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Real-Time Radarimage Real-Time Radar Image Understanding: A Machine­ Intelligence Approach Ann Marie Aull, Robert A. Gabel, and Thomas J. Goblick II Machine intelligence (MI) techniques have been combined with conventional signal processing and image processing techniques to build a software package that automatically recognizes reentry vehicles from a sequence of radar images. This software package, called ROME (Radar Object Modeling Environment), takes as its input a time sequence ofrange-Doppler images ofa target produced by the Lincoln Laboratory Discrimination System from raw radar-data samples. ROME then processes thi~ image sequence to extract radar features and track them from image to image. From these feature tracks, ROME then constructs a three-dimensional model ofthe target. The object model derived from the image sequence is then compared to a catalog ofmodels ofknown objects to find the best match. Ifno sufficiently close match is found, the observed object is declared unrecognized. The object-model catalog is constructed by adding new models derived from radar data that do not match any model already in the catalog. Thus ROME is "trained" to recognize objects by using real radar data from known objects. Because ofthe strong interest in real-time recognition ofreentry vehicles, the entire ROME system was recoded for parallel execution on the MX-l multi­ processor, which was developed in the Machine Intelligence group at Lincoln Laboratory. This machine was designed for MI applications that involve intensive numeric as well as symbolic computation, and that have real-time processing requirements. The final version ofthe ROME object-recognition system, coded in a combination ofparallel Common LISP and C, runs in real time on the 16-node MX-l multiprocessor. OR MANY YEARS Lincoln Laboratory has been trajectory, prior to their interaction with the atmo­ . involve~ in research pertaining to defense sphere. In this exoatrnospheric region, object trajecto­ F against intercontinental ballistic missiles, in­ ries are independent ofvehicle shape and mass; hence cluding research on the detection, tracking, and rec­ gross vehicle dynamics cannot be used as discrimi­ ognition of reentry vehicles. During this time several nants. The approach taken here is based on the ap­ approaches to the problem of rapid and reliable rec­ pearance and local dynamics ofthe object as inferred ognition of reentry vehicles have been pursued. In from a sequence ofradar images. this article we report on an approach that combines The concept of this approach to the early recogni­ conventional signal processing and image processing tion of reentry vehicles is to have a radar detect an techniques with machine intelligence (MI) techniques unknown object, track it, and take enough data to recognize reentry vehicles at an early stage in their samples to form a sequence ofrange-Doppler images. VOLUME 5, NUMBER 2, 1992 THE LINCOLN LABORATORY JOURNAL 195 -AULLET AL. Real- Time Radar Image Understanding: A Machine-Intelligence Approach Then, while this image sequence is being processed apptoach involves a toolbox ofsignal processing primi­ to recognize the object, the radar can detect and take tives, termed knowledge sources, that are designed to data on another object. The radar can thus be time select data subsets, extract features, and compare ex­ shared to handle multiple targets. Of course, real­ tracted features with the data to produce confidence time operation of the overall detection-and-recogni­ measures. These primitives include such standard sig­ tion process is the primary objective, which in this nal processing procedures as clustering algorithms, context means that the object-recognition processing correlation measures, spectral analysis, and paramet­ should take no longer than the amount oftime needed ric curve fitting; their choice and design are based on to gather the radar data on the object. procedures used in the manual analysis and identifi­ Conventional approaches to object classification cation of these objects. typically involve parameter estimation, multidimen­ The second system module is the semantic model­ sional pattern classification, or template matching. building and matching scheme. This component takes These approaches have been successful in many con­ the data-derived features and produces a semantic texts, but are difficult to apply to the recognition ofa model that is then matched against a catalog ofstored three-dimensional object from a sequence of noisy semantic models for object identification. The se­ images. They also can be computationally intensive mantic model represents the extracted information as and sensitive to noise, artifacts, and minor object physical components, intercomponent relationships, alterations. and properties of the components and their relation­ Human analysts, however, have been successful at ships. The extracted model and the stored catalog extracting estimates of radar scattering-center mo­ model are identical in form and thus can readily be tion even from noisy data at low imaging rates. This compared and contrasted. In addition, the system observation motivated us to pursue the approach of can generalize several examples ofsemantic models to building a three-dimensional model ofan object as a form a single semantic model representation. This collection of radar scatterers on a rigid frame that feature is useful when the system is presented with exhibits local dynamics about the gross trajectory. data of objects for which we do not have a priori Hence we take an approach that is more like recogni­ information. tion of visual objects, rather than the conventional The final system module is the control mechanism statistical-decision-theory or pattern-classification based on the blackboard structure developed in the apptoach. field ofMI [2J. The blackboard structure consists ofa global memory (the blackboard) in which informa­ Knowledge-Based Signal Processing tion is posted as it accumulates. Knowledge sources The goal ofour approach is to combine the power of that embody specific information-processing algo­ conventional methods with the flexible representa­ rithms check the state ofthe blackboard to see iftheir tions and control structures from the field of ma­ expertise can be applied to move the processing to­ chine intelligence. A first step in our object classifica­ ward its final result. A knowledge source extracts tion is to derive primitive features from which a information currently on the blackboard, does its semantic model can be built. The semantic model own processing, and posts its results back on the then can be readily manipulated and interpreted by a blackboard. The system control continues in this human observer as well as by automatic recognition, manner until no knowledge sources remain to be discrimination, and generalization procedures. applied. A scheduler controls the triggering ofknowl­ We have built a recognition system and interactive edge sources in cases when more than one is ready to workstation software package that we call the Radar perform some action. The blackboard design is in­ Object Modeling Environment (ROME). Three ma­ herently modular and opportunistic, allowing both jor conceptual modules are in our current system. data-driven and model-driven processing. The first of these modules is based on an approach The ROME user interface displays the recognition known as knowledge-based signal processing [1 J. This and blackboard control operations as they proceed. 196 THE LINCOLN LABORATORY JOURNAL VOLUME 5, NUMBER 2, 1992 ·AULLET AL. Real- Time Radar image Understanding: A Machine-intelligence Approach Blackb041d l<kS I<FERTURE SPIN-RATE RIGID-BODV» (SPIN-RATE. 9) I<KS I<RESOLVER SPIN-RRTE RIGID-BODY» ( SPIN-RESOLVED?) I<KS I<FEATURE RRNCE-CLUSTERS 'lItHD-BODY» (PRECeSSION 1.5680162 0.5852""6 3.605369"'e-<4 1.3064252) '<kS I<FERTURE OCCLUDED RNTENNA» (PRECESSION-RESOLVED? . T) '<1(5 '<FEATURE PRECESSION RIGID-BODy) (OCCLUDED) I<KS I<RESOLVER PRECESSION IUGID-BODY» (RAI1GE-CLUSTERS • T) ......', " . ,~~>:~. ,:~~,:~,~;~:~~\~.~ ;):;~ ;\~::~:~~ ~.~ ':~ ~~~~.~::~ ':.:~' .. .. .. ..\.::.. ...... ....... MO'h1 ROME Rada,. Ob eet Modellin" Environment .. Slipping scatter-era: Load Trock,•••I rae. • ,.~ .1,18 ReS Show Load.d Trock. C1.ar Trock. 1 9.geO 9.632 2 9.585 1),292 Pia, Track R•••, and Pia, Track 3 1.396 9.296 Plotting Parameters Describe Track .. 1.568 9.lo45 Ot:scribc Plotted Trae (reate Track System Pwamcters K5 State 2 Spinning scatter-ers: Chang. K5 S..... BLACKBOARD STATE • "'..,ge: X-"..,ge: ....gle ~MOlJ[L f3llli1ll[[] 1 1.311 9.212 31.5 ~ 30 fllOM DATA ~ 2 1.320 9.218 213.9 R..., Blockboard R•••, KS Initialize System Break Refresh Exit IJtndow 1 FIGURE 1. Presentation of information in a screen image taken from a Symbolics workstation running ROME. The input data, in a Doppler-versus-time format, is shown in the middle panel. Directly below it are parameters of the derived model together with a cartoon outline of the essential features of the vehicle. Monitor panels at the top left and right show, respectively, the state of active knowledge sources and of the blackboard. At the bottom right is the mouse-controlled operator's panel. In addition to its recognition capabilities, ROME presentation from the Symbolics workstation screen, can act as a workstation for manual analysis of target shows the data track in the middle panel, the operator's characteristics. The menu-driven workstation pro­ control at lower right, the knowledge source at upper vides a useful
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