so as to obtain a vector value taking a Hermite fitting function coefficient and a measurement element system error model coefficient as to-b-estimated parameters; inserting the obtained vector value of the to-be-estimated parameters into the Hermite fitting function and the measurement element system error model, so as to obtain a target track coordinate parameter value, a speed parameter value and a measurement element system error value. The invention provides a Hermite function constraint-based data fusion calculation method. By adopting the method, the structure of the mathematical model is simplified when the estimation accuracy, the calculation stability and the calculation efficiency of the target track parameter are ensured.

Description

technical field [0001] The invention relates to the field of measurement technology, in particular to a data fusion calculation method based on Hermite function constraints. Background technique [0002] The measurement accuracy of the moving target trajectory directly affects the results of engineering test appraisal, and also affects the ability to support the fault analysis and improved design of the measurement system and the system under test. Therefore, measurement accuracy has always been the goal pursued tirelessly in the field of measurement. As the maneuverability of the moving target to be measured becomes more and more complex, a comprehensive network measurement and control system has been built in a targeted manner, covering a variety of measurement systems, including optical theodolite, photographic theodolite, pulse radar, continuous wave radar, phased array radar and space Locating telemetry receiving stations, etc. The massive raw measurement data provide...

AI Technical Summary

Problems solved by technology

The classic error model optimal ballistic estimation (EMBET) data fusion method considers that the target trajectory has no correlation in time series, that is, the trajectory parameter fusion solution is performed at each sampling moment. This method does not make full use of the target trajectory time series. Relevance makes the design matrix huge, consumes computing resources, has a long data processing cycle and low efficiency

On the basis of this theory, many scholars have proposed data fusion methods based on spline function constraints and dynamic constraints, etc. The purpose is to compress the space dimension of the estimated parameters of the target trajectory and enhance the joint equation constraints and error detection. Sensitivity, improve target trajectory parameter estimation accuracy, calculation stability and calculation efficiency, and have a large number of engineering applications to prove, but the mathematical model construction is relatively complicated

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