Efficient multi-resolution space-time adaptive processor

DETAILED DESCRIPTION The need in the art is addressed by the image processing system and method of the present invention.
In accordance with the inventive method, adapted for use in an illustrative image processing application, a first composite input signal is provided based on plurality of data values output from a sensor in response to a scene including a target and clutter.
The composite signal is processed to provide a plurality of tap weights.
The tap weights are generated by the matrix of data values which is first filtered by a wavelet transform to provide a set of coefficients.
The coefficients are sparsened to provide a sparse matrix.
The sparse matrix is then inverse wavelet transformed to provide the tap weights.
Finally, the tap weights are applied to the composite signal to yield a clutter reduced output signal.
In the illustrative implementation, the matrix is a covariance matrix.
However, a method for implementing the teachings of the invention in the data domain is also disclosed.
In the illustrative implementation, the sparsed matrix is inverted and a set of steering vectors is applied to create the tap weights.
The invention affords an enhanced Signal-to-Interference+Noise Ratio (SINR) because (i) wavelets provide better bases for nonstationary processes and therefore offer improved sample support performance and (ii) coefficient thresholding in wavelet domain removes noisy data that is difficult to estimate.