Raytheon Company

Hughes Aircraft Company

Other

Hughes Electronics Corporation

Thomas K Lo

Jack M Sacks

Nam D Banh

Timothy Edward Caber

Todd L Baker

Charles A McNary

Wayne P Simoni

Sheldon S Wald

Hien T Dang

Nikki J Lawrence

Harry A Schmitt

Frank N Longo

William J Schmitt

Hai-Wen Chen

Franklin J Meyers

Timothy T Bohn

Stephen M Jensen

Joshua A Whorf

Ronald O White

Andy L Quan

Mark S Moellenhoff

Lynne M Karlin

Harrison Nguyen

Leonard A Lee

Kazuo R Hosokawa

Kelly D Holthaus

Lop G Ng

Deceased Sacks

Teresa A Hinkle

Deborah C Roty

Hagos Teku

Jack M Sacks, Deceased

W Scott Johnston

Methods and apparatus for processing data from multiple sources according to various aspects of the present invention include a sensing system for determining target information. The sensing system includes sensors to generate target information and local trackers to receive the target information from the sensors. The local trackers may generate local target data for the target, and a combiner connected to the local trackers may use the local target data to generate global data for the target.

Methods and apparatus for processing data from multiple sources

A generalized Hough transform is used to acquire and track vehicular targets for missile guidance. This is accomplished by recognizing that most vehicles have silhouettes that may be described as a 'rounded rectangle'. The position and shape of such rounded rectangles is described in terms of 5 parameters (x, y, a, b, &#x3b8;) where x, yare the center coordinates, a,b are the major and minor axis and &#x398; is the orientation. The computation of a five dimensional Hough transform on an image including such a target will produce the five parameters that provide the 'best fit' rounded rectangle to the target. These parameters are then passed to a missile tracker. This capability can be used to improve track gate handoff from the automatic target recognizer (ATR) to the missile tracker, changing aspect ratios of maneuvering targets, limited lock-on after launch (LOAL), aimpoint designation and fire control system to missile seeker handover.

Vehicular target acquisition and tracking using a generalized hough transform for missile guidance

A non-traditional, non-uniformity compensation processor. In the illustrative embodiment, the feedforward adaptive non-uniformity compensation processor includes a multiplier which receives a video image and generates a compensated video image by multiplying the video signal by one of a noise reducing constant or one. The inventive processor further includes a shunting multiplication processor which supplies a selected one of the noise reducing constant or one to the multiplier in response to the presence of one of fixed pattern noise (FPN) and temporal noise (TN) in adjacent frames of the video image. In an illustrative embodiment, the inventive system cones the output of an FPA to generate a coned output signal. The system isolates the FPN in the coned output signal and feedforward processes the coned output signal on a pixel-by-pixel basis to generate a FPN-reduced output signal. In an exemplary embodiment, the feedforward processing step includes the step of multiplying the coned output signal by a predetermined value which is substantially smaller, i.e., approximately zero, than the coned output signal.

Non-traditional adaptive non-uniformity compensation (ADNUC) system employing adaptive feedforward shunting and operating methods therefor

A tracking system controls an aimed device ( ) to keep it aimed at a target ( ). A control system ( ) determines when the tracker has lost its lock on the target ( ) by comparing the target's instantaneous acceleration with its median acceleration. When the difference exceeds a predetermined threshold, the system searches backwards through a chronological buffer until it finds position data which antedate the receipt of inaccurate position data from the tracker. A second-order filter such as a Kalman filter is used to provide estimated target states. The difference between a time updated output from the second-order filter ( ) and the present measured state from the tracking device is low pass filtered to provide a measurement of the instantaneous acceleration of the target. The buffer ( ) stores position, velocity, and median acceleration values covering a span of time at least about twice as long as the time required to determine that a breaklock has occurred.

Breaklock detection system and method

A fuzzy controller ( ) for use in target detection systems. The fuzzy controller ( ) includes a first circuit ( ) for determining a number of false alarms in a frame of data. A second circuit ( ) determines a desired number of false alarms for the frame of data. A third circuit ( ) computes a threshold multiplier factor based on the number of false alarms, the desired number of false alarms, and one or more fuzzy rules. In a specific embodiment, the first circuit ( ) includes a target detection system for providing addresses of pixels whose values are within a predetermined range relative to a detection threshold. The second circuit ( ) includes an input device ( ) for accepting the desired number of false alarms as input to the fuzzy controller ( ). The third circuit ( ) includes a fuzzifier input calculation circuit ( ) that computes a fuzzifier input value based on the number of false alarms detected in a frame and the desired number of false alarms.

Accurate target detection system

A system and method of finding a detection threshold that separates real scene objects seen through an optical system from sensor fixed artifacts. The inventive system continuously scans the sensor so that objects in the scene move in an inverse sense relative to the scanning motion while fixed frame artifacts remain fixed in focal plane coordinate system. The difference in temporal behavior is used to discriminate against fixed frame artifacts. The inventive system finds a detection threshold that maximizes the portion of a target that may be segmented while avoiding setting the threshold so low that it merges spatially adjacent artifacts in with the target.

Imaging system and method for hardbody detection and sensor fixed artifact rejection

An accurate target detection system. The system includes a sensor (,) that receives electromagnetic signals and provides electrical signals in response thereto. A non-uniformity correction circuit (,) corrects non-uniformities in the sensor (,) based on the electrical signals and provides calibrated electrical signals in response thereto. A third circuit (,) determines if a target signal is present within the calibrated electrical signals and provides a target detection signal in response thereto. A fourth circuit (,) selectively activates or deactivates the non-uniformity correction circuit (,) based on the target detection signal. In a specific embodiment, the sensor (,) is an array of electromagnetic energy detectors (,), each detector providing an electrical detector output signal. The non-uniformity correction circuit (,, and,) includes circuit for compensating for gain, background, and noise non-uniformities (,, and,) in the electromagnetic energy detectors. The non-uniformity correction circuit (,, and,) includes a detector gain term memory (,) for storing detector gain compensation values. The detector gain compensation values are normalized by noise estimates unique to each of the detectors. The third circuit (,, and,) includes a signal enhancement circuit for reducing noise (,) in the calibrated electrical signals. The third circuit (,, and,) includes a noise estimation circuit (,) that estimates noise in each of the detector output signals and provides noise estimates in response thereto. The noise estimation circuit (,) further includes a noise estimator circuit (,) and a recursive background estimator (,).

Accurate target detection system for compensating detector background levels and changes in signal environments

A process for reducing background clutter of a scene, and isolating an object of interest. The process includes calculating a minimum output from a plurality of appropriately configured anti-median filters representing a minimum difference processor (MDP) filter. In a first embodiment, the MDP filter includes a (5.times.5) matrix of elements wherein each element has an intensity value representation of the scene. A first anti-median filter array is configured as a horizontal array of 5 pixel elements in which the center element of the matrix is the center element of the horizontal array. A median value of the 5 elements of the horizontal array is determined, and is subtracted from the value of the center element to determine an anti-median value of the horizontal array. The same process is performed for a vertical 5 element anti-median array, and two diagonal 5 element anti-median arrays, all including the center element of the matrix as the center common element of the arrays. The lowest anti-median value is then outputted as the output of the MDP filter. In a second embodiment, the values of the remaining elements of the matrix which were not included in any of the horizontal, vertical or diagonal arrays described above are subtracted from the center element of the matrix. The lowest value from the group of anti-median values together with the remaining subtracted values is outputted from the MDP filter. This enables the background clutter of the scene to be substantially suppressed or eliminated.

Minimum difference processor

A processor and method for discriminating against interference during target acquisition and reacquisition processing of densely cluttered images. The processor and method that adaptively estimates the feature probability density function of the interference from the image data. The estimated interference probability density function, along with target feature estimates are input to a Bayesian classifier that discriminates the interference from the target.

Clutter rejection using adaptive estimation of clutter probability

A relational to object mapping system and method permit the transfer and translation of data between a database server having a relational database and an object-oriented apparatus. The object code, such as C++, is translated by a generator object into suitable relational database statements, such as structured query language (SQL). The SQL statement is passed to a query object which executes the statement. In executing the SQL statement, the query object accesses an environment object which supplies the query object with appropriate connection objects for establishing a link between the query object and the database server. The database server supplies the requested data from the relational database to the query object which then stores the data in a storage buffer. The object-oriented apparatus can then access the data from the storage buffer. The query object may activate a transaction object for processing the data before the data is accessed by the object-oriented apparatus.

System and method for relational to object mapping

Moving objects (36) are detected by processing images through a spatial filter (42) and positional shifter (46), which shifts the image by an amount reflecting a line of sight velocity hypothesis of the objects. The images are stacked together by a stacker 48, causing the intensity of an object moving at the line of sight velocity hypothesis to increase, and other objects to be blurred. The stacked image is spatially filtered (50) to remove the blurred objects and linear artifacts, and the moving objects (36) of interest are selected according to their increased intensities. In a practical system, the images are processed with a range of velocity hypotheses to identify both the object and the true line of sight velocity.

Detection of moving objects

A system for detecting an object in a scene. The invention (10) includes memory (12) for storing a first frame of image data represented by a plurality of input signals I.sub.o. A spatial filter (16) is included for spatially filtering a second frame of image data represented by a plurality of input signals I.sub.o to provide first spatial filter output signals of the form .eta..sub.s I.sub.o. These signals are subtracted from the first frame of image data to provide difference signals of the form .eta..sub.D I.sub.o, where .eta..sub.D represents a measure of object registration. A second spatial filter (18) is included for spatially filtering the difference signals to provide spatially filtered difference signals of the form .eta..sub.s .eta..sub.D I.sub.o. Finally, the signals are processed (30) to extract the measure of object registration .eta..sub.D.

Amplitude ratio discrimination for MTI processors

An improved method for finding the mask threshold for a maskable bilevel correlator operates on a series of frames of information. Beginning with an initial threshold, two rates are computed. The rate of change of the correlation peak magnitude P.sub.(T), a function of threshold T, with respect to the change in the value of the mask threshold T, or (dP.sub.(T) /dT), and the corresponding rate of change of correlation reference pixels with magnitude exceeding the mask threshold, U.sub.(T), a function of threshold T, with respect to the change in the value of the mask threshold T, or (dU.sub.(T) /dT), are measured. The correct, or best, value of the threshold occurs when .vertline.dP.sub.(T) /dT.vertline..gtoreq.0.5*.vertline.dU.sub.(T) /dT.vertline..

Automatic mask threshold

A method and apparatus for detecting an object of interest against a cluttered background scene. In a first preferred embodiment the sensor tracking the scene is movable on a platform such that each frame of the video representation of the scene is aligned, i.e., appears at the same place in sensor coordinates. A current video frame of the scene is stored in a first frame storage device (14) and a previous video frame of the scene is stored in a second frame storage device (20). The frames are then subtracted by means of an invertor (24)and a frame adder (28) to remove most of the background clutter. The subtracted image is put through a first leakage reducing filter, preferably a minimum difference processor filter (32). The current video frame in the first frame storage device (14) is put through a second leakage-reducing filter, preferably minimum difference processor filter (36). The outputs of the two processors are applied to a two quadrant multiplier (42) to minimize the remaining background clutter leakage and to isolate the moving object of interest. In a second preferred embodiment the sensor does not track the scene and therefore the frames of video from the two storage devices (140, 200) must be aligned before the subtraction process can occur. The alignment function is performed by a background registration correlator (150) and a frame shifter (130). Once the two frames of video data are aligned they are then subtracted and preferably applied to the minimum difference processor filters (320, 360) as with the first embodiment. The outputs of the two minimum difference processors (320, 360) are again applied to a two quadrant multiplier (420) to eliminate the remaining background clutter.

Moving target detection method using two-frame subtraction and a two

Three image frames containing an object of interest and background clutter are taken at successive time intervals and stored in memory. The background of images A, B and C are registered preferably using an area correlator 12. A median filter 16 is used to select a median value from the registered image frames. Then, subtractor 18 serves to subtract the median pixel values from one of the image frames. This difference output is then thresholded to provide a binary signal whose pixel values exceeding the threshold levels are generally associated with the position of the moving object.

Growing community of inventors

Temple City, CA, United States of America

Thomas K Lo