A MATH MODEL FOR COMPUTING NOISE VARIANCE MATRICES FOR A SYSTEM OF RADAR TRACKERS.
ARMY TEST AND EVALUATION COMMAND WHITE SANDS MISSILE RANGE N MEX DEPUTY FOR NATIONAL RANGE OPERATIONS
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The paper presents a method of calibrating noisy sensors using an unweighted least-squares procedure in a vector space setting and using orthogonal projections to implement the least-squares criterion. Using the least-squares estimate of the parameters that characterize the sensors, a variance matrix of the noise on each sensor is then computed. The method is illustrated by an analysis of both a scalar process e.g., output of amplifiers and a multi-variable process e.g., output of amplifiers and a multi-variable process e.g., radars tracking an object. The scheme, it should be pointed out, is independent of the inputs to the system under study and hence provides a powerful tool for analysis. Finally, derivations are made in a vector-space setting employing vector-matrix techniques and using an adapted version of the notation of Dirac and Friedman. The topics covered include such items as linear transformations, vector spaces, vector packaging, variance analysis of vector multi-variable processes, least squares via orthogonal projections, and others. Author
- Active and Passive Radar Detection and Equipment