&lt;title&gt;Parallel algorithms for automatic target recognition using laser radar imagery&lt;/title&gt;

Forman, Arthur V.; Sullivan, Daniel J.; Chang, Arthur W.

doi:10.1117/12.23503

Cited by 3 publications

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“…The selected algorithm is similar to other existing approaches. 3,4 The algorithm that was implemented, which is shown in block diagram form in figure 2, is essentially an image segmentation algorithm 5 designed to segment pixels on potential target objects from surrounding pixel regions. The data received from the ladar by the ATR processor is the same as described for the fixed target algorithm.…”

Section: Mobile Target Detection Algorithmmentioning

confidence: 99%

Results of ladar ATR captive flight testing experiments

Cook

2001

SPIE Proceedings

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As part of an ongoing captive flight test demonstration project at the Naval Air Warfare Center (NAWC), China Lake, CA, two Automatic Target Recognition (ATR) algorithms were implemented in hardware, integrated with the second in a series of ladar sensors, and flown aboard a T-39 aircraft against both fixed and stationary mobile target sites on the ranges of the Naval Air Weapons Station (NAWS), also at China Lake, CA. The first ATR algorithm was developed to recognize fixed targets and to select aim-points with a performance goal of a five pixel Circular Error Probability (CEP.) The second ATR algorithm was developed to detect stationary mobile targets, such as tanks and trucks. The performance goal for this algorithm was to achieve 90% probability of detection. Both of these algorithms operate by exploiting the very accurate 3D geometry provided by the ladar. This paper describes the 1999 and 2000 captive flight tests involving these two algorithms, including the flight tests themselves, the hardware implementations, and the resulting ATR performance. Additionally, the large ladar data set, collected during twenty-four two-hour flights, will be briefly described.

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Section: Mobile Target Detection Algorithmmentioning

confidence: 99%

Results of ladar ATR captive flight testing experiments

Cook

2001

SPIE Proceedings

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“…There is an enormous array of algorithms that have been proposed, implemented in hardware, and tested within many Department of Defense (DOD) services and agencies. A selection of algorithm classes are statistical, shape based (template/model), MTI, increased dimensionality (e.g., 3-D LADAR), [14][15][16] hyper-/multispectral (MS/HS), [17][18][19] and neural nets. Multisensor phenomenologies have been tried, including multisensor, where more than one sensor is looking at the same target; multilook, where one sensor gets several looks at the target from different aspects; and multimode fusion, where sensors of different modalities sense the target (e.g., acoustic and EO signals are fused).…”

Section: Detectionmentioning

confidence: 99%

Review of current aided/automatic target acquisition technology for military target acquisition tasks

Ratches

2011

Opt. Eng

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Abstract. Aided and automatic target recognition (Ai/ATR) capability is a critical technology needed by the military services for modern combat. However, the current level of performance that is available is largely deficient compared to the requirements. This is largely due to the difficulty of acquiring targets in realistic environments but has also been due to the difficulty in getting new concepts from, for example, the academic community, due to limitations for distribution of classified data. The difficulty of the performance required has limited the fulfillment of the promise that is so anticipated by the war fighter. We review the metrics, imagery data bases, and sensors associated with Ai/ATR performance and suggest possible technical approaches that could enable new advancements in military-relevant performance. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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<title>Real-time distortion-tolerant composite filters for automatic target identification</title>

Sullivan¹,

Forman²,

Chang³

1992

SPIE Proceedings

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This paper describes work being performed at Martin Marietta to synthesize and implement robust, distortion-tolerant composite filters to perform automatic target recognition (ATR). IWo main topics will be discussed. The first part of the paper outlines two filter design methods for trading between distortion tolerance and false alarm rate. In the second part, we will explain the real-time digital implementation of a multiclass ATR system using composite filters in Martin Marietta's massively parallel computer, the Geometric Arithmetic Parallel Processor (GAPP).

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<title>Parallel algorithms for automatic target recognition using laser radar imagery</title>

Cited by 3 publications

References 0 publications

Results of ladar ATR captive flight testing experiments

Results of ladar ATR captive flight testing experiments

Review of current aided/automatic target acquisition technology for military target acquisition tasks

<title>Real-time distortion-tolerant composite filters for automatic target identification</title>

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