US5963653A — Hierarchical information fusion object recognitio…

Claim 1

Independent Claim 146 words

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, comprising the following steps: extracting information derived from each said sensor source to obtain detections and their feature attributes; providing at least two processing streams, one for each said sensor source, for converting said detections and their feature attributes into hypotheses on identity and class of detected objects; fusing said hypotheses at different feature levels for each said processing streams via hierarchical information fusion algorithms; and sharing and combining the detection feature attributes and hypotheses about the detections between the two processing streams using said hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, and providing said fused hypotheses to said processing streams for determining hypotheses based on said feature attributes.

Claim 2

Dependent Claim 146 words

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, comprising the following steps: extracting information derived from each said sensor source to obtain detections and their feature attributes; providing at least two processing streams, one for each said sensor source, for converting said detections and their feature attributes into hypotheses on identity and class of detected objects; fusing said hypotheses at different feature levels for each said processing streams via hierarchical information fusion algorithms; and sharing and combining the detection feature attributes and hypotheses about the detections between the two processing streams using said hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, and providing said fused hypotheses to said processing streams for determining hypotheses based on said feature attributes.

Claim 3

Dependent Claim 59 words

The method as claimed in claim 1, wherein: each said processing stream has a plurality of processing stages, and said hierarchical information fusion algorithms are performed at each said processing stage with decision nets based on evidential reasoning paradigms, to restrict the hypotheses on identity and class of detected objects and reduce the probability of misclassification of detected objects.

Claim 4

Dependent Claim 28 words

The method as claimed in claim 2, wherein the decision nets are project-defined and knowledge-driven decision nets, selected from the group of Markov tree systems and neural networks.

Claim 5

Dependent Claim 39 words

The method as claimed in claim 3, further comprising the step of: generating and updating the decision nets automatically from a problem description, and wherein the decision nets are made in modular form to allow easy and rapid updates.

Claim 6

Dependent Claim 128 words

The method as claimed in claim 1, wherein the method is adapted for a hierarchical real-time SAR/FLIR air-to-ground targeting object recognition system for automated target classification on the detection feature attribute level, each said processing stream has a plurality of processing stages, the sensors are a synthetic aperture radar (SAR) and a forward-look infra-red (FLIR), said processing stages are SAR processing stages and FLIR processing stages, further comprising: a spatial registration module for spatial registration of the detections from said multiple sensor sources to establish correspondences of common objects and to reject uncorrelated clutter detections, wherein the sensor sources include on-board and off-board sensor sources; and an off-board cues module for accepting off-board cues including information about target type, target position, target orientation, target velocity, and communication/radiation signatures.

Claim 7

Dependent Claim 88 words

The method as claimed in claim 5, wherein: said hierarchical information fusion algorithms are performed at each said processing stage with decision nets based on evidential reasoning paradigms, to restrict the hypotheses on identity and class of detected objects and reduce the probability of misclassification of detected objects, wherein at least one of said processing stages receives from said fusion algorithms the restricted hypotheses for feature processing, and the decision nets are project-defined and knowledge-driven decision nets, selected from the group of Markov tree systems and neural networks.

Claim 8

Dependent Claim 39 words

The method as claimed in claim 6, further comprising the step of: generating and updating the decision nets automatically from a problem description, and wherein the decision nets are made in modular form to allow easy and rapid updates.

Claim 9

Dependent Claim 189 words

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, wherein the method is adapted for a hierarchical real-time SAR/FLIR air-to-ground targeting object recognition system for automated target classification on the detection feature attribute level, comprising the following steps: extracting information derived from each said sensor source to obtain detections and their feature attributes; providing at least two processing streams, one for each said sensor source, for converting said detections and their feature attributes into hypotheses on identity and class of detected objects; providing a plurality of fusion engines having hierarchical information fusion algorithms for fusing said hypotheses at different feature levels for each said processing streams; and sharing and combining the detection feature attributes and hypotheses about the detections between the two processing streams using hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, and at least one of said fusion engines providing said fused hypotheses of said one fusion engine to said processing streams for determining hypotheses based on said feature attributes.

Claim 10

Dependent Claim 189 words

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, wherein the method is adapted for a hierarchical real-time SAR/FLIR air-to-ground targeting object recognition system for automated target classification on the detection feature attribute level, comprising the following steps: extracting information derived from each said sensor source to obtain detections and their feature attributes; providing at least two processing streams, one for each said sensor source, for converting said detections and their feature attributes into hypotheses on identity and class of detected objects; providing a plurality of fusion engines having hierarchical information fusion algorithms for fusing said hypotheses at different feature levels for each said processing streams; and sharing and combining the detection feature attributes and hypotheses about the detections between the two processing streams using hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, and at least one of said fusion engines providing said fused hypotheses of said one fusion engine to said processing streams for determining hypotheses based on said feature attributes.

Claim 11

Dependent Claim 104 words

The method as claimed in claim 8, wherein: each said processing stream has a plurality of processing stages, the sensors are a synthetic aperture radar (SAR) and a forward-look infra-red (FLIR), said processing stages are SAR processing stages and FLIR processing stages, further comprising: a spatial registration module for spatial registration of the detections from said multiple sensor sources to establish correspondences of common objects and to reject uncorrelated clutter detections, wherein the sensor sources include on-board and off-board sensor sources; and an off-board cues module for accepting off-board cues including information about target type, target position, target orientation, target velocity, and communication/radiation signatures.

Claim 12

Dependent Claim 48 words

The method as claimed in claim 9, wherein: said hierarchical information fusion algorithms are performed at each said processing stage with decision nets based on evidential reasoning paradigms, to restrict the hypotheses on identity and class of detected objects and reduce the probability of misclassification of detected objects.

Claim 13

Dependent Claim 28 words

The method as claimed in claim 10, wherein the decision nets are project-defined and knowledge-driven decision nets, selected from the group of Markov tree systems and neural networks.

Claim 14

Dependent Claim 39 words

The method as claimed in claim 11, further comprising the step of: generating and updating the decision nets automatically from a problem description, and wherein the decision nets are made in modular form to allow easy and rapid updates.

Claim 15

Dependent Claim 181 words

A hierarchical object recognition system for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, said system comprising: an extraction information engine derived from each said sensor source using hierarchical information extraction algorithms to obtain detections and their feature attributes; at least two processing streams, one for each said sensor source, each said processing stream having a plurality of processing stages adapted for converting said detections and their feature attributes into hypotheses on identity and class of detected objects; and a fusion engine having a fuse decision module for sharing and combining the detection feature attributes and hypotheses about the detections between the at least two processing streams using hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, said fusion engine sharing and combining the hypotheses of at least one of said processing stages for each said sensor source in order to provide the combined hypotheses to another one of said processing stages for determining hypotheses based on said feature attributes.

Claim 16

Dependent Claim 181 words

A hierarchical object recognition system for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, said system comprising: an extraction information engine derived from each said sensor source using hierarchical information extraction algorithms to obtain detections and their feature attributes; at least two processing streams, one for each said sensor source, each said processing stream having a plurality of processing stages adapted for converting said detections and their feature attributes into hypotheses on identity and class of detected objects; and a fusion engine having a fuse decision module for sharing and combining the detection feature attributes and hypotheses about the detections between the at least two processing streams using hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, said fusion engine sharing and combining the hypotheses of at least one of said processing stages for each said sensor source in order to provide the combined hypotheses to another one of said processing stages for determining hypotheses based on said feature attributes.

Claim 17

Dependent Claim 129 words

The system as claimed in claim 13, wherein: each said processing stream comprising: an area of interest stage, an indexer stage, wherein at least one of said indexer stages generates feature data based upon data provided by at least one of said fusion engines and by another indexer stage, a spatial features stage, and an internal features stage, the extraction information engine has a fuse detections stage which confirms the objects detected by each said processing stream, the fuse decision module comprises at least one fuse hypotheses stage for fusing aggregated detections from each processing stream to restrict the set of hypotheses for the subsequent stages of processing, and the area of interest stage includes: a search/detect stage, a geometric features extraction stage, and a reflective features extraction stage.

Claim 18

Dependent Claim 48 words

The system as claimed in claim 13, wherein said hierarchical information fusion algorithms are performed at each said processing stage with decision nets based on evidential reasoning paradigms, to restrict the hypotheses on identity and class of detected objects and reduce the probability of misclassification of detected objects.

Claim 19

Dependent Claim 47 words

The system as claimed in claim 15, wherein the decision nets are project-defined and knowledge-driven decision nets, selected from the group of Markov tree systems and neural networks, generated and updated automatically from a problem description, and made in modular form to allow easy and rapid updates.

Claim 20

Dependent Claim 115 words

The system as claimed in claim 13, wherein: said system is a hierarchical real-time SAR/FLIR air-to-ground targeting object recognition system for automated target classification on the detection feature attribute level, the sensors are a synthetic aperture radar (SAR) and a forward-look infra-red (FLIR), said processing stages are SAR processing stages and FLIR processing stages, further comprising: a spatial registration module for spatial registration of the detections from said multiple sensor sources to establish correspondences of common objects and to reject uncorrelated clutter detections, wherein the sensor sources include on-board and off-board sensor sources; and off-board cues module for accepting off-board cues including information about target type, target position, target orientation, target velocity, and communication/radiation signatures.

Claim 21

Dependent Claim 48 words

The system as claimed in claim 17, wherein said hierarchical information fusion algorithms are performed at each said processing stage with decision nets based on evidential reasoning paradigms, to restrict the hypotheses on identity and class of detected objects and reduce the probability of misclassification of detected objects.

Claim 22

Dependent Claim 47 words

The system as claimed in claim 18, wherein the decision nets are project-defined and knowledge-driven decision nets, selected from the group of Markov tree systems and neural networks, generated and updated automatically from a problem description, and made in modular form to allow easy and rapid updates.

Claim 23

Dependent Claim 161 words

The system as claimed in claim 19, wherein: the SAR subsystem comprising: a radar area of interest stage, a radar indexer stage, a radar spatial features stage, and a radar internal features stage, the FLIR subsystem comprising: a FLIR area of interest stage, a FLIR indexer stage, a FLIR spatial features stage, and a FLIR internal features stage, the extraction information engine has a fuse detections stage which confirms the objects detected by both the SAR and the FLIR subsystem, the fuse decision module comprises at least one fuse hypotheses stage for fusing aggregated detections from the SAR and FLIR subsystem to restrict the set of hypotheses for the subsequent stages of processing, the radar area of interest stage includes: a radar search/detect stage, a radar geometric features extraction stage, and a radar reflective features extraction stage, and the FLIR area of interest stage includes: a FLIR search/detect stage, a FLIR geometric features extraction stage, and a FLIR emissive features stage.

Claim 24

Dependent Claim 130 words

A hierarchical information fusion target recognition system for classifying targets based upon target data from a plurality of sensor sources: an information extraction engine for processing target data from each of said sensor sources at different feature levels in order to formulate target hypotheses regarding classification of said targets; a plurality of fusion engines connected to said information extraction engine for fusing target hypotheses at each of said different feature levels from said sensor sources, and at least one of said fusion engines providing said fused target hypotheses of said one fusion engine to said information extraction engine for determining hypotheses on identity and class of detected objects; and a target list generator connected to said fusion engines for generating a list of targets based upon said fused target hypotheses.

Claim 25

Dependent Claim 130 words

A hierarchical information fusion target recognition system for classifying targets based upon target data from a plurality of sensor sources: an information extraction engine for processing target data from each of said sensor sources at different feature levels in order to formulate target hypotheses regarding classification of said targets; a plurality of fusion engines connected to said information extraction engine for fusing target hypotheses at each of said different feature levels from said sensor sources, and at least one of said fusion engines providing said fused target hypotheses of said one fusion engine to said information extraction engine for determining hypotheses on identity and class of detected objects; and a target list generator connected to said fusion engines for generating a list of targets based upon said fused target hypotheses.

Claim 26

Dependent Claim 25 words

The hierarchical information fusion target recognition system of claim 21 wherein said different feature levels include spatial features and internal features related to said target.

Claim 27

Dependent Claim 23 words

The hierarchical information fusion target recognition system of claim 22 wherein said internal features relate to reflective and emissive features of said targets.

Claim 28

Dependent Claim 72 words

The hierarchical information fusion target recognition system of claim 21 further comprising: a detections fusion engine for fusing detections of said sensor sources in order to detect false alarms in classifying targets, said detections fusion engine performing said detections based upon correlating area of interest data from said sensor sources, said detections fusion engine providing said fused detections to said information extraction engine for processing target data at a predetermined feature level.

Claim 29

Dependent Claim 160 words

The hierarchical information fusion target recognition system of claim 21 further comprising: first and second indexer stages being respectively associated with a first and second sensor source for formulating first and second hypotheses regarding target classification based respectively upon said target data of said first and second sensor sources, said first indexer stage generating first geometric feature data based upon target data from said first sensor, said first indexer stage providing said first geometric data to said second indexer stage, said second indexer stage generating second geometric feature data based upon target data from said second sensor and upon said provided first geometric data, said second indexer stage formulating said second hypotheses regarding target classification of said target data from said second sensor source; and a hypotheses fusion engine connected to said first and second indexer stages for fusing said first and second formulated hypotheses, wherein said target list generator classifies targets based upon said fused first and second hypotheses.

Claim 30

Dependent Claim 97 words

The hierarchical information fusion target recognition system of claim 25 further comprising: a detections fusion engine for fusing detections of said sensor sources in order to detect false alarms in classifying targets, said detections fusion engine performing said detections based upon correlating area of interest data from said sensor sources, said detections fusion engine providing said fused detections to said information extraction engine for processing target data at a predetermined feature level, said first and second indexer stages utilizing said generated fused detections from said detections fusion engine in order to formulate said first and second hypotheses.

Claim 31

Dependent Claim 56 words

The hierarchical information fusion target recognition system of claim 25 further comprising: a joint target model database for storing model geometric features of predetermined types of targets, said first and second indexer stages comparing said determined first and second geometric feature data with said stored geometric model features in order to formulate first and second hypotheses.

Claim 32

Dependent Claim 113 words

The hierarchical information fusion target recognition system of claim 25 further comprising: first and second spatial features stages being respectively associated with a first and second sensor source, said first and second spatial features stages formulating first and second spatial hypotheses regarding target classification based respectively upon said target data of said first and second sensor sources and upon said fused first and second hypotheses from said first and second indexer stages; and; a spatial features hypotheses fusion engine connected to said first and second spatial features stages for fusing said first and second formulated spatial hypotheses, wherein said target list generator classifies targets based upon said fused first and second spatial hypotheses.

Claim 33

Dependent Claim 31 words

The hierarchical information fusion target recognition system of claim 21 wherein said fusion engines include decision nets which utilize evidential reasoning paradigms in order to fuse hypotheses regarding said target data.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2002-10-04	2003-04-05	2003-10-05	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2006-10-04	2007-04-05	2007-10-05	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2010-10-04	2011-04-05	2011-10-05	$7700.00	Window Closed / Lapsed

Hierarchical information fusion object recognition system and method

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