This paper examines the determination of sensor biases by comparing multiple track outputs from spatially disparate platforms. In many cases, this can be achieved using least-squares methods, minimising the summed-squares of track differences. This method, while simple, is shown to provide valuable information concerning the observability of a given type of bias. Observability is here defined as permitting (in principle) the determination of a unique value for that bias, for each sensor in a pair (to take the simplest case), given sufficient measurements pairs. For the purposes of this study, the types of bias are divided into three main classes: measurement, own-position and alignment. In general, only members of the first group are individually observable, while for the other two classes observable combinations of the individual biases can be derived. The least-squares method also provides an effective method for assessing observability in practice (for example, in certain sensor-track configurations), and in determining which estimated biases are most reliable in such circumstances.
展开▼
