Raytheon Company

Christopher M Pilcher

John R Goulding

Christopher Harris

William D Weaver

Suhail Shabbir Saquib

Raymond Samaniego

John Louis Tomich

Crystal D Rhodes

Devices, systems, and methods for machine learning (ML) automatic target recognition (ATR) decision explanation are provided. A method can include receiving an object specification matrix from an object model database that indicates, for each of a plurality of physical portions of an object, whether each of a plurality of features are present or absent in a physical portion of the physical portions of the object and a proportional physical displacement between the features in the object, receiving feature data indicating for an image of a portion of the object, a likelihood whether each of features are present in the image, determining based on the object specification matrix and the feature data, a probability and corresponding uncertainty that the image corresponds to the object, and providing the probability and corresponding uncertainty of the object to help classify the object.

Object classification based on spatially discriminated parts

Devices, systems, and methods for machine learning model generation. A method can include generating image chips of an image. The image chips can each provide a view of a different extent of an object in the image. Based on an object definition that indicates respective features of the object and a location of the respective features along a length of the object, it can be determined whether any of the image chips include any of the respective features. Each image chip of the image chips can be labeled to include an indication of any of the features included in the image chip resulting in labelled image chips. The method can include training an ensemble classifier based on the labelled image chips resulting in a trained ensemble classifier.

Feature extraction, labelling, and object feature map

An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

Systems and methods for feature extraction and artificial decision explainability

In certain embodiments, a subterranean imaging apparatus comprises at least two receive channels configured on a land-based vehicle and a synthetic aperture radar (SAR) system. The at least two receive channels are operable to generate electrical signals according to electromagnetic radiation reflected from a subterranean target below a ground surface. The SAR system is operable to receive the electrical signals from the at least two receive channels, generate raw images from the received electrical signals, generate a weighting according to phase statistics of pixels in the raw images, and combine the raw images using the weighting to generate a refined image of the subterranean target.

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Dallas, TX, United States of America

Christopher M Pilcher