Image analysis – Image compression or coding – Pyramid – hierarchy – or tree structure
Reexamination Certificate
1998-10-19
2001-09-18
Chen, Wenpeng (Department: 2624)
Image analysis
Image compression or coding
Pyramid, hierarchy, or tree structure
C342S090000, C382S103000
Reexamination Certificate
active
06292592
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to radar and electro-optic image data processing systems and techniques. More specifically, the present invention relates to space time adaptive array processing systems and techniques for use in radar and electro-optic image data processing applications.
2. Description of the Related Art
Active and passive imaging technologies are employed in diverse applications where there is a need to track an object as it moves through space. In military and commercial applications, for example, radar is often used to track targets and other aircraft. Electro-optic technologies including laser based systems are also used for such applications.
Unfortunately, as is well known in the art, tracking of targets including aircraft and spacecraft using radar and electro-optic techniques and the tracking of vessels using sonar may be problematic due to the presence of clutter and other sources of interference. Clutter is often due to the detection of objects other than a desired target and may result from natural as well as artificial objects. Further, the clutter may vary in size and number and may be static or dynamic. Interference may be intentional and, if so, it may be designed to overpower the sensing technology or it may be designed to cause a malfunction or misread of a true target location.
Accordingly, the elimination of clutter and other sources of interference has received considerable attention from designers of passive and active tracking systems. One technique which offers the promise of being of use for such applications is called space time adaptive array processing or “STAP”. Considered for use with systems that employ an array of sensing elements, STAP would involve the creation of a covariance matrix in the vicinity of the target. The covariance matrix would be used to provide an estimate of the clutter. The estimate would then be used to remove the clutter in a gate around the target.
STAP attempts to suppress spatio-temporal interference, hence covariance matrix estimates must be updated in real time to handle rapidly changing interference statistics. Unfortunately, clutter is often nonstationary due to movement or jamming. As a result, the steps of estimating the covariance matrix and canceling the clutter using STAP would be computationally intensive. Hence, the data processing requirements for current applications are often considerable, e.g., on the order of ten billion floating point operations per second (10 GFLOPS).
Accordingly, there is a need in the art for an efficient system and method for processing sensor outputs to eliminate clutter and interference for current and future military, commercial and industrial applications.
SUMMARY OF THE INVENTION
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.
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Braunreiter Dennis C.
Chen Hai-Wen
Schmitt Harry A.
Chen Wenpeng
Collins David W.
Lenzen, Jr. Glenn H.
Raytheon Company
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