Naval Information Warfare Systems Command

We develop anomaly detectors, i.e., detectors that do not presuppose a signature model of one or more dimensions, for three clutter models: the local normal model, the global normal mixture model, and the global linear mixture model. The local normal model treats the neighborhood of a pixel as having a normal probability distribution. The normal mixture model considers the observation from each pixel as arising from one of several possible classes such that each class has a normal probability distribution. The linear mixture model considers each observation to be a linear combination of fixed spectra, known as endmembers, that are, or may be, associated with materials in the scene, and the coefficients, interpreted as fractional abundance, are constrained to be nonnegative and sum to one. We show how the generalized likelihood ratio test (GLRT) may be used to derive anomaly detectors for the local normal and global normal mixture models. The anomaly detector applied with the linear mixture approach proceeds by identifying target like endmembers based on properties of the histogram of the abundance estimates and employing a matched filter in the space of abundance estimates. To overcome the limitations of the individual models, we develop a joint decision logic, based on a maximum entropy probability model and the GLRT, that utilizes multiple decision statistics, and we apply this approach using the detection statistics derived from the three clutter models. Examples demonstrate that the joint decision logic can improve detection performance in comparison with the individual anomaly detectors. We also describe the application of linear prediction filters to repeated images of the same area to detect changes that occur within the scene over time.

The Data Fusion Model maintained by the Joint Directors of Laboratories (JDL) Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise the expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi- sensor/multi-source systems. Data fusion involves combining information - in the broadest sense - to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people, it can be useful to include consideration of informational and perceptual states in addition to the physical state. Developing cost-effective multi-source information systems requires a method for specifying data fusion processing and control functions, interfaces, and associate databases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology: current developments do not lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data FUsion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data Fusion Tree Architecture model for system description, design and development.

Two sweeping generalizations can be made about most natural systems: They are intrinsically nonlinear and they operate in noisy environments. Examples abound, ranging from weather systems to oscillating chemical reactions to the movements of an eel. The most complex example is arguably the human central nervous system, flooded as it is with the “noise” of modern life.

We enhance the response of a ``stochastic resonator'' by coupling it into a chain of identical resonators. Specifically, we show via numerical simulation that local linear coupling of overdamped nonlinear oscillators significantly enhances the signal-to-noise ratio of the response of a single oscillator to a time-periodic signal and noise. We relate this array enhanced stochastic resonance to the global spatiotemporal dynamics of the array and show how noise, coupling, and bistable potential cooperate to organize spatial order, temporal periodicity, and peak signal-to-noise ratio.

A multitude of sensitivity studies in the literature point to the importance of proper chemical and morphological characterization of particles when the radiative impacts of airborne dusts are modeled. However, the community data set is based on heterogeneous measurement methods relying on varying aerodynamic, chemical, morphological, and optical means. During the Puerto Rico Dust Experiment, size distributions of dust particles from Africa were measured using a variety of aerodynamic, optical, and geometric means. Consistent with the literature, comparisons of these size distributions showed quite dissimilar results. “Measured” volume median diameters varied from 2.5 to 9 μm for various geometric, aerodynamic, optical, and optical inversion methods. Aerodynamic systems showed mixed performance. Column integrated size distributions inverted from AERONET Sun/sky radiance data produced somewhat reasonable results in the coarse mode when given proper constraints and taken in the proper context. The largest systematic errors were found in optical particle counters due to insensitivities to particle size in the 4–10 μm region with further complications due to dust particle morphology and index of refraction issues. As these methods can produce quite dissimilar size distributions, considerable errors in calculated radiative properties can occur if incorrectly modeled into dust parameters. None of the methods compared in this study can adequately reproduce the measured mass extinction or mass scattering efficiency of the dust using spherical geometry methods. Given all of the uncertainties in the sizing methods, we promote the use of fundamental and quantifiable descriptors of particles such as mass as a function of aerodynamic diameter.

As part of the Puerto Rico Dust Experiment (PRIDE), airborne and surface dust particle samples from Africa were collected and subjected to bulk elemental and single‐particle analysis. Airborne samples were collected on polycarbonate filters at various altitudes and underwent single‐particle scanning electron microscopy with energy dispersive analysis with X‐rays (EDAX) to derive elemental ratios of key soil elements. Particle chemistry was related to size and morphological characteristics. At the principle surface site, particles were collected on a Davis Rotating Drum (DRUM) cascade impactor strips in eight stages from 0.1 to 12 μm at 4 hour time resolution. These samples were subjected to X‐ray florescence (XRF) to determine bulk elemental composition from Al through Zn. The elemental data showed good correlation between the DRUM and the aircraft samples. Cluster analysis of single‐particle data resulted in 63 statistically significant clusters. Several clusters can be easily related to their parent mineralogical species. However, as dust particles are to a large extent aggregates, most clusters are based on a continuum of varied mineralogical species and cannot be easily categorized. With 60,500 total particles counted from the airborne filters, a statistically significant number of large particles could be analyzed. Estimated mean surface area modal diameter is ∼5 μm, with an average aspect ratio of 1.9. An apparent change in source region is seen in the morphological data and non alumino‐silicate minerals but is not seen in the aluminum to silicon ratio. We suspect homogenization during long‐range transport.

An efficiency-enhanced power-amplifier system design is presented based on wide-bandwidth envelope tracking (WBET) with application to orthogonal frequency-division multiplexing wireless local area network systems. Envelope elimination and restoration (EER) and WBET are compared in terms of the time mismatch sensitivity between the base-band amplitude path and the RF path, and it is demonstrated that WBET is much less sensitive than EER to these effects. An adaptive time-alignment algorithm for the WBET system is developed and demonstrated. The analysis and algorithm are verified by experimental results. The measurement shows that the peak drain efficiency of the complete system was 30% at a 2.4-GHz orthogonal frequency-division multiplexing output power of 20 dBm.

In this paper, we consider the use of multiple antennas and space-time coding for high data rate underwater acoustic (UWA) communications. Recent advances in information theory have shown that significant capacity gains can be achieved by using multiple-input-multiple-output (MIMO) systems and space-time coding techniques for rich scattering environments. This is especially significant for the UWA channel where the usable bandwidth is severely limited due to frequency-dependent attenuation. In this paper, we propose to use space-time coding and iterative decoding techniques to obtain high data rates and reliability over shallow-water, medium-range UWA channels. In particular, we propose to use space-time trellis codes (STTCs), layered space-time codes (LSTCs) and their combinations along with three low-complexity adaptive equalizer structures at the receiver. We consider multiband transmissions where the available bandwidth is divided into several subbands with guard bands in between them. We describe the theoretical basis of the proposed receivers along with a comprehensive set of experimental results obtained by processing data collected from real UWA communications experiments carried out in the Pacific Ocean. We demonstrate that by using space-time coding at the transmitter and sophisticated iterative processing at the receiver, we can obtain data rates and spectral efficiencies that are not possible with single transmitter systems at similar ranges and depths. In particular, we have demonstrated reliable transmission at a data rate of 48 kb/s in 23 kHz of bandwidth, and 12 kb/s in 3 kHz of bandwidth (a spectral efficiency of 4 bs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> Hz <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) at a 2-km range.

The metrics used for the Fifth Message Understanding Conference (MUC-5) evaluation are a major update to those used for MUC-4 in 1992. The official MUC-5 metrics express error rates while the official MUC-4 metrics express performance in terms of recall and precision (used for MUC-5 only as "unofficial" metrics). This paper discusses the current metrics and the reasons for their adoption.

The author compiles everything a student or experienced developmental engineer needs to know about the supporting technologies associated with the rapidly evolving field of robotics. From the table of contents: Design Considerations * Dead Reckoning * Odometry Sensors * Doppler and Inertial Navigation * Typical Mobility Configurations * Tactile and Proximity Sensing * Triangulation Ranging * Stereo Disparity * Active Triangulation * Active Stereoscopic * Hermies * Structured Light * Known Target Size * Time of Flight * Phase-Shift Measurement * Frequency Modulation * Interferometry * Range from Focus * Return Signal Intensity * Acoustical Energy * Electromagnetic Energy * Optical Energy * Microwave Radar * Collision Avoidance * Guidepath Following * Position-Location Systems * Ultrasonic and Optical Position-Location Systems * Wall, Doorway, andCeiling Referencing * Application-Specific Mission Sensors

This article presents a public goods-based theory that describes the process of producing multifirm, alliance-based, interorganizational communication and information public goods. These goods offer participants in alliances collective benefits that are (a) nonexcludable, in that they are available to all alliance partners whether or not they have contributed, and (b) jointly supplied, in that partners' uses of the good are noncompeting. Two generic types of goods produced are connectivity, the ability of partners to directly communicate with each other through the information and communication system, and communality, the availability of a commonly accessible pool of information to alliance partners. Four types of alliances that can produce these goods are identified: (a) precompetitive, (b) competitive, (c) joint value creation, and (d) value chain. The article examines a variety of factors that influence the production of alliance-based connective and communal goods. Twenty-three integrated propositions are presented. The article concludes with an example of the application of the theoretical model to research on connectivity and communality provided through an alliance-based interorganizational communication and information system linking more than 50 alliance partners.

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

For 26 days in mid‐June and July 2000, a research group comprised of U.S. Navy, NASA, and university scientists conducted the Puerto Rico Dust Experiment (PRIDE). In this paper we give a brief overview of mean meteorological conditions during the study. We focus on our findings on African dust transported into the Caribbean utilizing a Navajo aircraft and AERONET Sun photometer data. During the study midvisible aerosol optical thickness (AOT) in Puerto Rico averaged 0.25, with a maximum &gt;0.5 and with clean marine periods of ∼0.08. Dust AOTs near the coast of Africa (Cape Verde Islands and Dakar) averaged ∼0.4, 30% less than previous years. By analyzing dust vertical profiles in addition to supplemental meteorology and MPLNET lidar data we found that dust transport cannot be easily categorized into any particular conceptual model. Toward the end of the study period, the vertical distribution of dust was similar to the commonly assumed Saharan Air Layer (SAL) transport. During the early periods of the study, dust had the highest concentrations in the marine and convective boundary layers with only a weak dust layer in the SAL being present, a state usually associated with wintertime transport patterns. We corroborate the findings of Maring et al. [2003] that in most cases, there was an unexpected lack of vertical stratification of dust particle size. We systematically analyze processes that may impact dust vertical distribution and speculate that dust vertical distribution predominately influenced by flow patterns over Africa and differential advection coupled with fair weather cloud entrainment, mixing by easterly waves, and regional subsidence.

The Segway Robotic Mobility Platform (RMP) is a new mobile robotic platform based on the self-balancing Segway Human Transporter (HT). The Segway RMP is faster, cheaper, and more agile than existing comparable platforms. It is also rugged, has a small footprint, a zero turning radius, and yet can carry a greater payload. The new geometry of the platform presents researchers with an opportunity to examine novel topics, including people-height sensing and actuation modalities. This paper describes the history and development of the platform, its characteristics, and a summary of current research projects involving the platform at various institutions across the United States.

OBJECTIVE: Military deployment can have profound effects on physical and mental health. Few studies have examined whether interventions prior to deployment can improve mechanisms underlying resilience. Mindfulness-based techniques have been shown to aid recovery from stress and may affect brain-behavior relationships prior to deployment. The authors examined the effect of mindfulness training on resilience mechanisms in active-duty Marines preparing for deployment. METHOD: Eight Marine infantry platoons (N=281) were randomly selected. Four platoons were assigned to receive mindfulness training (N=147) and four were assigned to a training-as-usual control condition (N=134). Platoons were assessed at baseline, 8 weeks after baseline, and during and after a stressful combat training session approximately 9 weeks after baseline. The mindfulness training condition was delivered in the form of 8 weeks of Mindfulness-Based Mind Fitness Training (MMFT), a program comprising 20 hours of classroom instruction plus daily homework exercises. MMFT emphasizes interoceptive awareness, attentional control, and tolerance of present-moment experiences. The main outcome measures were heart rate, breathing rate, plasma neuropeptide Y concentration, score on the Response to Stressful Experiences Scale, and brain activation as measured by functional MRI. RESULTS: Marines who received MMFT showed greater reactivity (heart rate [d=0.43]) and enhanced recovery (heart rate [d=0.67], breathing rate [d=0.93]) after stressful training; lower plasma neuropeptide Y concentration after stressful training (d=0.38); and attenuated blood-oxygen-level-dependent signal in the right insula and anterior cingulate. CONCLUSIONS: The results show that mechanisms related to stress recovery can be modified in healthy individuals prior to stress exposure, with important implications for evidence-based mental health research and treatment.

The use of shifted-spectra, first-derivative spectroscopy (or edge detection), and fast Fourier transform filtering techniques for fluorescence rejection in Raman spectra is demonstrated. These techniques take advantage of the fact that Raman signals are very narrow in comparison to fluorescence bands in order to discriminate between the two. None of these techniques require modification of existing instrumentation. Fast Fourier transform filtering and deconvolution techniques also provide a means of improving spectral resolution and the signal-to-noise ratio.

Research on when and how to use three-dimensional (3D) perspective views on flat screens for operational tasks such as air traffic control is complex. We propose a functional distinction between tasks: those that require shape understanding versus those that require precise judgments of relative position. The distortions inherent in 3D displays hamper judging relative positions, whereas the integration of dimensions in 3D displays facilitates shape understanding. We confirmed these hypotheses with two initial experiments involving simple block shapes. The shape-understanding tasks were identification or mental rotation. The relative-position tasks were locating shadows and determining directions and distances between objects. We then extended the results to four experiments involving complex natural terrain. We compare our distinction with the integral/separable task distinction of Haskel and Wickens (1993). Applications for this research include displays for air traffic control, geoplots for military command and control, and potentially, any display of 3D information.

Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species' range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.

Analysis of acoustic signals recorded from the U.S. Navy's SOund SUrveillance System (SOSUS) was used to detect and locate blue whale (Balaenoptera musculus) calls offshore in the northeast Pacific. The long, low-frequency components of these calls are characteristic of calls recorded in the presence of blue whales elsewhere in the world. Mean values for frequency and time characteristics from field-recorded blue whale calls were used to develop a simple matched filter for detecting such calls in noisy time series. The matched filter was applied to signals from three different SOSUS arrays off the coast of the Pacific Northwest to detect and associate individual calls from the same animal on the different arrays. A U.S. Navy maritime patrol aircraft was directed to an area where blue whale calls had been detected on SOSUS using these methods, and the presence of vocalizing blue whale was confirmed at the site with field recordings from sonobuoys.

In some nonlinear dynamic systems, the addition of noise to a weak periodic signal can increase the detectability of the signal, a phenomenon belonging to a class of noise-induced cooperative behavior known as stochastic resonance (SR). There has been much recent speculation on the possible role of SR in signal processing by sensory neurons. However, most results have focused exclusively on increasing the output signal-to-noise ratio (SNR) of time-periodic signals, even though many real-world signals (e.g., those encountered in some neurophysiological and communications applications) are not of this form. Here we consider a generalization of SR, based on the Shannon mutual information between the transmitted and received signal. This generalization can be applied to cases (e.g., the information transmitted by the output spike train of an integrate-fire model neuron which we consider here), involving aperiodic input signals for which the output SNR might be ill-defined, uninformative, or irrelevant. Since the SR-like effect in the transmitted information disappears with the optimal choice of model parameters, we suggest that such an effect is likely to be particularly relevant to systems, e.g., neuronal populations, in which natural circuit constraints may render parameter optimization impractical.