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Modified Kalman target detection algorithm applied to metal detection

We discuss an improved Kalman filter-based algorithm for automatic detection of targets from metal detector data. This innovations process utilizes the difference between measurements and single-stage predicted values. In our previous work a Kalman filter based algorithm was used to detect targets assuming that the metal detector output signal is a constant in the background. In this work we extend the capability of this method to detect targets by assuming the distribution of the metal detector output data is Gaussian. The analysis has been extended by computing state estimation errors, covariance matrices and treating metal detector background data as a discrete-time Gauss-Markov random sequence. The proposed detection algorithms have been applied to Minelab F1A4-MIM metal detector data.