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Bias CRLB in Sine Space for a Three-Dimensional Sensor
IEEE Transactions on Aerospace and Electronic Systems ( IF 2.797 ) Pub Date : 2019-05-20 , DOI: 10.1109/taes.2019.2917986
Michael Kowalski; Yaakov Bar-Shalom; Peter Willett; Djedjiga Belfadel; Fred Daum

As bias estimation methods are developed, it becomes necessary to obtain the bound on bias estimation for more complex bias and sensor models. Three-dimensional (3-D) sensors, such as radars commonly used in applications, contain both scale and additive biases in sine space which result in a nonlinear estimation problem that may have poor observability and accuracy depending on the geometry of the sensors. By converting the sine space and range measurements to Cartesian using an unbiased conversion, it is possible, via creation of pseudomeasurements, to eliminate the need to estimate the target's state thereby reducing the sensor bias estimator complexity. The present paper evaluates the Cramér–Rao lower bound (CRLB) for estimating scale and additive biases in sine space for 3-D sensors and compares it with a maximum likelihood formulation implemented via iterated least squares, which is thereby shown to be statistically efficient. Additionally, the importance of measurement diversity is investigated with respect to the CRLB.
更新日期:2020-02-11

 

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