Raytheon Company

Todd L Baker

Thomas K Lo

Timothy Edward Caber

Sheldon S Wald

Hien T Dang

Yang Chen

Kent P Pflibsen

Kurt Reiser

Michael A Leal

The present invention provides a MKV interceptor including multiple kill vehicles with autonomous management capability and kinematic reach to prosecute a large threat extent. Each KV can self-manage its own KV deployment and target engagement for a determined target volume assigned by a designated master KV. At least one KV is master capable of managing the post-separation of all of the KVs without requiring updates to the mission plan post-separation. The autonomous capability and increased kinematic reach provides for a more efficient use of boosters and more effective engagement of the threat.

Multiple kill vehicle (MKV) interceptor with autonomous kill vehicles

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

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 method providing automatic learning belief functions enabling the combination of different, and possibly contradictory information sources. The present invention provides the ability to determine erroneous information sources, inappropriate information combinations, and optimal information granularities, along with enhanced system performance.

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Green Valley, AZ, United States of America

Todd L Baker