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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

Optimization of a Multi-Stage ATR System for Small Target Identification

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

false alarm rate↗

Neural Network Target Identification System for False Alarm Reduction

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

ATR↗

The effects of precipitation on radar target identification and imaging

The properties of precipitation which will influence radar system design are discussed. The spatial characteristics of rainfall and the sizes and shapes of raindrops are described. The dielectric behavior of water is combined with these characteristics to determine the effects of rain on electromagnetic waves. These effects include: absorption, scatter, noise emission, phase shift, and depolarization.

Hodge, D. B.↗

The effects of precipitation on radar target identification and imaging

The properties of precipitation which will influence radar system design are discussed. The spatial characteristics of rainfall and the sizes and shapes of raindrops are described. The dielectric behavior of water is combined with these characteristics to determine the effects of rain on electromagnetic waves. These effects include: absorption, scatter, noise emission, phase shift, and depolarization.

Hodge, D. B.↗

Mode Projection Method for Target Mode Identification

A method for projecting flight configuration eigenvectors onto the vector space of the test configuration eigenvectors is proposed. The underlying concept of the proposed method is that any flight configuration eigenvectors that can be replicated by a linear combination of test configuration eigenvectors, is correlated, if the constituent test eigenvectors are themselves correlated. Therefore, the modal test target mode shapes should be those test modes that combine to form the important modes of the flight configurations. This approach also recognizes that it is the deformed shapes that dictate what sections of the structure are “highly strained” (in a relative sense, within each mode shape), and that it is these highly strained sections that require an accurate stiffness representation to achieve an accurate model correlation.

Modal Testing↗

Mode Projection for Target Mode Identification

A method for projecting flight configuration eigenvectors onto the vector space of the test configuration eigenvectors is proposed. The underlying concept of the proposed method is that any flight configuration eigenvectors that can be replicated by a linear combination of test configuration eigenvectors, is correlated, if the constituent test eigenvectors are themselves correlated. Therefore, the modal test target mode shapes should be those test modes that combine to form the important modes of the flight configurations. This approach also recognizes that it is the deformed shapes that dictate what sections of the structure are “highly strained” (in a relative sense, within each mode shape), and that these “highly strained” sections require an accurate stiffness representation to achieve an accurate model correlation.

modal testing↗

Mode Projection Method for Target Mode Identification

A method for projecting flight configuration eigenvectors onto the vector space of the test configuration eigenvectors is proposed. The underlying concept of the proposed method is that any flight configuration eigenvectors (that can be replicated by a linear combination of test configuration eigenvectors) is correlated if the constituent test eigenvectors are themselves correlated. Therefore, the modal test target mode shapes should be those test modes that combine to form the important modes of the flight configurations. This approach also recognizes that it is the deformed shapes that dictate what sections of the structure are “highly strained” (in a relative sense, within each mode shape), and that these “highly strained” sections require an accurate stiffness representation to achieve an accurate model correlation.

Jeffrey A Peck↗

Self-Identifying Reflecting Targets

Proposed scheme for target identification requires no radiation of energy by target itself. Instead, target alters light beam reflected from itself back to interrogating station in unique way. Interrogator then identifies target from its signature impressed on returned beam. Selfidentifying target useful in surveying, navigation, and remote monitoring (for example, of mountain-climbing parties.)

Frazer, R. E.↗

Development of a machine vision guidance system for automated assembly of space structures

The topics are presented in viewgraph form and include: automated structural assembly robot vision; machine vision requirements; vision targets and hardware; reflective efficiency; target identification; pose estimation algorithms; triangle constraints; truss node with joint receptacle targets; end-effector mounted camera and light assembly; vision system results from optical bench tests; and future work.

Eric G Cooper↗

Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen and measured the time and accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting and a small number of people (either odd or even number) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response.

Brain computer interfaces (BCI)↗

Identification of comet nucleus from comet coma.

The study of comets is of important scientific value as part of the exploration of the solar system. This paper proposes a method, called 'stationary target identification,' to detect the nucleus within the coma. The method makes use of the fact that the coma is ever expanding about the nucleus. Therefore, when a radar beam is used for detection, the coma particles will produce a return signal that is Doppler shifted. The effects of the spatial extent of the coma and Doppler bandwidth of the coma returns upon the required data processing are considered. Also different methods of implementation are considered.-

Brock, H. I.↗

Optical pattern recognition III; Proceedings of the Meeting, Orlando, FL, Apr. 21, 22, 1992

Consideration is given to transitioning of optical processing into systems (TOPS), optical correlator hardware, phase-only optical correlation filters, optical distortion-invariant correlation filters, and optical neural networks. Particular attention is given to a test target for optical correlators, a TOPS electronic warfare channelizer program, a portable video-rate optical correlator, a joint transform correlator employing electron trapping materials, a novelty filtered optical correlator using a photorefractive crystal, a comparison of correlation performance of smart ternary phase-amplitude filters with gray-scale and binary input scenes, real-time distortion-tolerant composite filters for automatic target identification, landscaping the correlation surface, fast designing of a circular harmonic filter using simulated annealing, feature-based correlation filters for distortion invariance, automatic target recognition using a feature-based optical neural network, and a holographic inner-product processor for pattern recognition.

Casasent, David P.↗

Simulating Mars: Enabling Testing of the Perseverance Rover Sampling and Caching Subsystem on Earth

The development of the Sampling and Caching Subsystem (SCS) on the JPL Perseverance Rover lies at the intersection of testing, robotics, and geology. The SCS team established three primary system test campaigns and venues to aid in the development of SCS through verification and validation testing – Qualification Model Dirty Testing (QMDT) to provide a venue for testing in a Martian environment, Vehicle System Testbed (VSTB) for testing while integrated with the mobility subsystem on Martian-like terrain, and the Flight Software Testbed (FSWTB) for conducting tests using the flight motor controllers and software system on a hexapod which had the ability to simulate rover tilt. Each venue contributed a vital piece to the SCS building blocks. However, the QMDT venue operating within a 10-ft diameter Thermal Vacuum chamber to simulate Martian environment provided a sui generis opportunity to fine tune the entire sampling and caching process while building the team’s knowledge base about rock drillability, system life, and target selection. On Earth, because Martian rocks are not readily available, the development team must utilize geoanalogs to the rocks and regolith on Mars. Geologists on the team helped establish a set of standard rock types to use for Mars missions, like Basalt, Sandstone, Mudstone, Gypsum, and other related geoanalogs. These geoanalogs are characterized with a standard suite of tests for density, compressibility, and other characteristics to categorize potential drillability. This concept of drillability is what links the geoanalogs on Earth to the samples we collect on Mars. With the simulant characteristics defined, these geoanalog rocks are ready to be drilled into as we do on the Martian surface. A key aspect of interacting with the surface on Mars is rock target identification and selection. The Perseverance robotic system uses the on-board cameras, instrumentation, and software to collect enough information to identify potential scientific targets. With the targets identified, SCS can place the Corer and abrade the surface or collect a sample. For a ground test activity like QMDT, the test team did not have all of the camera and instrumentation systems that the rover does, so the team developed ground test equivalents to process a rock, build a target map, and define the target. The team constructed a Rock Scanning Station to build a 3D point cloud of the rock. This point cloud was then processed and evaluated with predefined and programmed criteria in a Target Downselect Tool. A primary output of the Target Downselect Tool is a defined target that can be uploaded directly to the robotic software system to simulate and build the robotic sequences used in tests. With these insights and programmatic definition of targets, the QMDT test team was able to make the same decisions that the Perseverance surface operations team does. In addition, valuable lessons learned from developing the target selection ground tools and using them were implemented into the tools used for surface operations.

Kim, Junggon↗

Simulation studies and the need for field reflectance studies

There is a need to calibrate satellite data obtained at large polar (i.e., off-nadir) view angles for effects caused by scanner geometry and by the atmosphere. If such effects are not corrected for, then recorded radiance data will contain systematic and random errors which will make accurate target identification and quantification difficult. Adequate calibration of digital radiance data requires both an inductive analysis of digital satellite images and a deductive analysis based upon a priori simulation studies. The inputs to a simulation model (ground reflectance, atmospheric transmission and bandscatter) and their variability are dependent upon (for example) sun-target-sensor geometry. There is a major need to make accurate ground reflectance measurements to provide calibration information on the anisotropy of ground reflectance. There is also a need to assess atmospheric transmission and backscatter from the image itself. A potential simple, inexpensive method for making ground reflectance measurements in the above context is discussed.

Duggin, M. J.↗

Biochemical Detection and Identification False Alarm Rate Dependence on Wavelength Using Laser Induced Fluorescence

Most organic and many inorganic materials absorb strongly in specific wavelength ranges in the deep UV between about 220nm and 300nm. Excitation within these absorption bands results in native fluorescence emission. Each compound or composite material, such as a bacterial spore, has a unique excitation-emission fingerprint that can be used to provide information about the material. The sensitivity and specificity with which these materials can be detected and identified depends on the excitation wavelength and the number and location of observation wavelengths.We will present data on our deep ultraviolet Targeted Ultraviolet Chemical Sensors that demonstrate the sensitivity and specificity of the sensors. In particular, we will demonstrate the ability to quantitatively differentiate a wide range of biochemical agent targets against a wide range of background materials. We will describe the relationship between spectral resolution and specificity in target identification, as well as simple, fast, algorithms to identify materials.Hand-held, battery operated instruments using a deep UV laser and multi-band detection have been developed and deployed on missions to the Antarctic, the Arctic, and the deep ocean with the capability of detecting a single bacterial spore and to differentiate a wide range of organic and biological compounds.

native flourescence↗