Search

Airborne radar carried on board unmanned aerial vehicles (UAVs) is serving as the harbinger of new remote sensing applications for security and rescue in inclement environments. The mobility and agility of UAVs, along with intelligent onboard sensors (cameras, acoustics, and radar), are more effective during the early stages of disaster response. The ability of radars to penetrate through objects and operate in low-visibility conditions enables the detection of occluded human subjects on and under debris when other sensing modalities fail. Recently, radars have been deployed on UAVs to measure minute human physiological parameters, such as respiratory and heart rates while sensing through clothing and building materials. Signal processing techniques are critical in enabling UAV-borne radars for human vital sign detection (VSD) in multiple operation modes. UAV radar interferometry provides valuable VSD in both hovering and flying motions. In the synthetic aperture radar (SAR) configuration, UAV-based VSD is available at a high spatial resolution. Novel radar configurations, such as in through-material sensing, UAV swarm, and tethered UAVs, are required to penetrate obstacles, facilitate multitasking, and allow for high endurance, respectively. This article provides an overview of the recent advances in UAV-borne VSD, with a focus on the deployment modes and processing methods.

In this work an experimental radar testbed and dual directional couplers (DDC) are used to collect measurements of the forward and reverse waves at each element, its mutual coupling, and beam-patterns. The collected measured data is used to validate a methodology for assessing radio frequency (RF) performance of a co-located multiple-input multipleoutput (MIMO) radar system transmitting binary phased coded (BP) waveforms. The estimated and measured active reflection coefficient (ARC) and beam-patterns are presented. The effect of different excitations and mutual coupling on the radiated fields is also presented.

Objective: This study develops an electro-encephalography-based method for predicting postoperative delirium early during cardiac surgeries involving deep hypothermia circulatory arrest (DHCA), potentially providing an opportunity to intervene and minimize poor surgical outcome. DHCA is a surgical technique used during cardiac surgeries to facilitate repairs. Deep hypothermia is induced and supplemented by perfusion techniques to protect the brain during circulatory arrest, but concern for cerebral injury still remains. Methods: This research studies whether or not monitoring burst suppression, an electrophysiological phenomenon observed during patient cooling and warming, helps in predicting postoperative delirium, a correlate of poor prognosis. A metric called the burst suppression duty cycle (BSDC), akin to burst suppression ratio, is formulated to characterize this electrophysiological activity. Results: Assuming no complications occur prior to circulatory arrest, delirium diagnoses are correlated with the time elapsed until suppression activity ceases since resuming cardiopulmonary bypass. By comparing against a benchmark the times when BSDC reaches 100%, 15 of 16 cases can be correctly predicted. Similar accuracy can be achieved when querying BSDC progress earlier during warming. Conclusion: Our results show that our BSDC metric is a promising candidate for early detection of postoperative delirium, and motivates further analysis of the causal relationship between postoperative delirium and the procedure transitioning out of circulatory arrest. Significance: The developed methodology anticipates incidences of postoperative delirium during rewarming, which potentially provides an opportunity to intervene and avert it.

This study presents findings at Terahertz (THz) frequency band for non-contact cardiac sensing application. For the first time, cardiac pulse information is simultaneously extracted using THz waves based on the two established principles in electronics and optics. The first fundamental principle is micro-Doppler (mD) motion effect, initially introduced in coherent laser radar system 1, 2 and first experimentally demonstrated for vital sign detection 3. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave). The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). PPG has been a popular technology for pulse diagnosis. Recently it has been widely incorporated into various smart wearables for long-term monitoring, such fitness training and sleep monitoring. Herein, the concept of Terahertz-Wave-Plethysmography (TPG) is introduced, which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle 4. The TPG principle is justified by scientific deduction, electromagnetic wave (EM) simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by Terahertz Electronics Lab at Arizona State University (ASU), Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and stand deviation (std) 1.08 BPM. The results validate the feasibility of radar based plethysmography for direct pulse monitoring. Finally, a comparative study on pulse sensitivity in TPG and rPPG is conducted. The results indicate that the TPG contains more pulsatile from the forehead BOI than that in the rPPG signals and thus generate better heart rate (HR) estimation statistic in the form of empirical cumulative distribution function (CDF) of HR estimation error.

This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.

Small, consumer grade unmanned aerial systems (UAS) provide a cost-effective solution in many wireless communications applications where access to typical terrestrial based communications methods might not be feasible. In this work we characterize and model the small-scale wireless channel effects of the UAS-to-ground wireless channel in residential and mountainous desert terrains. In particular, we focus on constructing statistical models of the wireless channel for small rotary wing UAS flying at low altitudes (

The maximum value of an antenna array’s sidelobe beampattern, or radiation pattern in the power domain, is an important parameter determining its performance. In this paper, when array antenna elements have random phase centers, we approximate the maximum sidelobe value, or peak sidelobe level, as a Gumbel distribution using the extreme value theory (EVT). Before the EVT result, an expression for the beampattern distribution at each angle in the array field of view is found. From this expression, the pointwise convergence of the difference between the beampattern and exponential distributions in the limit of a large number of antennas is obtained. Using the exponential distribution approximation, EVT is applied with samples of the beampattern. A bound is given for the difference between the beampattern sample maximum and its true maximum in the sidelobe region.

We develop and present a novel minimum estimation error variance waveform design method. The method optimizes the spectral shape of a unimodular radar waveform such that the performance of a joint radar-communications system that shares spectrum is maximized. We also develop the novel spectral water-filling successive interference cancelation data rate, which employs the continuous spectral water-filling algorithm to obtain the optimal communications power spectrum. We perform a numerical study to compare the performance of the new technique with the previously derived spectral mask shaping method. The global estimation rate and the data rate capture the radar and the communications performance, respectively.

Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands.

We develop and present a radar waveform design method that optimizes the spectral shape of the radar waveform so that joint performance of a cooperative radar communications system is maximized. The continuous water-filling (WF) spectral-mask shaping method presented in this paper is based on the previously derived spectral-mask shaping technique. However, the method presented in this paper is modified to utilize the continuous spectral water-filling algorithm to improve communications performance. We also introduce additional practical system constraints on the autocorrelation peak side-lobe-to-main-lobe ratio and radar waveform spectral leakage. Finally, we perform a numerical study to compare the performance of the continuous WF spectral-mask-shaping method with the previously derived method. The global estimation rate, which also accounts for non-local estimation errors, and the data rate capture radar and communications performance respectively.

Though it is often taken as a truism that communication contributes to organizational productivity, there are surprisingly few empirical studies documenting a relationship between observable interaction and productivity. This is because comprehensive, direct observation of communication in organizational settings is notoriously difficult. In this paper, we report a method for extracting network and speech characteristics data from audio recordings of participants talking with each other in real time. We use this method to analyze communication and productivity data from seventy-nine employees working within a software engineering organization who had their speech recorded during working hours for a period of approximately 3 years. From the speech data, we infer when any two individuals are talking to each other and use this information to construct a communication graph for the organization for each week. We use the spectral and temporal characteristics of the produced speech and the structure of the resultant communication graphs to predict the productivity of the group, as measured by the number of lines of code produced. The results indicate that the most important speech and network features for predicting productivity include those that measure the number of unique people interacting within the organization, the frequency of interactions, and the topology of the communication network.

We derive bounds on mutual information for arbitrary estimation problems in additive noise, modeled using Gaussian mixtures. Previous work exploiting the I-minimum-mean-squared-error (MMSE) formula to formulate a bridge between bounds on the MMSE for Gaussian mixture model estimation problems and bounds on the mutual information are generalized to allow arbitrary noise modeling. A novel upper bound on estimation information is also developed for the general estimation case. In addition, limits are analyzed to develop bounds on arbitrary entropy, asymptotic behavior of all bounds, and bound errors with some results bridged back to the MMSE domain.

Wireless mediums, such as RF, optical, or acoustical, provide finite resources for the purposes of remote sensing (such as radar) and data communications. Often, these two functions are at odds with one another and compete for these resources. Applications for wireless technology are growing rapidly, and RF convergence is already presenting itself as a requirement for both users as consumer and military system requirements evolve. The broad solution space to this complex problem encompasses cooperation or codesigning of systems with both sensing and communications functions. By jointly considering the systems during the design phase, rather than perpetuating a notion of mutual interference, both system's performance can be improved. We provide a point of departure for future researchers that will be required to solve this problem by presenting the applications, topologies, levels of system integration, the current state of the art, and outlines of future information-centric systems.

Lattices provide useful structure for distributed coding of correlated sources. A common lattice encoder construction is to first round an observed sequence to a `fine&rsquo, lattice with dither, then produce the result&rsquo, s modulo to a `coarse&rsquo, lattice as the encoding. However, such encodings may be jointly-dependent. A class of upper bounds is established on the conditional entropy-rates of such encodings when sources are correlated and Gaussian and the lattices involved are a from an asymptotically-well-behaved sequence. These upper bounds guarantee existence of a joint&ndash, compression stage which can increase encoder efficiency. The bounds exploit the property that the amount of possible values for one encoding collapses when conditioned on other sufficiently informative encodings. The bounds are applied to the scenario of communicating through a many-help-one network in the presence of strong correlated Gaussian interferers, and such a joint&ndash, compression stage is seen to compensate for some of the inefficiency in certain simple encoder designs.

In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar communications. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms., 14 pages, 17 figures, 1 table, published in IEEE Transactions on Intelligent Transportation Systems

We investigate methods of co-existence between radar and communications systems. Each system typically considers the other system a source of interference. Consequently, the traditional solution is to isolate the two systems spectrally or spatially. By considering a cooperative radar and communications signaling scheme, we derive achievable bounds on performance for a receiver that observes communications and radar return in the same frequency allocation. We assume the radar and communications operations to be a single joint system. Bounds on performance of the joint system are measured in terms of data information rate for communications and a novel radar estimation information rate for the radar.

West African regions along the Niger River experience climate and land cover changes that affect hydrological processes and therewith the distribution of fresh water resources (WR). This study provides an investigation of long-term changes in terrestrial water storages (TWS) of the Niger River basin and its subregions by analyzing a decade of satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) mission. The location of large trends in TWS maps of differently processed GRACE solutions points to rising groundwater stocks. Soil moisture data from a global land surface model allow separating the effect of significantly increasing amount of WR from that of TWS variations. Surface water variations from a global water storage model validated with observations from altimetry data were applied to estimate the groundwater component in WR. For the whole Niger, a rise in groundwater stocks is estimated to be 93 ± 61 km3 between January 2003 and December 2013. A careful analysis of uncertainties in all data sets supports the significance of the groundwater rise. Our results confirm previous observations of rising water tables, indicating that effects of land cover changes on groundwater storage are relevant on basin scales. Areas with rising water storage are stocking a comfortable backup to mitigate possible future droughts and to deliver water to remote areas. This has implications for Niger water management strategies. Increasing groundwater recharges may be accompanied by reduction in water quality. This study helps to inform authority's decision to mitigate its negative impacts on local communities.

This paper investigates power control and relay selection in Full Duplex Cognitive Relay Networks (FDCRNs), where the secondary-user (SU) relays can simultaneously receive and forward the signal from the SU source. We study both non-coherent and coherent scenarios. In the non-coherent case, the SU relay forwards the signal from the SU source without regulating the phase, while in the coherent scenario, the SU relay regulates the phase when forwarding the signal to minimize the interference at the primary-user (PU) receiver. We consider the problem of maximizing the transmission rate from the SU source to the SU destination subject to the interference constraint at the PU receiver and power constraints at both the SU source and SU relay. We develop low-complexity and high-performance joint power control and relay selection algorithms. The superior performance of the proposed algorithms are confirmed using extensive numerical evaluation. In particular, we demonstrate the significant gain of phase regulation at the SU relay (i.e., the gain of the coherent mechanism over the noncoherent mechanism)., Comment: The 51st Annual Conference on Information Systems and Sciences 2017 (IEEE CISS 2017)

In this article, we study the mean-squared-error performance of Bayesian direction-of-arrival (DOA) estimation in which prior belief about the target location is incorporated into the estimation process. Our primary result is an extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) direction-of-arrival estimation. We work in a general framework in which the prior information used in the MAP estimation may not match the actual target distribution. In particular, when the prior is incorrect, the MAP estimator degrades relative to the performance of a MAP estimator with the correct prior. Our methods are able to accurately predict the performance of a MAP estimator in this more general situation. We apply our methods to investigate the sensitivity of MAP direction-of-arrival estimation to mismatches between the chosen prior and the actual angular distribution of the target.

Millimeter-wave remote sensing technology can significantly improve measurements of volcanic eruptions, yielding new insights into eruption processes and improving forecasts of drifting volcanic ash for aviation safety. Radiometers can measure water vapor density and temperature inside eruption clouds, improving on existing measurements with infrared cameras that are limited to measuring the outer cloud surface. Millimeter-wave radar can measure the 3D mass distribution of volcanic ash inside eruption plumes and their nearby drifting ash clouds. Millimeter wavelengths are better matched to typical ash particle sizes, offering better sensitivity than longer wavelength existing weather radar measurements, as well as the unique ability to directly measure ash particle size in-situ. Here we present sensitivity calculations in the context of developing the WAMS (Water and Ash Millimeter-wave Spectrometer) instrument. WAMS, a radar/radiometer system designed to use off-the-shelf components, would be able to measure water vapor and ash throughout an entire eruption cloud, a unique capability., 9 pages, 5 figures, accepted by IEEE Transactions on Geoscience and Remote Sensing

A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space–delay correlation and covariance matrices from 15‐s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done using a support vector machine (SVM), whose outputs are averaged using a running 15-minute window to obtain a final prediction score. The algorithm was tested on 19 of 21 patients in the Freiburg EEG data set who had three or more seizures, predicting 71 of 83 seizures, with 15 false predictions and 13.8h in seizure warning during 448.3h of interictal data. The proposed algorithm scales with the number of available EEG signals by discovering the variations in correlation structure among any given set of signals that correlate with seizure risk.

An asymptotic technique is presented for finding the spectral efficiency of multiantenna links in spatially distributed wireless networks where transmitters have channel-state-information (CSI) corresponding to their target receiver. Transmitters are assumed to transmit independent data streams on a limited number of channel modes which limits the rank of transmit covariance matrices. An approximation for the spectral efficiency in the interference-limited regime as a function of link-length, interferer density, number of antennas per receiver and transmitter, number of transmit streams, and path-loss exponent is derived. It is found that targeted-receiver CSI, which can be acquired with low overhead in duplex systems with reciprocity, can increase spectral efficiency several fold, particularly when link lengths are large, node density is high, or both. Additionally, the per-link spectral efficiency is found to be a function of the ratio of node density to the number of receiver antennas, and it can often be improved if nodes transmit using fewer streams. These results are validated for finite-sized systems by Monte-Carlo simulation and are asymptotic in the regime where the number of users and antennas per receiver approach infinity.

Bounds are developed on the maximum communications rate between a transmitter and a fusion node aided by a cluster of distributed receivers with limited resources for cooperation, all in the presence of an additive Gaussian interferer. The receivers cannot communicate with one another and can only convey processed versions of their observations to the fusion center through a Local Array Network (LAN) with limited total throughput. The effectiveness of each bound’s approach for mitigating a strong interferer is assessed over a wide range of channels. It is seen that, if resources are shared effectively, even a simple quantize-and-forward strategy can mitigate an interferer 20 dB stronger than the signal in a diverse range of spatially Ricean channels. Monte-Carlo experiments for the bounds reveal that, while achievable rates are stable when varying the receiver’s observed scattered-path to line-of-sight signal power, the receivers must adapt how they share resources in response to this change. The bounds analyzed are proven to be achievable and are seen to be tight with capacity when LAN resources are either ample or limited. Keywords: distributed reception; communications networks; channel capacity; relay channels; interference mitigation

In wireless communications systems that potentially operate in interference, acquisition and temporal alignment of a transmitted signal by a receiver can be the most fragile component of the link. In this paper, synchronization detection in the presence of interference for multiple-input multiple-output (MIMO) communication is discussed. Here, synchronization indicates signal acquisition and timing estimation at the receiver, and is formulated as a binary statistical hypothesis test. Transmit sequences from multiple antennas are received by multiple antennas in noisy environments with spatially correlated noise (interference). Flat-fading and frequency-selective channel models for both the interference and signal of interest are considered. By applying well-known multiple antenna approaches to the MIMO synchronization problem, a number of new synchronization test statistics are introduced. These test statistics are motivated by minimum-mean-square-error (MMSE) beamformers, generalized-likelihood ratio test (GLRT), least-squared (LS) channel estimation, and spatial invariance. Test statistics appropriate for orthogonal-frequency-division-multiplexing (OFDM) systems are considered, including test statistics that take advantage of cyclic prefixes and of pilot sequences within an OFDM symbol. Performances of various test statistics in terms of probability of missing detection for some probability of a false detection are shown to vary by multiple orders of magnitude in the presence of interference.

Ground moving-target indication (GMTI) provides both an opportunity and challenge for coherent multiple-input multiple-output (MIMO) radar. MIMO techniques can improve a radar's angle estimation and the minimum detectable velocity (MDV) for a target. However, the challenge of clutter mitigation places significant constraints on MIMO radar waveforms. In this paper, the loss of target return because of clutter mitigation (signal-to-noise ratio (SNR) loss) is the driving performance metric. The ideal, orthogonal repeated-pulse waveform is shown not to exist. Pulse-to-pulse time-varying waveforms, such as Doppler-division multiple access (DDMA), are shown to offer SNR loss performance approaching ideal MIMO systems.

Wireless channel data was collected in Cambridge, Massachusetts for diverse propagation environments over distances ranging from tens of meters to several kilometers using mobile 2.4-GHz transmitters and receivers. The 20-MHz bandwidth signals from eight individually movable van-top antennas were Nyquist sampled simultaneously with 12-bit accuracy. Although path-loss variance for any given link length within single residential/urban neighborhoods was large, single streets typically exhibited path-loss, L(dB)=-10 log10 (Pr/Pt) ? 10? log10 r + C, where P is the received or transmitted power, r the link-length, ? the street-dependent path-loss coefficient, and C the loss incurred at street intersections. Measurements yielded ? ? 1.5 + 3.2s ± 0.27 for 2 < ? < 5; s is the fraction of the street length having a building gap on either side. Experiments over links as short as 100 meters indicate a 10-dB advantage in estimating path loss for this model compared to optimal linear estimators based on link length alone. Measured air-to-ground links were well modeled by ? = 2 for the elevated LOS path, and by stochastic log-normal attenuation for the ground-level scattering environment. These models permit path-loss predictions based on readily accessible environmental parameters, and lead to efficient nodal placement strategies for full urban coverage.

A multi-input multi-output (MIMO) radar system, unlike standard phased-array radar, can transmit via its antennas multiple probing signals that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar enables superior capabilities compared with a standard phased-array radar. One of the common practices in radar has been range compression. We first address the question of ldquoto compress or not to compressrdquo by considering both the Cramer-Rao bound (CRB) and the sufficient statistic for parameter estimation. Next, we consider MIMO radar waveform optimization for parameter estimation for the general case of multiple targets in the presence of spatially colored interference and noise. We optimize the probing signal vector of a MIMO radar system by considering several design criteria, including minimizing the trace, determinant, and the largest eigenvalue of the CRB matrix. We also consider waveform optimization by minimizing the CRB of one of the target angles only or one of the target amplitudes only. Numerical examples are provided to demonstrate the effectiveness of the approaches we consider herein.

We investigate methods of co-existence between radar and communications systems. Since historically each system views the other as a source of interference, the systems are traditionally isolated spectrally or spatially in order to prevent performance degradation. By considering a cooperative radar and communications signaling scheme, we derive achievable bounds on performance for a receiver that observes communications and radar return in the same frequency allocation. These inner bounds on performance will be derived in scenarios where the radar system has to perform target parameter estimation for multiple targets. Additionally, a novel inner bound that maximizes the Fisher information for radar parameter estimation is also derived. Bounds on performance of the joint system are measured in terms of data information rate for communications and radar estimation information rate parameterization for the radar.

Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency and link reliability for a given total transmit power. Increased capacity is achieved by introducing additional spatial channels which are exploited using space-time coding. The spatial diversity improves the link reliability by reducing the adverse effects of link fading and shadowing. The choice of coding and the resulting performance improvement are dependent upon the channel phenomenology. In this paper, experimental channel-probing estimates are reported for outdoor environments near the personal communication services frequency allocation (1790 MHz). A simple channel parameterization is introduced. Channel distance metrics are introduced. Because the bandwidth of the channel-probing signal (1.3 MHz) is sufficient to resolve some delays in outdoor environments, frequency-selective fading is also investigated. Channel complexity and channel stationarity are investigated. Complexity is associated with channel-matrix singular value distributions. Stationarity is associated with the stability of channel singular value and singular vector structure over time.

Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Increased capacity is achieved by introducing additional spatial channels that are exploited using space-time coding. In this paper, the environmental factors that affect MIMO capacity are surveyed. These factors include channel complexity, external interference, and channel estimation error. The maximum spectral efficiency of MIMO systems in which both transmitter and receiver know the channel (using channel estimate feedback) is compared with MIMO systems in which only the receiver knows the channel. Channel complexity is studied using both simple stochastic physical scattering and asymptotic large random matrix models. Both uncooperative (worst-case) and cooperative (amenable to multiuser detection) interference are considered. An analysis for capacity loss associated with channel estimation error at the transmitter is introduced.

Waveform design that allows for a wide variety of chirps has proven benefits. However, dictionary based optimization is limited and gradient search methods are often intractable. A new method is proposed using differential evolution (DE) to design cubic chirps with coefficients constrained to the three-dimensional (3D) unit sphere. Nonlinear functions sufficiently approximated by a third order Maclaurin series can be represented in this chirp space. Cascaded integrator methods for generating polynomial chirps allow for practical implementation in real world systems. While simplified tracking models and finite waveform dictionaries have information theoretic results, we explore two-dimensional (2D) tracking continuous waveform design in cluttered environments.

We investigate cooperative radar and communications signaling. While each system typically considers the other system a source of interference, by considering the radar and communications operations to be a single joint system, the performance of both systems can, under certain conditions, be improved by the existence of the other. As an initial demonstration, we focus on the radar as relay scenario and present an approach denoted multiuser detection radar (MUDR). A novel joint estimation and information theoretic bound formulation is constructed for a receiver that observes communications and radar return in the same frequency allocation. The joint performance bound is presented in terms of the communication rate and the estimation rate of the system., Comment: 6 pages, 2 figures, to be presented at 2014 IEEE Radar Conference

In this letter, the optimal spectral efficiency for a given message size that minimizes the probability of causing disruptive interference for ad hoc wireless networks or cognitive radios is investigated. Implicitly, the trade being optimized is between longer transmit duration and wider bandwidth versus higher transmit power. Both single-input single-output (SISO) and multiple-input multiple-output (MIMO) links are considered. Here, a link optimizes its spectral efficiency to be a "good neighbor." The probability of interference is characterized by the probability that the signal power received by a hidden node in a wireless network exceeds some threshold. The optimization is a function of the transmitter-to-hidden-node channel exponent. It is shown that for typical channel exponents a spectral efficiency of slightly greater than 1 b/s/Hz per antenna is optimal.

We have searched for two-body charmless decays of B mesons to purely hadronic exclusive final states including $\omega$ or $\phi$ mesons using data collected with the CLEO II detector. With this sample of $6.6 \times 10^6$ B mesons we observe a signal for the $\omega K^+$ final state, and measure a branching fraction of B($B^+ \to \omega K^+$) = 1.5 +0.7 -0.6 +-0.2 $\times 10^{-5}$. We also observe some evidence for the $\phi K^*$ final state, and upper limits are given for 22 other decay modes. These results provide the opportunity for studies of theoretical models and physical parameters.