Detection and localization of obscured objects from near-field sensor array data: physical models and statistical processing
The problem of near field object detection from array-based electromagnetic induction data is addressed. A forward scattering model based on the Born approximation is employed. Using insight obtained from this model, a clutter model in the form of a state space system is developed which describes the correlation of the noise both across the sensing array and from one position of the array to the next as the measurement device proceeds down track. A multiple-model detection scheme based on the whitening properties of the Kalman filter is employed to perform the actual object detection. This approach is applied to the detection and localization of buried objects. Examples are provided for land mines buried directly in front and off to one side of the array.