The fog node placement problem has become an important topic in the field of the internet of things. In this paper, we explore a specific scenario where all fog nodes (FNs) are connected to a base station through a minimum spanning tree and we assume they all have the same radius of coverage. We also assume each FN has the capacity to process all the data coming from the end users (EUs) within the radius of coverage. We evaluate the effectiveness of difference placement algorithms using information elasticity concepts.

This paper proposes a MIMO imaging method with an iterative-based super-resolution technique for automotive radar applications. Vehicle radars have recently used 4D imaging radar, offering improved detection ranges and high-resolution capability. The application of imaging radar technology aims to extend the maximum detection distance through noise reduction techniques while also enabling the miniaturization of vehicle radars using the MIMO approach. To enhance the maximum detection distance, we employ a Wavelet-based noise reduction method for range FFT. Additionally, for improved angular resolution, we use a MIMO radar implementation based on a super-resolution algorithm, in contrast to the conventional MIMO imaging method that utilizes the FFT algorithm. Specifically, we focus on an emerging iterative-based algorithm, which effectively addresses the complexity issues associated with super-resolution techniques. Through extensive experiments, we validate the effectiveness of this proposed method. The results demonstrate its potential in realizing wide detection distance and high-resolution for vehicle radar systems.

In this paper, a feature extraction technique based on the electromagnetic representation of radar signals is presented. In particular, we focus on ground penetrating radar imaging, where we model the backscatter from varying two dimensional geometric shapes with arbitrary local coordinate rotations. Due to the electrically small nature of buried targets, and the bending of the radar signal at the air-soil interface we focus on exact methods to model the surface current density induced on scattering surfaces. Overcomplete basis sets are derived from the electromagnetic descriptions to represent the scene in a sparse manner. From this proposed modeling framework we devise a novel methodology to exploit the prediction of scattering behavior to extract features for classification from radar scenes when multiple buried scattering surfaces are present. We see that our method can identify and reconstruct buried scattering geometries in the presence of false targets that are brought about from the nonlinear nature of the exact electromagnetic modelling methods. A noniterative algorithm based on the conjugate of Green’s function is developed to solve for the surface current in an unknown domain using multi-frequency, multi-aperture data. Our modeling and feature extraction algorithms are numerically validated for different target shapes buried in lossy soil profiles.

The Rician${K}$-factor has been extensively used to characterize indoor and outdoor wireless propagation channels based on experimental and analytical models. In this paper, the$K$-factor statistics are derived analytically and experimentally validated for LOS and NLOS scenarios. Receiver operating characteristics (ROC) are formulated based on the derived detection statistics such that an optimal decision threshold can be selected to better enhance LOS/NLOS detection accuracy compared to usual methods. The proposed approach is shown to have a greater LOS/NLOS discrimination accuracy than other state-of-the-art approaches. Furthermore, there is evidence that a second metric can be obtained which characterizes the current environment and provides a confidence value, which can be used to inform further decisions.

In this paper, a wideband direct modeling and feature extraction method for two dimensional geometric objects with arbitrary local coordinate rotations is considered. In particular we focus on the problem of performing feature extraction for the case of electrically small objects which is particularly applicable to ground penetrating radar (GPR) applications. We first derive low frequency surface current density solutions for a class of simple two dimensional scatterers buried below a rough stochastic surface in a lossy half-space. Then for the inverse problem, a novel methodology is developed for pose and location invariant feature extraction derived from basis sets emerging from our proposed modeling framework. Our modeling and feature extraction algorithms are validated for different target shapes buried in lossy soil profiles.

In this paper, generalized expressions for system gain in a coherent communication system are developed. In an ad hoc or distributed network, the transmit and receive operations can be coordinated at the RF level to produce an array gain, similar to a fixed antenna array; such formations are known as coherent distributed arrays. The system examined in this paper consists of two open-loop coherent distributed arrays: one transmitter array and one receiver array. The gain of the entire system containing both arrays is described by the coherent communication gain. The concept of group array factor, which is the multi-receiver extension of array factor, is also introduced. Group array factor allows for a characterization of gain independent from propagation model and properties of the individual transmitters and receivers. The univariate optimization problem of maximizing group array factor as a function of transmitter array beam angle in the azimuth plane is described, and a practical bounding on the search space is introduced. The physical dimensions of the communication system are analyzed in terms of their influence on the objective function, and a condition for guaranteeing convexity of the bounded objective function is given. An example transmitter array-receiver array system is introduced and used to showcase the concepts herein.

We present the design and implementation of a portable, digital, real-time random noise radar system operating in the ultrahigh frequency range for through-the-wall detection and imaging. Noise radar technology is combined with modern digital signal processing approaches to architect a system to covertly perform range imaging of obscured stationary and moving targets as well as to detect the presence of humans via micro-Doppler detection combined with empirical mode decomposition. We model the propagation and sampling nonidealities in the system and propose techniques to overcome the effect of these nonidealities. Experimental results demonstrate the system's capability to image target scenes and characterize human activity from different stand-off distances.

Far-field target detection, multi-sensor communications, and security are essential requirements of first-emergency networks. A radar-communications system is a potential opportunistic spectrum access (OSA) solution, harnessing the coexisting advantages of radio detection and ranging (RADAR), and wireless communications. A multi-functional waveform has been designed, by embedding an Orthogonal Frequency Division Multiplexing (OFDM) signal within a spectrally notched ultra-wideband (UWB) random noise waveform. Extending that development, this paper analyzes the waveform's Bit-Error-Rate (BER) and Ambiguity Function (AF) formulations to demonstrate its OSA ability, that offers reliable multi-user communications, and high range and Doppler resolution in target detection. We further conclude that up to 30% of the available UWB bandwidth can be simultaneously utilized for concealed data communications without adversely affecting radar performance or its physical layer covertness.

This paper examines the results of our research on the use of ultra-wideband noise waveforms for imaging objects behind walls. The advantages of using thermally generated noise as a probing signal are introduced. The technique of heterodyne correlation, used to inject coherence in the random noise probing signal and to collapse the wideband reflected signal into a single frequency, is presented. We address issues related to locating, detection, and tracking humans behind walls using the Hilbert-Huang transform approach for human activity characterization. The results indicate that noise radar technology combined with modern signal processing approaches is indeed a viable technique for covert high-resolution imaging of obscured stationary and moving targets.

Although techniques for resolution enhancement in single-aspect radar imaging have made rapid progress in recent years, it does not necessarily imply that such enhanced images will improve target identification or recognition. However, when multiple looks of the same target from different aspects are obtained, the available knowledge increases, allowing more useful target information to be extracted. Physics-based image fusion techniques can be developed by processing the raw data collected from multiple inverse synthetic aperture radar sensors, even if these individual images are at different resolutions. We derive an appropriate data fusion rule to generate a composite image containing enhanced target shape characteristics for improved target recognition. The rule maps multiple data sets collected by multiple radars with different system parameters on to the same spatial-frequency space. The composite image can be reconstructed using the inverse 2-D Fourier transform over the separated multiple integration areas. An algorithm called the Matrix Fourier Transform is proposed to realize such a complicated integral. This algorithm can be regarded as an exact interpolation such that there is no information loss caused by data fusion. The rotation centers need to be carefully selected to properly register the multiple images before performing the fusion. A comparison of the image attribute rating curve between the fused image and the spatially averaged images quantifies the improvement in the detected target features. The technique shows considerable improvement over a simple spatial averaging algorithm and thereby enhances target recognition.

Resolution enhancement techniques in radar imaging have attracted considerable interest in recent years. In this work, we develop an iterative sidelobe apodization technique and investigate its applications to synthetic aperture radar (SAR) and inverse SAR (ISAR) image processing. A modified noninteger Nyquist spatially variant apodization (SVA) formulation is proposed, which is applicable to direct iterative image sidelobe apodization without using computationally intensive upsampling interpolation. A refined iterative sidelobe apodization procedure is then developed for image-resolution enhancement. Examples using this technique demonstrate enhanced image resolution in various applications, including airborne SAR imaging, image processing for three-dimensional interferometric ISAR imaging, and foliage-penetration ultrawideband SAR image processing.

A new technique has been developed that permits coherent processing of backscatter data acquired by a radar system transmitting ultrawideband (UWB) random noise waveforms and processing the received signals using a heterodyne correlation receiver. This technique has been used in various applications, such as Doppler estimation, polarimetry, interferometry, buried-object detection, synthetic aperture radar (SAR) imaging, inverse SAR (ISAR) imaging, foliage penetration imaging, etc. One of the major advantages of the noise radar system is its inherent immunity to external interference. In such a radar system, the correlation receiver consists of a programmable variable-delay line, a mixer followed by a lowpass filter. One drawback of this type of receiver is that it sequentially processes the target returns, thus limiting the system response time and the dynamic detection range. We have recently integrated a novel heterodyning acoustooptic (AO) time-integrating correlation receiver that uses a traveling-wave AO deflector for wideband signal processing. The transmit waveform modulates the intensity of a laser diode that is multiplied by the traveling-wave modulation produced by the AO deflector, and the correlation is time-integrated on a charge-coupled device photodetector array. The principal advantages of this AO correlation receiver are its ability to generate a large range of variable delays, as well as to perform the signal correlation operation in parallel. Compared to the conventional sequential correlation receiver using a variable stepped delay line and correlator, implementation of the AO variable-delay line and heterodyning correlator can 1) reduce the processing time and greatly increase the processing gain due to the parallel correlation mechanism and 2) greatly increase the number of range cells depending on the number of resolvable spots of the AO deflector. This results in rapid data acquisition, longer integration time on parallel detector pixels (3000 pixels), and improved SNR. It is also shown that this radar has more range gates (up to 1000), which ultimately improves the detectable range resolution. Furthermore, several field experiments performed with different target arrangements demonstrate that the acoustooptic variable-delay line and correlator is able to profile various targets instantaneously and with very high SNR.

Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring.

Multi-dimensional high resolution is one of the most important factors in automatic target recognition (ATR) using the synthetic aperture radar (SAR) or inverse SAR (ISAR) images. In this work, we propose a resolution enhancement technique in three-dimensional (3-D) interferometric SAR/ISAR (IF SAR/ISAR) imaging using an iterative sidelobe apodization procedure.

Cross correlation receiver is one of the most important parts in a random noise radar system. In this paper, the impact of different correlation receiving techniques on the imaging performance of ultra wideband (UWB) random noise radar is studied. Three types of correlation receivers, namely, the ideal analog correlation receiver, the digital-analog correlation receiver, and the fully digital correlation receiver, are discussed.

Shape analysis has not been considered in remote sensing as extensively as in other pattern recognition applications. However, shapes such as those of geometric patterns in agriculture and irregular boundaries of lakes can be extracted from the remotely sensed imagery even at relatively coarse spatial resolutions. This paper presents a procedure for efficiently retrieving and representing shapes of interesting features in remotely sensed imagery using supervised classification, object recognition, parametric contour tracing, and proposed piecewise linear polygonal approximation techniques. In addition, shape similarity can be measured by means of a computationally efficient metric. The study was conducted on a time series of radiometric and geometric rectified Landsat Multispectral Scanner (MSS) images and Thematic Mapper (TM) images, covering the scenes containing lakes in the Nebraska Sand Hills region. The results validate the effectiveness of the proposed processing chain in change detection of lake shapes and show that shape similarity is an important parameter in quantitatively measuring the spatial variations of objects.

The information content in remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between different target types, and the type and amount of noise present in the imagery. Various statistical and textural measures are used to characterize image information content, based upon which different image processing techniques are employed to quantify this parameter. Previous work in this area have resulted in three different approaches for quantifying the image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well refined, are difficult to apply to all types of remote sensing imagery. We have developed an approach based on the use of classification accuracy to quantify image information content, since loss of information occurs if pixels in the image are wrongly classified. Using this approach, we have separately developed negative exponential models relating information content to image parameters such as spatial and spectral resolution. In this paper, we describe a preliminary unified information content model and assess its performance using hyperspectral AVIRIS imagery. The model combines the effects of the above parameters and takes into account the interrelationships between them with respect to information contained within the image. Using the model, appropriate trade-offs between the parameters can be investigated for obtaining a specific value for image information content for a particular application.

Change detection is an important application of remote sensing. This paper presents our approach to retrieve and represent interesting shapes in the remotely sensed imagery using supervised classification, edge detection, and polygonal approximation techniques, and to compare the shape similarity by a computationally efficient metric. The experiments were conducted on a time series of calibrated and registered Landsat MSS images, covering the scene of the lakes in western Nebraska. The results show the effectiveness of the shape-based change detection approach, which is potentially useful for specific applications such as the study of the lake change response to short or long term climatic variation and flood or drought monitoring.

An airborne low-cost synthetic aperture radar (SAR) is under development at the University of Nebraska-Lincoln. The SAR system is an X-band, stepped-chirp frequency modulation (SCFM) radar system. One of its unique features is that the waveform generation consists of a timing-controlled D/A converter and VCO arrangement to synthesize the SCFM signal, whereby allowing for less design complexity and a much lower overall system cost. In this paper, we present a brief description of the system and some computer simulation results.

The Generalized Rayleigh-Gans (GRG) approach is used for the scattering cross section computation of leaves at low frequencies, while the Physical Optics (PO) approach is employed at high frequencies. In this paper, the magnitude of the scattering amplitude of the leaf is calculated using the two analytical methods (GRG PO) at different frequencies (i.e., L, C and X bands) and with different leaf shapes (circular and elliptical). In general, the difference between these models is of the order of several dB.

Several spaceborne and airborne lidar systems have been launched for vegetation canopy studies. Previous research has shown that lidars are useful tools for remote sensing of vegetation architecture. To support its Airborne Remote Sensing Program, the University of Nebraska has developed a multiwavelength airborne polarimetric lidar system. This system employs a Nd:YAG laser which emits radiation at two wavelengths: the fundamental at 1064 nm and the frequency-doubled at 532 nm. Both laser beams are highly linearly polarized (100:1 extinction ratio) and have a beam divergence angle of /spl sim/4 mrad. The receiver consists of four channels, which enable dual-wavelength and dual-polarization detection. In addition to the polarimetric information that could be gathered, this lidar system also has ranging capability and is able to record the whole lidar waveform. Thus, our lidar is capable of performing studies of vegetation canopy structure as well as characterization of vegetation depolarization. The system has been packaged to fly aboard a Piper Saratoga aircraft from a height of 1000 m. In this paper, we will present the details of the lidar system design, instrumentation, the system alignment and preliminary ground test results.

A coherent ultra-wideband random noise radar system operating in the 1-2-GHz frequency range has been developed at the University of Nebraska. A unique signal processing procedure based on heterodyne correlation techniques preserves phase coherence within the system, thereby enabling it to be used for synthetic aperture radar (SAR) imaging. Data acquisition is performed using a rotating boom with antennas installed atop a van containing radar equipment. This setup facilitates a simple and low-cost mobile SAR implementation, best suited for short-range quasi-stripmap Arc-SAR imaging. The use of ultra-wideband signals provides reasonable resolution of the obtained imagery. The amplitude and the phase response of the system are used to form the frequency-domain target scattering profile matrix, which are then transformed into a SAR image. The paper discusses the theory of SAR imaging using random noise signals and presents a detailed description of the radar and experimental imagery obtained using this system.

A new method for measuring the effective complex relative permittivity of a reflecting surface is presented. The approach is based on the two-ray model. We derive an equation of a circle representing the complex reflection coefficient which relates the incidence angle, frequency, and received power from the path gain using the two-ray model. The intersection point of three such circles at different heights, while maintaining the same incidence angle, yields the correct complex reflection coefficient value. By measuring the received power for both the vertical and horizontal polarizations, the relative permittivity of the surface can be determined. The technique is validated using computer simulation, as well as field measurements of typical terrain surfaces, such as asphalt, grass, and bare soil. A major advantage of this method is that it obviates the need to use antennas with a narrow beam pattern.

Random noise polarimetry is a new radar technique for high-resolution probing of subsurface objects and interfaces. The University of Nebraska has developed a polarimetric random noise radar system based on the heterodyne correlation technique. Simulation studies and performance tests on the system confirm its ability to respond to phase differences in the received signals. In addition to polarimetric processing capability and the simplified system design, random noise radar also possesses other desirable features, such as immunity from radio frequency interference. The paper discusses the theoretical foundations of random noise polarimetry, and presents examples out of the entire data set collected that demonstrate the usefulness of the image processing and Stokes matrix presentation to enhance target detection using the coherent random noise radar.

Two-dimensional (2-D) inverse synthetic aperture radar (ISAR) imaging has been widely used in target scattering diagnosis, modeling and target identification. A major shortcoming is that a 2-D ISAR image cannot provide information on the relative altitude of each scattering center on the target. In this paper, we present an interferometric inverse synthetic aperture radar (IF-ISAR) image processing technique for three-dimensional (3-D) target altitude image formation. The 2-D ISAR images are obtained from the signature data acquired as a function of frequency and azimuthal angle. A 3-D IF-ISAR altitude image can then be derived from two 2-D images reconstructed from the measurements by antennas at different altitudes. 3-D altitude image formation examples from both indoor and outdoor test range data are demonstrated on complex radar targets.

The potential of high-resolution radar imagery to estimate various hydrological parameters, such as soil moisture, has long been recognized. Image simulation is one approach to study the interrelationships between the radar response and the underlying ground parameters. In order to perform realistic simulations, the authors incorporated the effects of naturally occurring spatial variability and spatial correlations of those ground parameters that affect the radar response, primarily surface roughness and soil moisture. Surface roughness and soil moisture images were generated for a hypothetical 100/spl times/100 m bare soil surface area at 1 m resolution using valid probability distributions and correlation lengths. These values were then used to obtain copolarized radar scattering coefficients at 2 GHz (L band) and 10 GHz (X band) frequencies using appropriate backscatter models, which were then converted to a digital number within 0-255 gray scale in order to generate radar images. The effect of surface roughness variability causes variability in the radar image, which is more apparent under smooth soil conditions. On the other hand, the inherent spatial pattern in soil moisture tends to cause similar patterns in the radar image under rougher soil conditions. The maximum difference between contrast-enhanced mean values of the radar image digital number due to moisture variations occurs at surface roughness values in the 1.5-2.0 cm range.>

Active mid-infrared laser reflectance characteristics of 18 different benchmark soil samples were measured at different angles of incidence at 9.283, 9.569, 10.247, and 10.633 mu m wavelengths under both copolarized and cross-copolarized conditions. Calibration was performed for each measurement using a Labsphere Infra-fold Diffuse Reflectance Standard of 94% reflectance. One hundred independent samples were averaged for each measurement to yield a mean reflectance value. The soil samples were characterized in terms of the soil taxonomy, mineralogy, geographic location, soil texture, and organic content and represented wide variability in soil properties. The data thus obtained are presented in graphical form, and the effect of soil mineralogy on the spectral features is investigated. A physically based empirical model is also presented to predict the angular dependence of copolarized backscatter.>