Combined target positioning and sensor registration method in multi-source ranging

Abstract

The invention discloses a combined target positioning and sensor registration method in multi-source ranging. The method includes the following steps: by analyzing a measurement model of a multi-source ranging sensor, constructing a likelihood function for the features of white gaussian noise by using the measurement noise of the sensor, analyzing, deducing and resolving the likelihood function byusing the maximum likelihood method, and obtaining a target fusion position and the analytical solution of the ranging sensor by performing registration error estimation. The method combines the IMMalgorithm and the maximum likelihood algorithm, on the basis of a previous registration error estimation value of the sensor, can obtain the initial position state information of the target by using the IMM algorithm, substitutes the initial position state information to an estimation formula for registration error, and obtains a registration error estimation value. The registration error estimation value which is obtained from the calculation is further substituted to a next time iteration, by configuring iteration times or a smaller threshold value, the registration error estimation value isconverged, and the registration error estimation value is eventually substituted to the converged registration error estimation to obtain a precise target position fusion value.

Description

Technical field [0001] The invention belongs to the technical field of navigation and positioning and relates to a method for joint target positioning and sensor registration under multi-source ranging. Background technique [0002] The multi-sensor spatial registration is mainly to eliminate the system deviation of the sensor, so as to improve the positioning accuracy of the target. At present, the methods for spatial registration mainly include sequential processing methods and batch processing methods. The sequential processing methods mainly include online estimation based on filtering technology, joint online estimation algorithms and decoupled online estimation algorithms, which have a small amount of calculation. Batch registration methods mainly include least square method, generalized least square method, maximum likelihood method and exact maximum likelihood method. This type of algorithm requires centralized processing of data over a period of time, so the amount of c...

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Problems solved by technology

[0004] Under the condition that there is only a certain measurement information in multiple sensors, such as the measurement information only has ranging information, the above methods often cannot perform registration or the estimation effect is not ideal, because, with only ranging information, it is impossible to construct Equivalent measurement equation, so that it is impossible to filter or calculate the deviation in batch processing, or the amount of information is less, resulting in poor estimation effect when performing filtering estimation

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