REAL-TIME ESTIMATION AND RANDOM SIGNAL DETECTION.
NAVAL POSTGRADUATE SCHOOL MONTEREY CA
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This work investigates some problems arising in application of Kalman linear filter theory to real problems, where practical estimates must replace exact theoretical quantities in problem formulation. The principle objective is application of linear filter theory to random signal detectionclassification. However, an example of classical estimation, error estimation in shipboard inertial navigation systems, is offered to illustrate general points discussed. A unified treatment of models for random time series is presented, including a comparative review of models which have been proposed and procedures for obtaining model coefficients. Correlation detection of deterministic signals is discussed and the resulting principles extended to the case of random signal detection. Application of linear filter theory to the problem is indicated. Finally, an experimental study in random sgnal detectionclassification is included. Experimental signals used are hydrophone recordings of sea noise and sea noise plus diesel submarine. Consistency of successful results obtained suggests practical utility of method in certain random signal detectionclassification problems. Author
- Statistics and Probability