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Internet of Things is coming to critical missions such as battlefield, border patrol, search and rescue, critical structure monitoring and surveillance, etc. Internet of Things (IoT) in Critical Missions or Internet of Mission-Critical Things (IoMCT) is propelled by the convergence of sensing, communication, computing, and control. To support IoMCT, the mission-critical networks will need to be flexible and interactive, and still work despite limited bandwidth, intermittent connectivity and with a large number of devices on the network. The focus of IoMCT is to improve surveillance utilizing a network, not fusion of disparate sensor products. Such adaptation, management, and re-organization of information sources, devices, and networks must be accomplished almost entirely autonomously, in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services.

Network-enabled electronic warfare (NEW) is the development of modeling and simulation efforts that explore the advantages and limitations of NEW concepts. The advantages of linking multiple electronic support measures (ESM) and electronic attack (EA) assets to achieve improved capabilities across a networked battle force have yet to be quantified. In this paper, we utilize radar sensors as ESM and EA assets to demonstrate the advantages of NEW in collaborative automatic target recognition (CATR). Signal (waveform) design for radar sensor networks (RSN) in NEW is studied theoretically. The conditions for waveform coexistence and the interferences among waveforms in RSN are analyzed. We apply the NEW to CATR via waveform diversity combining and propose maximum-likelihood (ML)-ATR algorithms for nonfluctuating targets as well as fluctuating targets. Simulation results indicate that our NEW-CATR performs much better than the single sensor-based ATR algorithm for nonfluctuating and fluctuating targets.

In this paper, we propose a Time Difference of Arrival (TDoA) algorithm for passive geolocation based on delay estimation of two correlated wireless channels. It's assumed that the passive receiver is carried by a small flying UAV in the sky, and the transmitter is located on the ground (static or mobile), so Rician flat fading model should be used. To estimate the delay of two correlated channels, Block Phase Estimation (BPE) is used for each wireless channel estimation, and then the two estimated channels are compared to get the best time delay. We also compare it against a cross-correlation-based TDoA algorithm. Simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and Root-Mean-Square-Error (RMSE). Four different Rician fading channel groups are evaluated, and conclusions are drawn for our TDoA algorithm and the cross-correlation-based TDoA algorithm.

1Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019-0016, USA 2Department of Computer Science, The George Washington University, Washington, DC 20052, USA 3Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan 4Air Force Research Laboratory/RHX, Brooks City Base, San Antonio, TX 78235, USA 5Core Software Department, iBiquity Digital Corporation, 150 Allen Road, Suite 201, Basking Ridge, NJ 07920, USA 6School of Information and Telecommunications, Beijing University of Posts and Telecommunications, Beijing 100876, China

Sensing-through-wall will benefit various applica- tions such as emergence rescues and military operations. In order to add more signal processing functionality, it is vital to understand the characterization of sense-through-wall channel. In this paper, we propose a statistical channel model on a basis of real experimental data using UWB noise radar. We employ CLEAN algorithm to obtain the multipath channel impulse response (CIR) and observe that the channel amplitude at each path can be accurately characterized as T location-scale distribution. We also analyze that the multipath contributions arrive at the receiver are grouped into clusters. The time of arrival of clusters can be modeled as a Poisson arrival process, while within each cluster, subsequent multipath contributions or rays also arrive according to a Poisson process. However, these arrival rates are much smaller than those of indoor UWB channels.

In this paper, we propose a discrete-cosine-transform (DCT)-based approach for sense-through-foliage target detection when the echo signal quality is good, and a radar sensor network (RSN) and DCT-based approach when the echo signal quality is poor. A RAKE structure which can combine the echos from different cluster-members is proposed for clusterhead in the RSN. We compared our approach with the ideal case when both echos are available, i.e., echos with target and without target. We also compared our approach against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Simulation results show that our DCT-based scheme works much better than the existing approach, and our RSN and DCT-based approach can be used for target detection successfully while even the ideal case fails to do it.

In this paper, we propose that the foliage clutter follows log-logistic model using maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) on PDF curves between original clutter and statistical model data. The measured clutter data is provided by Air Force Office of Scientific Research (AFOSR). In addition to investigating the log-logistic model, we also compare it with other popular clutter models, namely log-normal, Weibull and Nakagami. We show that the log-logistic model not only achieves the smallest standard deviation (STD) error on estimated model parameters, but also has the best goodness-of-fit and smallest RMSE. Further, the performance of detection at presence of foliage clutter is theoretically analyzed.

Network-enabled Electronic Warfare (NEW) is to develop modeling and simulation efforts to explore the advantages and limitations of network-enabled electronic warfare concepts. The advantages of linking multiple electronic support measures (ESM) and electronic attack (EA) assets to achieve improved capabilities across a networked battleforce have yet to be quantified. In this paper, we will use radar sensors as ESM and EA assets to demonstrate the advantages of NEW in Collaborative Automatic Target Recognition (CATR). We apply the NEW to CATR via waveform diversity combining and and propose maximum-likehood (ML)-ATR algorithms for nonfluctuating target as well as fluctuating target. Simulation results show that our NEW-CATR performs much better than single sensor-based ATR algorithm for nonfluctuating targets or fluctuating targets. Conclusions are drawn based on our analysis and simulations and future research works on this research topic are discussed.

This report describes a mathematical model to predict the temperature increase in a thin layer of tissue culture exposed to electromagnetic energy at a frequency of 35 gigahertz (GHz). The goal of the modeling effort was to compare the calculated temperature rise with experimental valves obtained with mouse microphage cell cultures using an infrared camera. The expected temperature increase was calculated using a commercial finite difference time domain code (FDTD). Two problems arise when using FDTD to simulate high frequency electromagnetic radiation of a thin culture to obtain temperature information important to biologists: small time step (necessitating long simulation time) and extrapolation of temperature from specific absorption rates (SARs). In this paper we describe our numerical calculations and compare their results with experimental observations.