Abstract
For mobile radar, offset biases and attitude biases influence radar measurements simultaneously. Attitude biases generated from the errors of the inertial navigation system (INS) of the platform can be converted into equivalent radar measurement errors by three analytical expressions (range, azimuth, and elevation, resp.). These expressions are unique and embody the dependences between the offset and attitude biases. The dependences indicate that all the attitude biases can be viewed as and merged into some kind of offset biases. Based on this, a unified registration model (URM) is proposed which only contains radar “offset biases” in the form of system variables in the registration equations, where, in fact, the “offset biases” contain the influences of the attitude biases. URM has the same form as the registration model of stationary radar network where no attitude biases exist. URM can compensate radar offset and attitude biases simultaneously and has minor computation burden compared with other registration models for mobile radar network.
Highlights
With the increasing demands of the navigation accuracy in military and civilian applications, it is vitally important to fuse all the information from different sensors to obtain accurate target location estimate and comprehensive attribute information
Both unified registration model (URM) and optimized bias estimation model (OBEM) algorithms are tested and compared in a simulated scenario where a common track is generated for two radars which are installed on different ships
As for AAM [1], it has been compared with OBEM in [1] and the estimation performance is poorer than OBEM, especially for the elevation biases
Summary
With the increasing demands of the navigation accuracy in military and civilian applications, it is vitally important to fuse all the information from different sensors to obtain accurate target location estimate and comprehensive attribute information. These criteria use the singularity of matrix to identify the observability of the system (OoS) and US in an optimal situation because when dependent variables are contained in the system, CM of the registration equations is singular. The attitude bias conversion model (ABCM) [7] is proposed to explicitly establish nonlinear registration equations using linear dependencies among all biases Both models can be improved with deeper understanding of the relationships between OBs and ABs. The linearization-caused estimate errors were analyzed in [7], where it was proved that these errors are minor and can be omitted for MRR model.
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