<title>Coordinate transformation bias in target tracking</title>

Seemingly benign transformations of data from one coordinate system to another can introduce bias errors resulting from nonlinearities in the underlying conversion equations. These biases, unless corrected, can affect the statistical fidelity of parameter estimates. This paper examines the effect of such biases on the 3D tracking of targets - specifically, the transformation from sensor measurements coordinates to Cartesian x-y-z coordinates. The standard approach to correcting for bias errors involves the adjustment of transformed measurements by the estimated biases. This estimation procedure, however, varies with the assumed relationship between the distributions governing the sensor measurement and the 'true' target position. At one extreme, the measurement can be considered fixed and the 'true' position varied; at the other extreme, 'truth' is fixed and the measurement varied. These two options are compared and contrasted with various hybrid alternatives that impose variable distributions on both the measurement and 'truth'.