Lockheed Martin Corporation

Lockhead Martin Corp.

Teranex, Inc.

Surachai Sutha

Hai-Wen Chen

Teresa Olson

Andrew P Abercrombie

Wlodzimierz Holsztynski

Dan T Long

A method for identifying potential targets as far away as possible is disclosed. In a simple background scene such as a blue sky, a target may be recognized from a relatively long distance, but for some high clutter situations such as mountains and cities, the detection range is severely reduced. The background clutter may also be non-stationary further complicating the detection of a target. To solve these problems, target detection (recognition) of the present invention is based upon temporal fusion (integration) of sensor data using pre-detection or post-detection integration techniques, instead of using the prior art technique of fusing data from only a single time frame. Also disclosed are double-thresholding and reversed-thresholding techniques which further enhance target detection and avoid the shortcomings of the traditional constant false alarm rate (CFAR) thresholding technique. The present invention further discloses improved spatial fusion techniques for target detection (recognition) employing multiple sensors instead of employing the more conventional single sensor techniques. If spatial fusion is implemented with more than three sensors, then target detection can be enhanced by also using post-detection techniques. Moreover, since the pre-detection and the post-detection technique are complementary to each other, a combination of these two integration techniques will further improve target detection (recognition) performance.

Target detection improvements using temporal integrations and spatial fusion

A fast method with logarithmic complexity for image compression and expansion is provided, implementable on the Geometric Arithmetic Parallel Processor (GAPP) or any similar fine-grained, mesh-connected Single Instruction-Multiple Datastream (SIMD) array processor. Efficient decomposition of the original image into multiple subimages enables the method to exploit the parallelism of the SIMD computer for implementing powerful multiresolution pyramid and hierarchical image processing techniques on a massively parallel processor.

Image compression/expansion using parallel decomposition/recomposition

An apparatus for processing data has a Single-Instruction-Multiple-Data (SIMD) architecture, and a number of features that improve performance and programmability. The apparatus includes a rectangular array of processing elements and a controller. The apparatus offers a number of techniques for shifting image data within the array. A first technique, the ROLL option, simultaneously shifts image planes in opposite directions within the array. A second technique, the gated shift option, makes a normal shift of an image plane to neighboring PEs conditional, for each PE, upon a value stored in a pattern register of each PE. A third technique, the carry propagate option, combines the computations from multiple PEs in order to complete an n-bit operation in fewer than n clocks by forming &ldquo;supercells&rdquo; within the array. The apparatus also includes a multi-bit X Pattern register and a multi-bit Y Pattern register. These registers have bit values corresponding to respective columns (for the X Pattern register) and rows (for the Y Pattern register) of the array. Patterns can be propagated from these registers into corresponding rows and columns. Further these registers can be used to receive values representing the logical OR of signals generated by individual PEs within respective rows and columns. Further, a number of global data registers are used to store information which can be broadcast back into the processing array.

Input/output support for processing in a mesh connected computer

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Orlando, FL, United States of America

Surachai Sutha