Target Recognition Capabilities Research Articles

A retrieval system from inverse synthetic aperture radar images: Application to radar target recognition

This paper presents an approach to aircraft target recognition using Inverse Synthetic Aperture Radar (ISAR) images. The goal of this work is to develop a robust algorithm to add Automated Target Recognition (ATR) capabilities to extract efficient feature vectors and ensure a comprehensive recognition process.In the first part on this paper, we used a database of ISAR images reconstructed from anechoic chamber simulations in order to extract efficient feature vectors. In the second part of this work, we proposed a recognition architecture to perform recognition tasks and provide a human operator with useful information for target recognition tasks.Several kinds of descriptor can be used to acquire information about target characteristics from radar signals and images. Indeed, a number of methods are currently used in automatic target recognition; notably based on ISAR imaging. However, target characteristic extraction from radar echoes remains a difficult task, and the methods are generally specific to either aircraft or ship recognition. In this work, we describe our approach to designing faster, more effective retrieval systems and a comprehensive architecture. Our approach uses global feature vectors for both ship and aircraft recognition.Firstly, the global feature vectors are described by two types of descriptor which have shown to be efficient in radar target recognition. The first type defines target shape obtained by watershed transformation, facilitating interpretation for the human operator. The second type of vector descriptor is based on so-called polar signatures, obtained using the polar mapping procedure. The latter are highly discriminative and thus significantly improve and facilitate target recognition, leading to greater precision and accuracy. Secondly, we describe the retrieval architecture suitable for the second type of vector descriptor, which checks invariance in relation to target rotation and scale. It is, in itself, more efficient and processing time at the retrieval step is reduced and controlled by the human operator. Finally, in order to validate our proposed feature vectors and architecture, the Support Vector Machine (SVM) classifier will be implemented; the results of which will be tested and presented in the last section of this paper.

Ground target recognition based on imaging LADAR point cloud data

The battlefield scenario continues to grow in complexity as the use of high-resolution sensors and precision strike weapons has forced the increased use of concealment and camouflage technology to improve weapon survivability. Thus, automatic target recognition (ATR) capability is becoming increasingly important to the Defense Community. There are two fundamental challenges in ATR for military applications. The first is the massive data loads generated by modern high-resolution sensors, which can quickly escalate to unmanageable proportions. The second challenge is camouflage, concealment, and deception, this further increases the complexity of ATR and moti

Engineering polymeric aptamers for selective cytotoxicity.

Chemotherapy strategies thus far reported can result in both side effects and drug resistance. To address both of these issues at the cellular level, we report a molecular engineering strategy, which employs polymeric aptamers to induce selective cytotoxicity inside target cells. The polymeric aptamers, composed of both multiple cell-based aptamers and a high ratio of dye-labeled short DNA, exploit the target recognition capability of the aptamer, enhanced cell internalization via multivalent effects, and cellular disruption by the polymeric conjugate. Importantly, the polymer backbone built into the conjugate is cytotoxic only inside cells. As a result, selective cytotoxicity is achieved equally in both normal cancer cells and drug-resistant cells. Control assays have confirmed the nontoxicity of the aptamer itself, but they have also shown that the physical properties of the polymer backbone contribute to target cell cytotoxicity. Therefore, our approach may shed new light on drug design and drug delivery.

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

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.

Individual Differences in Human-Robot Interaction in a Military Multitasking Environment

A military vehicle crew station environment was simulated and a series of three experiments was conducted to examine the workload and performance of the combined position of the gunner and robotics operator in a multitasking environment. The study also evaluated whether aided target recognition (AiTR) capabilities (delivered through tactile and/or visual cuing) for the gunnery task might benefit the concurrent robotics and communication tasks and how the concurrent task performance might be affected when the AiTR was unreliable (i.e., false alarm prone or miss prone). Participants’ spatial ability was consistently found to be a reliable predictor of their targeting task performance as well as their modality preference for the AiTR display. Participants’ attentional control was found to significantly affect the way they interacted with unreliable automated systems.

Radar and Optical Images Fusion Using Stripmap SAR Data with Multilook Processing

Radar and Optical Images Fusion Using Stripmap SAR Data with Multilook Processing The paper presents the real-life data results of SAR and optical images data fusion. The fusion has been carried out for SAR images obtained in stripmap SAR mode using multilook processing with different methods of final image creation. The aim of the fusion was to enhance the target recognition capabilities on the Earth surface for a simple single-channel SAR receiver.

OLIGONUCLEOTIDE DNA AND RNA AS DIRECT CAPPING LIGAND FOR NANOCRYSTALS: AN EMERGING METHOD FOR BIOLOGICAL DIAGNOSTICS AND THERAPEUTICS

Various types of nanocrystals have extensively demonstrated significant advantages in magnetic, chemical, catalytic, and particularly optical properties. Still, some limitations prevent these properties from being utilized for improved biological imaging, therapeutics or micro/nano-optoelectronics. A recently emerging, facile approach employing oligonucleotide DNA or RNA for direct surface passivation of nanocrystals is showing promise to bridge the gap between functional potential and realization. Oligonucleotide capping can provide hydrophilic nature, target recognition capabilities, and enhanced cellular uptake for nanocrystals, with a simplified synthesis capable of both templating and functionalizing. We overview synthesis, properties, and applications of nucleic acid templated nanocrystals and contrast these with nanocrystals synthesized by more classical capping methods. Finally, we highlight areas of research in oligonucleotide templated nanocrystals that have been largely unexplored to date, where further investigations can provide many new insights.

Effects of imperfect automation and individual differences on concurrent performance of military and robotics tasks in a simulated multitasking environment

This study investigated the performance and workload of the combined position of gunner and robotics operator in a simulated military multitasking environment. Specifically, the study investigated how aided target recognition (AiTR) capabilities for the gunnery task with imperfect reliability (false-alarm-prone vs. miss-prone) might affect the concurrent robotics and communication tasks. Additionally, the study examined whether performance was affected by individual differences in spatial ability and attentional control. Results showed that when the robotics task was simply monitoring the video, participants had the best performance in their gunnery and communication tasks and the lowest perceived workload, compared with the other robotics tasking conditions. There was a strong interaction between the type of AiTR unreliability and participants' perceived attentional control. Overall, for participants with higher perceived attentional control, false-alarm-prone alerts were more detrimental; for low attentional control participants, conversely, miss-prone automation was more harmful. Low spatial ability participants preferred visual cueing and high spatial ability participants favoured tactile cueing. Potential applications of the findings include personnel selection for robotics operation, robotics user interface designs and training development. The present results will provide further understanding of the interplays among automation reliability, multitasking performance and individual differences in military tasking environments. These results will also facilitate the implementation of robots in military settings and will provide useful data to military system designs.

Concurrent Performance of Military and Robotics Tasks and Effects of Cueing in a Simulated Multi-Tasking Environment

We simulated a military mounted crewstation environment and conducted two experiments to examine the workload and performance of the combined position of gunner and robotics operator. The robotics tasks involved managing a semi-autonomous ground robot or teleoperating a ground robot to conduct reconnaissance tasks. We also evaluated whether aided target recognition (AiTR) capabilities (delivered either through tactile or tactile + visual cueing) for the gunnery task might benefit the concurrent robotics and communication tasks. Results showed that participants' gunnery task performance degraded significantly when they had to concurrently monitor, manage, or teleoperate an unmanned ground vehicle compared to the gunnery-single task condition. When there was AiTR to assist them with their gunnery task, operators' concurrent performance of robotics and communication tasks improved significantly. However, there was a tendency for participants to over-rely on automation when task load was heavy, and performance degradations were observed in instances where automation failed to be entirely reliable. Participants' spatial ability was found to be a reliable predictor of robotics task performance, although the performance gap between those with higher and lower spatial ability appeared to be narrower when the AiTR was available to assist the gunnery task. Participants' perceived workload increased consistently as the concurrent task conditions became more challenging and when their gunnery task was unassisted. Individual difference factors such as spatial ability and perceived attentional control were found to correlate significantly with some of the performance measures. Implications for military personnel selection were discussed.

Concurrent Performance of Gunner's and Robotics Operator's Tasks in a Multitasking Environment

In this study, we simulated a generic mounted crew station environment and conducted an experiment to examine the workload and performance of the combined position of gunner and robotics operator. Results showed that participants' gunnery task performances degraded significantly when he had to concurrently monitor, manage, or teleoperate an unmanned ground vehicle compared to the gunnery single task condition. For the robotics tasks, participants had the lowest performance when using the semi-autonomous robot, indicating overreliance on the aided target recognition capabilities available when task load was heavy (i.e., concurrent performance of the gunnery task). Participants' perceived workload increased consistently as the concurrent task conditions became more challenging. Individual difference factors such as spatial ability and perceived attentional control were found to correlate significantly with some of the performance measures. Implications for military personnel selection were discussed.

Individual Differences in Concurrent Performance of Military and Robotics Tasks with Tactile Cueing

We simulated a military tank environment and examined the performance of the combined position of gunner and robotics operator and how aided target recognition (AiTR) capabilities (delivered either through tactile or tactile + visual cueing) for the gunnery task might benefit the concurrent robotics and communication tasks. Specifically, we investigated whether performance was affected by individual differences factors such as spatial ability and perceived attentional control. Results showed that participants' robotics and communication tasks both improved significantly when the AiTR was available to assist them with their gunnery task. The participants' spatial ability was found to be a good indicator of their gunnery and robotics task performance. However, when AiTR was available to assist their gunnery task, those participants of lower spatial ability were able to perform their robotics tasks as well as those of higher spatial ability. There was also evidence that operators' preference of cueing modality was related to their spatial ability and attentional control.

Effectiveness of Concurrent Performance of Military and Robotics Tasks and Effects of Cueing in a Simulated Multi-Tasking Environment

We simulated a military mounted crewstation environment and conducted two experiments to examine the workload and performance of the combined position of gunner and robotics operator. The robotics tasks involved managing a semi-autonomous ground robot or teleoperating a ground robot to conduct reconnaissance tasks. We also evaluated whether aided target recognition (AiTR) capabilities (delivered either through tactile or tactile + visual cueing) for the gunnery task might benefit the concurrent robotics and communication tasks. Results showed that participants' gunnery task performance degraded significantly when s/he had to concurrently monitor, manage, or teleoperate a robot compared to the gunnery-single task condition. When there was AiTR to assist them with their gunnery task, operators' concurrent performance of robotics and communication tasks improved significantly. However, there was a tendency for participants to over-rely on automation when taskload was heavy, and performance degradations were observed in instances where automation failed to be entirely reliable. Participants' spatial ability was found to be a reliable predictor of robotics task performance. Participants' perceived workload increased consistently as the concurrent task conditions became more challenging and when their gunnery task was unassisted.

Effects of tactile cueing on concurrent performance of military and robotics tasks in a simulated multitasking environment

This study examined the concurrent performance of military gunnery, robotics control and communication tasks in a simulated environment. More specifically, the study investigated how aided target recognition (AiTR) capabilities (delivered either through tactile or tactile + visual cueing) for the gunnery task might benefit overall performance. Results showed that AiTR benefited not only the gunnery task, but also the concurrent robotics and communication tasks. The participants' spatial ability was found to be a good indicator of their gunnery and robotics task performance. However, when AiTR was available to assist their gunnery task, those participants of lower spatial ability were able to perform their robotics tasks as well as those of higher spatial ability. Finally, participants' workload assessment was significantly higher when they teleoperated (i.e. remotely operated) a robot and when their gunnery task was unassisted. These results will further understanding of multitasking performance in military tasking environments. These results will also facilitate the implementation of robots in military settings and will provide useful data to military system designs.

Sp1 modulates ncOGT activity to alter target recognition and enhanced thermotolerance in E. coli

cDNAs encoding three isoforms of OGT (ncOGT, mOGT, and sOGT) were expressed in Escherichia coli in which the coexpression system of OGT with target substrates was established in vivo. No endogenous bacterial proteins were significantly O-GlcNAcylated by any type of OGT isoform while co-expressed p62 and Sp1 were strongly O-GlcNAcylated by ncOGT. These results suggest that most of bacterial proteins appear not to be recognized as right substrates by mammalian OGT whereas cytosolic environments may supply UDP-GlcNAc enough to proceed to O-GlcNAcylation in E. coli. Under these conditions, sOGT was auto- O-GlcNAcylated whereas ncOGT and mOGT were not. Importantly, we found that when Sp1 was coexpressed, ncOGT can O-GlcNAcylate not only Sp1 but also many bacterial proteins. Our findings suggest that Sp1 may modulate the capability of target recognition of ncOGT by which ncOGT can be led to newly recognize bacterial proteins as target substrates, finally generating the O-glyco-bacteria. Our results demonstrate that the O-glyco-bacteria showed enhanced thermal resistance to allow cell survival at a temperature as high as 52 °C.

Pattern recognition for detection of human heads in infrared images

Human automated target recognition (ATR) capability holds important tactical value in military as well as civilian applications. Unmanned systems equipped with real-time human ATR sensors and software will serve to detect potential threats before human forces encounter them. Unattended ground stations may use human ATR in search and rescue applications, to alert rescue teams when help is necessary. The algorithm proposed in this study utilizes infrared imagery to detect people based on the radiance and shape of the human head. The algorithm works in a three-step process of segmentation, feature extraction, and classification. First, the IR image is segmented to reveal only human skin areas (e.g., arms, legs, heads). Next, three morphological features are extracted from each segmented object of interest. Finally, a classifier will use the features to determine whether the object is a head or a nonhead, based on previous algorithmic training. Two types of classifiers were tested in this study: a k-nearest-neighbor classifier and various neural networks. Results show that using a neural network classifier, 97% accuracy in head identification is possible after examining two sequential uncorrelated frames containing the same human head in different views. Tests in a desert environment at nighttime show that the majority of test subjects are detected, with few false positives.

Integrated approach for automatic target recognition using a network of collaborative sensors

We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.

Results of 3-D georadar surveying and trenching the San Andreas fault near its northern landward limit

As part of a program to determine the location and geometry of the San Andreas Fault (SAF) buried beneath shallow sediments near its northern landward limit, three >20-m-long parallel trenches were constructed at positions distributed over a distance of ∼55 m. The majority of excavated material comprised unconsolidated fluvial sediments deposited in a number of paleochannels. Single zones of active faulting identified in each of the trenches were initially interpreted in terms of a solitary strand of the SAF. To map the SAF between and beyond the trenches and to detect other active fault zones hidden by the young sedimentary cover, we collected a dense ground-penetrating radar (georadar) data set across a 23.2×72 m area. The data were recorded using a semi-automated acquisition system that included a conventional georadar unit coupled to a self-tracking laser theodolite with automatic target recognition capabilities. Since these data were plagued by system ringing as a result of the moderate-to-high electrical conductivities of the surficial sediments, an extensive data processing scheme was required to extract meaningful subsurface information. The final processed georadar volume (cuboid) contained numerous subhorizontal and trough-shaped reflections that originated from the fluvial paleochannels. Using the geological interpretation of the trench walls as a guide to pick semi-automatically the times of the most important reflecting horizons, we discovered that alignments of the nearly linear boundaries of these horizons defined two NW–SE trending strands of the SAF within the survey area. The georadar expression of the eastern SAF strand could only be traced over a distance of ∼38 m. It had been intersected in the northern trench. In contrast, the western SAF strand extended over the entire length of the georadar volume and had been intersected in the central and southern trenches. Prominent reflections on georadar cross sections were found to be vertically displaced by 0.2–0.3 m across both SAF strands. A conspicuous linear-trending feature observed on horizontal sections at 3.3–3.6 m depth was laterally offset by 4.5–5.5 m along the eastern SAF strand. The interpreted vertical and horizontal offsets could have been generated by the 1906 San Francisco earthquake and/or earlier events. Undetermined amounts of aseismic slip may also have occurred along the newly defined SAF strands.

Effect of AR model-based data extrapolation on target recognition performance

A data extrapolation method is applied to improve the performance of a target recognition scheme based on the central moments of one-dimensional range profiles. We adopt the autoregressive (AR) model to extrapolate the radar cross section data of a target in order to expand the measured data window. It is shown that the resulting high resolution range profiles can enhance target recognition capability, providing a more accurate recognition performance than the multiple signal classification range profile for moderate signal-to-noise ratio SNR ranges. Furthermore, the effects of the AR model-based data extrapolation on target recognition accuracy are carefully analyzed and investigated.

Joint tracking and identification algorithms for multisensor data

The paper describes an algorithm to jointly form a track and assign an identity flag to a target on the basis of measurements provided by a suite of sensors: surveillance radar, high resolution radar and electronic support measures. The algorithm is built around Bayes' inference and Kalman filters with the interacting multiple model. The improved performance in the track formation and identity estimation, which accrues by the joint tracking and identification algorithm, is evaluated by Monte Carlo simulation and compared to the performance of filters that process the data provided by each single sensor. The joint tracking and identification algorithm plays an important role in modern surveillance systems with non-cooperative target recognition capabilities.

Semiautomated georadar data acquisition in three dimensions

Under favorable conditions, georadar techniques can provide vivid images of the shallow subsurface (<10–50 m). Although the significant advantages of 3-D georadar surveying strategies are well documented, they generally require much greater expenditures than traditional 2-D approaches. We introduce an efficient, semiautomated 3-D georadar acquisition and processing scheme that does not jeopardize data quality. A standard georadar acquisition unit is integrated with an innovative self‐tracking laser theodolite with automatic target recognition capabilities. Georadar and coordinate data are recorded simultaneously as the georadar antennae are transported steadily across a survey area. While tracking a target prism mounted between the antennae, the theodolite provides real‐time coordinate information with high accuracy in all directions. At ∼1 m/s (moderate walking speed) the coordinates are determined to better than ±0.04 m, and at ∼3 m/s they are better than ±0.07 m. Subsequent to acquisition, semiautomatic processing allows the georadar data to be static corrected, transferred to a regular grid using a novel 2-D Fourier transform method, amplitude modulated, filtered, and displayed. The acquisition component of the new scheme is five to ten times faster than standard step‐mode georadar techniques, and the semiautomatic processing component allows initial 3-D images to be viewed in the field. Typically, a 3-D georadar data set covering a 25 m × 25 m area may be collected and processed in less than 3 hours. One such data set recorded across a former glaciofluvial environment allows reflections from complex river channel sediments and surface features to be readily identified and interpreted.