Title :
Use of idempotent matrices to validate linear systems software
Author_Institution :
MIT, Lincoln Lab., Lexington, MA, USA
fDate :
11/1/1990 12:00:00 AM
Abstract :
In seeking to verify various computer implementations of Kalman filters, the extended Kalman filter (EKF), or other algorithms that rely on the same fundamental computations in a variety of different applications, it is useful to first validate software performance using low-dimensional test problems of known solution. Besides reporting the state-of-the-art in computing the critical fundamental linear system building blocks in a simple manner that is easy to understand, the author describes the easy construction of nontrivial test cases of known solution, provides a rigorous demonstration of how to augment/concatenate several low-dimensional test problems of known solution to comprise a higher-dimensional system of known solution that is also to be used in software test and validation/verification, and presents the computation of a closed-form exact optimal control solution that can also be used to validate software
Keywords :
Kalman filters; computerised signal processing; program verification; closed-form exact optimal control; computerised signal processing; critical fundamental linear system; extended Kalman filter; idempotent matrices; linear systems software; low-dimensional test; program verification; software test; software validation; Application software; Calibration; Covariance matrix; Linear systems; Software performance; Software systems; Software testing; Sonar navigation; System testing; White noise;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on