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In this paper, a technology of multi-radar imaging based on compressed sensing is proposed to improve image resolution. By constructing sample matrix, multi-radar super-resolution imaging is transformed to a compressed sensing problem. Utilizing the signal’s sparsity, super-resolution image can be obtained by solving an optimization problem. Simulation shows effectiveness of this technology.

Random Pulse Repetition Interval Compressed Sensing Radar (RPRICSR) has superiorities on target detection, unambiguous velocity measurement as well as anti-velocity jamming for its signal randomness. However, RPRICSR cannot detect the micro motion targets effectively under the presence of micro motion false targets within present echo processing methods. This paper firstly combined Short Sparse Recovery (SSR) method with RPRI signal under the frame of Compressed Sensing. Then the fact that echo signals of true and false micro motion targets are sparse in different dictionaries are utilized to separate the true and false micro motion targets based on a union dictionary. The proposed method is proved to be effective compared to the traditional signal processing methods of RPRICSR according to the simulation results.

Radar correlated imaging (RCI) introduces the optical correlated imaging technology to traditional microwave imaging, which has raised widespread concern recently. Conventional RCI methods neglect the structural information of complex extended target, which makes the quality of recovery result not really perfect, thus a novel combination of negative exponential restraint and total variation (NER-TV) algorithm for extended target imaging is proposed in this paper. The sparsity is measured by a sequential order one negative exponential function, then the 2D total variation technique is introduced to design a novel optimization problem for extended target imaging. And the proven alternating direction method of multipliers is applied to solve the new problem. Experimental results show that the proposed algorithm could realize high resolution imaging efficiently for extended target.

The Bridges had collapsed accidents in recent years due to bridges quality problems. Therefore, concretes nondestructive testing are particularly important. At present, most applications are Ground Penetrating Radar (GPR) technology in the detection of reinforced concretes structure. GPR are used the pulse method which alongside with definitive advantages, but the testing of the internal structure of the small thickness concretes has very low resolution by this method. In this paper, it’s the first time to use the ultra-wideband (UWB) stepped frequency conversion radar above problems. We use vector network analyzer and double ridged horn antenna microwave imaging system to test the reinforced concretes block. The internal structure of the concretes is reconstructed with a method of synthetic aperture of ω-k algorithm. By this method, the depth of the steel bar with the diameter of 1cm is shown exactly in the depth of 450mm×400mm×500mm and the depth error do not exceed 1cm.

Within the framework of the project, substances are indicators. It is these substances that are the main constituents of a watery suspension found on the surface of Mars. According to the conducted researches, the spectral region for the study of indicator substances was chosen. The method of remote sensing of the surface and the lidar construction scheme are chosen. The results of the preliminary calculation of the system are presented.

Due to its capability of capturing the kinematic properties of a target object, radar micro-Doppler signatures (m-DS) play an important role in radar target classification. This is particularly evident from the remarkable number of research papers published every year on m-DS for various applications. However, most of these works rely on the support vector machine (SVM) for target classification. It is well known that training an SVM is computationally expensive due to its nature of search to locate the supporting vectors. In this paper, the classifier learning problem is addressed by a total error rate (TER) minimization where an analytic solution is available. This largely reduces the search time in the learning phase. The analytically obtained TER solution is globally optimal with respect to the classification total error count rate. Moreover, our empirical results show that TER outperforms SVM in terms of classification accuracy and computational efficiency on a five-category radar classification problem.

A novel machine learning method named multiclass multiple kernel learning based on support vector data description with negative (MMKL-NSVDD) is developed to classify the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The proposed method not only inherits the close nonlinear boundary advantage of SVDD-neg model, which is applied with no assumptions regarding to the distribution of data and prior information, but also incorporates multiple kernel into the mode, avoiding fussy choice of kernel parameters and fusing multiple kernel information. Hence, it leads to a remarkable improvement of recognition rate, demonstrated by experimental results based on HRRPs of four aircrafts. The MMKL-NSVDD is ideal for HRRPBased radar target recognition.

In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly been used in many civil applications. However, they also pose a significant threat in restricted zones. Radar can be used to detect and discriminate UAVs. Due to the low flying altitude of the UAVs, it is found that the radar signals also include some unwanted echoes, reflected by building, ground, trees and grasses etc. Consequently, it has not been possible to get the clean UAVs characteristics for further classification. In this paper, the MTI filter is applied to cancel the ground clutter and based this, an improved MTI filter is further proposed. Compared with the traditional MTI filter, the improved one significantly enhances ground clutter rejection capability while maintaining most of the target power. As the result, the cleaner UAVs classification characteristics can be obtained. The effectiveness of the proposed method has been verified by an experimental CW radar dataset, collected from a helicopter UAV. Accepted version

In this work, we present the framework surrounding the development of a mmW radar image-based algorithm for wire recognition and classification for rotorcraft operation in degraded visual environments. While a mmW sensor image lacks the optical resolution and perspective of an IR or LIDAR sensor, it currently presents the only true see-through mitigation under the heaviest of degraded vision conditions. Additionally, the mmW sensor produces a high-resolution, radar map that has proven to be exceedingly interpretable, especially to a familiar operator. Seizing on these clear advantages, the mmW radar image-based algorithm is trained and evaluated against independent mmW imagery data collected from a live flight test in a relevant environment. The foundation of our approach is based on image processing and machine learning techniques utilizing radar-based signal properties and sensor and platform information for added robustness. We discuss some of the requirements and practical challenges of a standalone algorithm development, and lastly, present some preliminary examples using existing development tools and discuss the path for continued advancement and evaluation.

Nonlinear radar has proven to be a viable means of detecting devices that contain electrical nonlinearities. Electrical nonlinearities are present in dissimilar metals, metal to oxide junctions, semiconductors and more. This paper presents a linear and nonlinear synthetic aperture radar (SAR) system capable of imaging linear and nonlinear targets. The system creates images using data collected from a fixed 16 channel receiver with a single transmitter. A custom 16:1 switching network was developed to collect the SAR data from a 16 antenna receive array. SAR images presented show a nonlinear target placed directly on the ground and imaged in multiple range and cross-range locations. Data is also presented showing the clutter rejection properties of nonlinear radar. Images show that the harmonic radar is able to ignore the strong linear response from a corner reflector, while retaining the nonlinear response from a target.

This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface; filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

Adaptive filtering and channel estimation techniques are applied to laser based ranging systems that utilize wide-band intensity modulation to measure the range and reflectivity of underwater objects. The proposed method aims to iteratively learn the frequency dependent characteristics of the underwater environment using a frequency domain adaptive filter, which results in an estimate for the channels optical impulse response. This work presents the application of the frequency domain adaptive filter to simulated and experimental data, and shows it is possible to iteratively learn the underwater optical channel impulse response while using Hybrid Lidar/Radar techniques.

Blue-green laser systems are being developed for optical imaging and ranging in the underwater environment. The imaging application requires high range resolution to distinguish between multiple targets in the scene or between multiple target features, while the ranging application benefits from measurements with high range accuracy. The group at the Naval Air Warfare Center Aircraft Division (NAWCAD) in Patuxent River, MD has been investigating the merging of wideband radar modulation schemes with a pulsed laser system for underwater imaging and ranging applications. For the imaging application, the narrow peak produced by pulse compression at the receiver offers enhanced range resolution relative to traditional short pulse approaches. For ranging, the selection of modulation frequency bands approaching 1GHz provides backscatter and forward scatter suppression and enhanced range accuracy. Both passband and baseband digital processing have been applied to data collected in laboratory water tank experiments. The results have shown that the choice of processing scheme has a significant impact on optimizing the performance of modulated pulse laser systems for either imaging or ranging applications. These different processing schemes will be discussed, and results showing the effect of the processing schemes for imaging and ranging will be presented.

The U.S. Army Research Laboratory has developed the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar, which is capable of imaging concealed/buried targets using forward- and side-looking configurations. The SAFIRE radar is vehicle-mounted and operates from 300 MHz–2 GHz; the step size can be adjusted in multiples of 1 MHz. It is also spectrally agile and capable of excising frequency bands, which makes it ideal for operation in congested and/or contested radio frequency (RF) environments. Furthermore, the SAFIRE radar receiver has a super-heterodyne architecture, which was designed so that intermodulation products caused by interfering signals could be easily filtered from the desired received signal. The SAFIRE system also includes electro-optical (EO) and infrared (IR) cameras, which can be fused with radar data and displayed in a stereoscopic augmented reality user interface. In this paper, recent upgrades to the SAFIRE system are discussed and results from the SAFIRE’s initial field tests are presented.

The performance of electronic systems for radio-frequency (RF) spectrum analysis is critical for agile radar and communications systems, ISR (intelligence, surveillance, and reconnaissance) operations in challenging electromagnetic (EM) environments, and EM-environment situational awareness. While considerable progress has been made in size, weight, and power (SWaP) and performance metrics in conventional RF technology platforms, fundamental limits make continued improvements increasingly difficult. Alternatively, we propose employing cascaded transduction processes in a chip-scale nano-optomechanical system (NOMS) to achieve a spectral sensor with exceptional signal-linearity, high dynamic range, narrow spectral resolution and ultra-fast sweep times. By leveraging the optimal capabilities of photons and phonons, the system we pursue in this work has performance metrics scalable well beyond the fundamental limitations inherent to all electronic systems. In our device architecture, information processing is performed on wide-bandwidth RF-modulated optical signals by photon-mediated phononic transduction of the modulation to the acoustical-domain for narrow-band filtering, and then back to the optical-domain by phonon-mediated phase modulation (the reverse process). Here, we rely on photonics to efficiently distribute signals for parallel processing, and on phononics for effective and flexible RF-frequency manipulation. This technology is used to create RF-filters that are insensitive to the optical wavelength, with wide center frequency bandwidth selectivity (1-100GHz), ultra-narrow filter bandwidth (1-100MHz), and high dynamic range (70dB), which we will present. Additionally, using this filter as a building block, we will discuss current results and progress toward demonstrating a multichannel-filter with a bandwidth of < 10MHz per channel, while minimizing cumulative optical/acoustic/optical transduced insertion-loss to ideally < 10dB. These proposed metric represent significant improvements over RF-platforms.

Most existing radar algorithms are developed under the assumption that the environment, data clutter, is known and stationary. However, in practice, the characteristics of clutter can vary enormously in time depending on the operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. It is essential that the radar systems dynamically detect changes in the environment, and adapt to these changes by learning the new statistical characteristics of the environment. In this paper, we employ sparse recovery for clutter identification, specifically we identify the statistical profile the clutter follows. We use Monte Carlo simulations to simulate and test clutter data coming from various distributions.

The solution space to the DVE sensor problem can be considered to be a continuum where the goal is to minimize Size, Weight, Power, Cost (SWAP-C), and complexity while simultaneously maximizing performance. Performance is often achieved at the expense of SWAP-C and complexity. The core DVE sensor system technologies can be grouped into three broad areas: (1) sensors (e.g., LiDAR, radar, or imaging); (2) data processing such as fusion or sensor processing; and (3) synthetic vision, and symbology. Much of the body of DVE sensor research has focused on advancing the current state of the art in one or more of these particular areas, such as advanced sensing and/or data processing technologies. However, often the difficulties of integrating such a DVE technology into an aircraft and obtaining the proper hardware/software certification(s) for flight are not considered. Both of these adversely impact SWAP-C and complexity. In this paper we examine the solution space to the DVE sensor problem, identify the key drivers for SWAP-C and complexity, and present strategies for their mitigation.

Knowledge of the distance between a ship and other objects on the horizon has important implications for safety on the open ocean. Determining this distance can be challenging. RADAR sensors, which represent the standard solution to this problem, require a great deal of cost and power. A structure-from-motion computer vision technique is presented here to integrate known camera motion and a single pixel track on an imaged object to estimate range to that object. This approach leverages inherent wave motion of the camera platform along with a global exponentially stable observer to incrementally converge upon a range estimate. This method is detailed herein, and presented with both simulated and real-world results. This provides a passive and inexpensive solution to the open ocean ranging problem.

Ground penetrating radar (GPR) systems have emerged as a state-of-the-art remote sensing platform for the automatic detection of buried explosive threats. The GPR system that was used to collect the data considered in this work consists of an array of radar antennas mounted on the front of a vehicle. The GPR data is collected as the vehicle moves forward down a road, lane or path. The data is then processed by computerized algorithms that are designed to automatically detect the presence of buried threats. The amount of GPR data collected is typically prohibitive for real-time buried threat detection and therefore it is common practice to first apply a prescreening algorithm in order to identify a small subset of data that will then be processed by more computationally advanced algorithms. Historically, the F1V4 anomaly detector, which is energy-based, has been used as the prescreener for the GPR system considered in this work. Because F1V4 is energy-based, it largely discards shape information, however shape information has been established as an important cue for the presence of a buried threat. One recently developed prescreener, termed the HOG prescreener, employs a Histogram of Oriented Gradients (HOG) descriptor to leverage both energy and shape information for prescreening. To date, the HOG prescreener yielded inferior performance compared to F1V4, even though it leveraged the addition of shape information. In this work we propose several modifications to the original HOG prescreener and use a large collection of GPR data to demonstrate its superior detection performance compared to the original HOG prescreener, as well as to the F1V4 prescreener.

In the context of rising the frequency of natural disasters and catastrophes humanity has to develop methods and tools to ensure safe living conditions. Effectiveness of preventive measures greatly depends on quality and lead time of the forecast of disastrous natural phenomena, which is based on the amount of knowledge about natural hazards, their causes, manifestations, and impact. To prevent them it is necessary to get complete and comprehensive information about the extent of spread and severity of natural processes that can act within a defined territory. For these purposes the High Mountain Geophysical Institute developed the automated workplace for mining, analysis and archiving of radar, satellite, lightning sensors information and terrestrial (automatic weather station) weather data. The combination and aggregation of data from different sources of meteorological data provides a more informativity of the system. Satellite data shows the global cloud region in visible and infrared ranges, but have an uncertainty in terms of weather events and large time interval between the two periods of measurements, which complicates the use of this information for very short range forecasts of weather phenomena. Radar and lightning sensors data provide the detection of weather phenomena and their localization on the background of the global pattern of cloudiness in the region and have a low period measurement of atmospheric phenomena (hail, thunderstorms, showers, squalls, tornadoes). The authors have developed the improved algorithms for recognition of dangerous weather phenomena, based on the complex analysis of incoming information using the mathematical apparatus of pattern recognition.

Recently, the Stalker system has been developed as a high-resolution three-dimensional radar imaging system for the detection of concealed roadside explosive hazards. This system has shown considerable capability in distinguishing between true targets and false alarms using conventional processing techniques such as RX filtering on 2D projections of the data. In this paper, we develop an extension of these methods for use with 3D radar imagery. We show several different prescreening approaches for automatically marking potential target locations and describe an evaluation program called the Tiger scorer. We tested our approach on data collected at an arid U.S. Army test site.

A fully integrated Mission-Level Radar model is in development as part of a multi-year effort under the Northrop Grumman Mission Systems (NGMS) sector’s Model Based Engineering (MBE) initiative to digitally interconnect and unify previously separate performance and cost models. In 2016, an NGMS internal research and development (IR and D) funded multidisciplinary team integrated radio frequency (RF), power, control, size, weight, thermal, and cost models together using a commercial-off-the-shelf software, ModelCenter, for an Active Electronically Scanned Array (AESA) radar system. Each represented model was digitally connected with standard interfaces and unified to allow end-to-end mission system optimization and trade studies. The radar model was then linked to the Air Force’s own mission modeling framework (AFSIM). The team first had to identify the necessary models, and with the aid of subject matter experts (SMEs) understand and document the inputs, outputs, and behaviors of the component models. This agile development process and collaboration enabled rapid integration of disparate models and the validation of their combined system performance. This MBE framework will allow NGMS to design systems more efficiently and affordably, optimize architectures, and provide increased value to the customer. The model integrates detailed component models that validate cost and performance at the physics level with high-level models that provide visualization of a platform mission. This connectivity of component to mission models allows hardware and software design solutions to be better optimized to meet mission needs, creating cost-optimal solutions for the customer, while reducing design cycle time through risk mitigation and early validation of design decisions.

In this paper, improving data association process by increasing the probability of detecting valid data points (measurements obtained from ESM/RADAR system) in the presence of noise for location and target tracking are discussed. This develop a multisensor data association algorithm that fuses information from the multiple ESM receiver and surveillance RADAR. The develop a novel algorithm by self-organizing fuzzy neural network (SO-FNN) for multiple ESM-to-ESM (measurement-to-measurement data association) and ESMs-to-RADAR (track-to-track data association) problem in dense clutter environment. An adaptive search based on SO-FNN of the distance threshold measure is then used to detect valid filtered data point for data association. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm. The paper is organized as follows. Section 1 is the problem formulation. Section 2 design the new data association algorithm based on SO-FNN data association system design. Section 3 describes ESM-to-ESM and ESM-to-RADAR (measurement-to-measurement data association and track-to-track data association) scenario and the simulation results are presented and discussed. The summary are drawn in section 4, respectively.

The paper focuses on extracting scattering centers of radar targets using compressive sensing and using them as features in a target recognition system. It has been shown that a target’s high resolution range profile (HRRP) is sparse in time corresponding to few scatterers that can be associated with target geometry. The recognition system is tested using real radar data of commercial aircraft models. Classification is carried out using distance based and correlation based techniques. Scenarios where the target aspect angle is unknown or known to be within a certain range are also examined.

We desire a metric with which to evaluate the “goodness” of various image compression schemes in recreating an original Synthetic Aperture Radar image. Herein we propose a “coherence” measure that results in a single quality number for such an evaluation.

With the rapid proliferation of small unmanned aerial systems (UAS) in the national airspace, small operational drones are being sometimes considered as a security threat for critical infrastructures, such as sports stadiums, military facilities, and airports. There have been many civilian counter-drone solutions and products reported, including radar and electromagnetic counter measures. For the current electromagnetic solutions, they are usually limited to particular type of detection and counter-measure scheme, which is usually effective for the specific type of drones. Also, control and communication link technologies used in even RC drones nowadays are more sophisticated, making them more difficult to detect, decode and counter. Facing these challenges, our team proposes a “software-defined” solution based on noise and LPI radar. For the detection, wideband-noise radar has the resolution performance to discriminate possible micro-Doppler features of the drone versus biological scatterers. It also has the benefit of more adaptive to different types of drones, and covertly detecting for security application. For counter-measures, random noise can be combined with “random sweeping” jamming scheme, to achieve the optimal balance between peak power allowed and the effective jamming probabilities. Some theoretical analysis of the proposed solution is provided in this study, a design case study is developed, and initial laboratory experiments, as well as outdoor tests are conducted to validate the basic concepts and theories. The study demonstrates the basic feasibilities of the Drone Detection and Mitigation Radar (DDMR) concept, while there are still much work needs to be done for a complete and field-worthy technology development.

Current airborne radar systems segment the available time-on-target during each beam dwell into multiple Coherent Processing Intervals (CPIs) in order to eliminate range eclipsing, solve for unambiguous range, and increase the detection performance against larger Radar Cross Section (RCS) targets. As a consequence, these radars do not realize the full Signal-to-Noise Ratio (SNR) increase and detection performance improvement that is possible. Continuous High Pulse Repetition Frequency (HPRF) waveforms and processing enables the coherent integration of all available radar data over the full time-on-target. This can greatly increase the SNR for air targets at long range and/or with weak radar returns and significantly improve the detection performance against such targets. TSC worked with its partner KeyW to implement a Continuous HPRF waveform in their Sahara radar testbed and obtained measured radar data on both a ground vehicle target and an airborne target of opportunity. This experimental data was processed by TSC to validate the expected benefits of Continuous HPRF waveforms.

Through-the-Wall (TW) radar sensors are gaining increasing interest for security, surveillance and search and rescue applications. Additionally, the integration of Multiple-Input, Multiple-Output (MIMO) techniques with phased array radar is allowing higher performance at lower cost. In this paper we present a 4-by-4 TW MIMO phased array imaging radar operating at 2.4 GHz with 200 MHz bandwidth. To achieve high imaging resolution in a cost-effective manner, the 4 Tx and 4 Rx elements are used to synthesize a uniform linear array (ULA) of 16 virtual elements. Furthermore, the transmitter is based on a single-channel 4-element time-multiplexed switched array. In transmission, the radar utilizes frequency modulated continuous wave (FMCW) waveforms that undergo de-ramping on receive to allow digitization at relatively low sampling rates, which then simplifies the imaging process. This architecture has been designed for the short-range TW scenarios envisaged, and permits sufficient time to switch between antenna elements. The paper first outlines the system characteristics before describing the key signal processing and imaging algorithms which are based on traditional Fast Fourier Transform (FFT) processing. These techniques are implemented in LabVIEW software. Finally, we report results from an experimental campaign that investigated the imaging capabilities of the system and demonstrated the detection of personnel targets. Moreover, we show that multiple targets within a room with greater than approximately 1 meter separation can be distinguished from one another.

Passive coherent location (PCL) is a radar technique, in which the system uses reflections from opportunistic illumination sources in the environment for detection and tracking. Typically, PCL uses civilian communication transmitters not ideally suited for radar. The physical geometry of PCL is developed on the basis of bistatic radar without control of the transmitter antenna or waveform design. This poses the problem that often the receiver is designed with two antennas and channels, one for reference and one for surveillance. The surveillance channel is also contaminated with the direct signal and thus direct signal suppression (DSS) techniques must be used. This paper proposes an analytical solution based around hardware for DSS which is compared to other methods available in the literature. The methods are tested in varying bistatic geometries and with varying target radar cross section (RCS) and signal-to-noise ratio (SNR).

Due to challenges in the visual interpretation of radar signatures or in the subsequent information extraction, a fusion with other data sources can be beneficial. The most accurate basis for a fusion of any kind of remote sensing data is the mapping of the acquired 2D image space onto the true 3D geometry of the scenery. In the case of radar images this is a challenging task because the coordinate system is based on the measured range which causes ambiguous regions due to layover effects. This paper describes a method that accurately maps the detailed 3D information of a scene to the slant-range-based coordinate system of an imaging radar. Due to this mapping all the contributing geometrical parts of one resolution cell can be determined in 3D space. The proposed method is highly efficient, because computationally expensive operations can be directly performed on graphics card hardware. The described approach builds a perfect basis for sophisticated methods to extract data from multiple complimentary sensors like from radar and optical images, especially because true 3D information from whole cities will be available in the near future. The performance of the developed methods will be demonstrated with high resolution radar data acquired by the space borne SAR-sensor TerraSAR-X. The most accurate basis for a fusion of any kind of remote sensing data is the mapping of the acquired 2D image space onto the true 3D geometry of the scenery. In the case of radar images this is a challenging task because the coordinate system is based on the measured range which causes ambiguous regions due to layover effects. This paper describes a method that accurately maps the detailed 3D information of a scene to the slant-range-based coordinate system of an imaging radar. The performance of the developed methods will be demonstrated with high resolution radar data acquired by the space borne SAR-sensor TerraSAR-X.

Since the inception of coherent waveforms, it has been realized that the effect of the motion of a non-point like object can induce structure in the return spectrum of the waveform (Gray and Addison, Gray et-al). There are many ways to think about this, but a newer one is to think of the law of motion induced onto the scattered waveform as being "generated" by an operator to produce the law of motion (Gray and Parks). One can use this observation to design a receiver's matched filter response specific to these type of operators that generate a law of motion. We use Poisson bracket method for generating a Taylor series solution to a functional equation to illustrate how to do this. In addition, one can also do the same for three dimensional periodic motion using the rotation matrices. We illustrate this by developing the matched filter response for a rotating object in terms of the rotation matrix.

A 24 GHz medium-range human detecting sensor, using the Doppler Radar Physiological Sensing (DRPS) technique, which can also detect unmanned aerial vehicles (UAVs or drones), is currently under development for potential rescue and anti-drone applications. DRPS systems are specifically designed to remotely monitor small movements of non-metallic human tissues such as cardiopulmonary activity and respiration. Once optimized, the unique capabilities of DRPS could be used to detect UAVs. Initial measurements have shown that DRPS technology is able to detect moving and stationary humans, as well as largely non-metallic multi-rotor drone helicopters. Further data processing will incorporate pattern recognition to detect multiple signatures (motor vibration and hovering patterns) of UAVs.

It is often advantageous to modify, or warp, radar waveforms, particularly with respect to group-delay and spectral dilation. These warping adjustments may facilitate real-time motion compensation of waveforms in radar systems, especially when those waveforms are generated by a digital parametric waveform generator. Relevant waveforms to this paper include Frequency Modulated (FM) waveforms, such as the Linear-FM (LFM) chirp, Non-Linear FM (NLFM) chirp, and other general FM waveforms. We present techniques for making fine adjustments to dynamically warp general FM waveforms.

A missile warning system can detect the incoming missile threat(s) and automatically cue the other Electronic Attack (EA) systems in the suit, such as Directed Infrared Counter Measure (DIRCM) system and/or Counter Measure Dispensing System (CMDS). Most missile warning systems are currently based on passive sensor technology operating in either Solar Blind Ultraviolet (SBUV) or Midwave Infrared (MWIR) bands on which there is an intensive emission from the exhaust plume of the threatening missile. Although passive missile warning systems have some clear advantages over pulse-Doppler radar (PDR) based active missile warning systems, they show poorer performance in terms of time-to-impact (TTI) estimation which is critical for optimizing the countermeasures and also "passive kill assessment". In this paper, we consider this problem, namely, TTI estimation from passive measurements and present a TTI estimation scheme which can be used in passive missile warning systems. Our problem formulation is based on Extended Kalman Filter (EKF). The algorithm uses the area parameter of the threat plume which is derived from the used image frame.

THz Identification is a developing technology. Sensing in the THz range potentially gives opportunity for short range radar sensing because THz waves can better penetrate through obscured atmosphere, such as fog, than visible light. The lower scattering of THz as opposed to the visible light results also in significantly better imaging than in IR spectrum. A much higher contrast can be achieved in medical trans-illumination applications than with X-rays or visible light. The same THz radiation qualities produce better tomographical images from hard surfaces, e.g. ceramics. This effect comes from the delay in time of reflected THz pulses detection. For special or commercial applications alike, the industrial quality control of defects is facilitated with a lower cost. The effectiveness of THz wave measurements is increased with computational methods. One of them is Bayes modeling. Examples of this kind of mathematical modeling are considered.

Multiband cognitive radar systems, operating in a variety of frequency bands and combining the different channels into a joint system, can provide significant flexibility and capability to detect and track hostile targets. This paper proposes a multi-passband complex filter (MPCF) architecture and the related circuit design for a multiband cognitive radar system. By operating under the 5.8GHz UNII band, the sensing part detects the current usage of frequency bands from 5.15GHz to 5.825GHz and provides the information of unused channels. The multiband cognitive radar system uses the whole unused channels and eliminates the used channels by using an on-chip MPCF in order to be coexistent with the Wi-Fi standard. The MPCF filters out the unwanted channels and leave the wanted channels. It dynamically changes the bandwidth of frequency from 20MHz to 80MHz using the 0.18μm CMOS technology. The MPCF is composed of the combination of 5th-order Chebyshev low-pass filters and high-pass filters, and the overall inband ripple of the MPCF is 1.2dB. The consuming current is 21.7mA at 1.8V power supply and the 20MHz bandwidth noise is 55.5nV. The total harmonic distortion (THD) is 45dB at 25MHz and the adjacent channel rejection is 24dB. The result of the MPCF guarantees the performance requirements of the multiband cognitive radar system.

Weak Value Measurements (WVMs) with pre- and post-selected quantum mechanical ensembles were proposed by Aharonov, Albert, and Vaidman in 1988 and have found numerous applications in both theoretical and applied physics. In the field of precision metrology, WVM techniques have been demonstrated and proven valuable as a means to shift, amplify, and detect signals and to make precise measurements of small effects in both quantum and classical systems, including: particle spin, the Spin-Hall effect of light, optical beam deflections, frequency shifts, field gradients, and many others. In principal, WVM amplification techniques are also possible in radar and could be a valuable tool for precision measurements. However, relatively limited research has been done in this area. This article presents a quantum-inspired model of radar range and range-rate measurements of arbitrary strength, including standard and pre- and post-selected measurements. The model is used to extend WVM amplification theory to radar, with the receive filter performing the post-selection role. It is shown that the description of range and range-rate measurements based on the quantum-mechanical measurement model and formalism produces the same results as the conventional approach used in radar based on signal processing and filtering of the reflected signal at the radar receiver. Numerical simulation results using simple point scatterrer configurations are presented, applying the quantum-inspired model of radar range and range-rate measurements that occur in the weak measurement regime. Potential applications and benefits of the quantum inspired approach to radar measurements are presented, including improved range and Doppler measurement resolution.

We propose an approach based on artificial Neural Networks (NN) to classify wideband radar jammer signals for more efficient use of countermeasures. A robust NN is be used to correctly differentiate Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). We compare the performance of the NN using the samples of the power spectrum versus the autocorrelation. Prior experiments showed that frequency-domain moments of the FM signal itself are better descriptors than time-domain moments. Using simulated wideband FM radar signals, we compute a set of N autocorrelation and spectra and feed them to the NN which has ten hidden layers. For training purposes, the autocorrelations or spectra sets are divided into three groups, 75% for training, 15% for validating and 15% for testing. For the power spectra set, we observe that a Signal to Noise Ratio (SNR) of 5dB allows the network to approach an average of 5% percent Probability of Error (PE). Training with the autocorrelation set yields comparable results. For an SNR of 5dB, the average PE reached an average of 0.3%. In both instances, the NN reaches zero percent PE at an SNR of 10dB.

A new, versatile, UHF/L band, ultrawideband (UWB), vehicle-mounted radar system developed at the U.S. Army Research Laboratory (ARL) has recently been exercised at an arid U.S. test site. The unique switching scheme implemented to record data from all receive channels is described, along with the current calibration procedure. Radar and global positioning system (GPS) data collected in both forwardand side-looking configurations are processed, and synthetic aperture radar (SAR) images are formed. Results are presented for various target emplacement scenarios.

Radar based detection of human targets behind walls or in dense urban environments is an important technical challenge with many practical applications in security, defense, and disaster recovery. Radar reflections from a human can be orders of magnitude weaker than those from objects encountered in urban settings such as walls, cars, or possibly rubble after a disaster. Furthermore, these objects can act as secondary reflectors and produce multipath returns from a person. To mitigate these issues, processing of radar return data needs to be optimized for recognizing human motion features such as walking, running, or breathing. This paper presents a theoretical analysis on the modulation effects human motion has on the radar waveform and how high levels of multipath can distort these motion effects. From this analysis, an algorithm is designed and optimized for tracking human motion in heavily clutter environments. The tracking results will be used as the fundamental detection/classification tool to discriminate human targets from others by identifying human motion traits such as predictable walking patterns and periodicity in breathing rates. The theoretical formulations will be tested against simulation and measured data collected using a low power, portable see-through-the-wall radar system that could be practically deployed in real-world scenarios. Lastly, the performance of the algorithm is evaluated in a series of experiments where both a single person and multiple people are moving in an indoor, cluttered environment.

The new challenges originated from Digital Array Radar (DAR) demands a new generation of reconfigurable backend processor in the system. The new FPGA devices can support much higher speed, more bandwidth and processing capabilities for the need of digital Line Replaceable Unit (LRU). This study focuses on using the latest Altera and Xilinx devices in an adaptive beamforming processor. The field reprogrammable RF devices from Analog Devices are used as analog front end transceivers. Different from other existing Software-Defined Radio transceivers on the market, this processor is designed for distributed adaptive beamforming in a networked environment. The following aspects of the novel radar processor will be presented: (1) A new system-on-chip architecture based on Altera’s devices and adaptive processing module, especially for the adaptive beamforming and pulse compression, will be introduced, (2) Successful implementation of generation 2 serial RapidIO data links on FPGA, which supports VITA-49 radio packet format for large distributed DAR processing. (3) Demonstration of the feasibility and capabilities of the processor in a Micro-TCA based, SRIO switching backplane to support multichannel beamforming in real-time. (4) Application of this processor in ongoing radar system development projects, including OU’s dual-polarized digital array radar, the planned new cylindrical array radars, and future airborne radars.

Longer radio frequency waves better penetrating through high scattered media than millimeter waves, but imaging resolution limited by diffraction at longer wavelength. Same time frequency and amplitudes of diffracted waves (frequency domain measurement) provides information of object. Phase shift of diffracted waves (phase front in time domain) consists information about shape of object and can be applied for reconstruction of object shape or even image by recording of multi-frequency digital hologram. Spectrum signature or refracted waves allows identify the object content. Application of monopulse method with overlap closely spaced antenna patterns provides high accuracy measurement of amplitude, phase, and direction to signal source. Digitizing of received signals separately in each antenna relative to processor time provides phase/frequency independence. Fly eye non-scanning multi-frequency radar system provides simultaneous continuous observation of multiple targets and wide possibilities for stepped frequency, simultaneous frequency, chaotic frequency sweeping waveform (CFS), polarization modulation for reliable object detection. Proposed c-band fly eye radar demonstrated human detection through 40 cm concrete brick wall with human and wall material spectrum signatures and can be applied for through wall human detection, landmines, improvised explosive devices detection, underground or camouflaged object imaging.

In this study, an automatic take-off and landing system (ATOLS) based on radar guidance was developed to provide day/night, all weather, automatic takeoff and landing for unmanned aerial vehicles (UAVs). The ATOLS contains a ground-based tracking radar subsystem and an airborne transponder subsystem. This X-band tracking radar can provide precise position information for UAV-control operations (transponder mode) and fire-control systems (skin mode). It provides 360 degrees of azimuth coverage and therefore can be employed for navigation applications. Its maximum tracking range is about 17 km and accuracy of altitude measurement is about 1 ft with a 50-ft decision height above ground level. To substantiate the proposed ATOLS system, a differential global positioning system (DGPS) was also developed. When a UAV at a low-elevation angle is detected and tracked by a tracking radar, multipath propagation often leads to the degradation of tracking accuracy or even cause the radar to break track. As a result, it becomes a potential risk to flight safety of the ATOLS guidance and control of UAVs. To overcome this technical difficulty, this paper proposes a solution based on optimization of radar parameters to mitigate the interference from multipath signals. The feasibility of proposed method has been experimentally proven through the flight trials of UAVs. Compared to the conventional low-elevation tracking techniques, the proposed one employs the radar signal processing, and does not consume additional hardware and resources.

For nonlinear radar, the transmit power required to measure a detectable response from a target is relatively high, and generating that high power is achieved at the cost of linearity. This paper applies the distortion mitigation technique Linearization by Time-Multiplexed Spectrum (LITMUS) to intermodulation radar, a type of nonlinear radar which receives spectral content produced by the mixing of multiple frequencies at a nonlinear target. By implementing LITMUS, an experimental detection system for an intermodulation radar achieves a signal-to-noise ratio up to 20 dB for a total transmit power of approximately 80 mW and nonlinear targets placed at a standoff distance of 2 meters.

The authors acknowledge the funding received from the Science and Technology Facilities Council which has supported this work under grant ST/N006569/1. This paper discusses the micro-Doppler signatures of small UAVs obtained from a millimeter-wave radar system. At first, simulation results are shown to demonstrate the theoretical concept. It is illustrated that whilst the propeller rotation rate of the small UAVs is quite high, millimeter-wave radar systems are capable of capturing the full micro-Doppler spread. Measurements of small UAVs have been performed with both CW and FMCW radars operating at 94 GHz. The CW radar was used for obtaining micro-Doppler signatures of individual propellers. The field test data of a flying small UAV was collected with the FMCW radar and was processed to extract micro-Doppler signatures. The high fidelity results clearly reveal features such as blade flashes and propeller rotation modulation lines which can be used to classify targets. This work confirms that millimeter-wave radar is suitable for the detection and classification of small UAVs at usefully long ranges. Publisher PDF

Clutter and radio frequency interference (RFI) are prevalent issues in the field of radar and are specifically of interest to of cognitive radar. Here, methods for applying and testing the utility of cognitive radar for clutter and RFI mitigation are explored. Using the adaptable transmit capability, environmental database, and general “awareness” of a cognitive radar system (i.e. spectrum sensing, geographical location, etc.), a matched waveform is synthesized that improves the signal-to-clutter ratio (SCR), assuming at least an estimate of the target response and the environmental clutter response are known a prior i. RFI may also be mitigated by sensing the RF spectrum and adapting the transmit center frequency and bandwidth using methods that optimize bandwidth and signal-to-interference plus noise ratio (SINR) (i.e. the spectrum sensing, multi-objective (SS-MO) algorithm). The improvement is shown by a decrease in the noise floor. The above methods’ effectiveness are examined via a test-bed developed around a software defined radio (SDR). Testing and the general use of commercial off the shelf (COTS) devices are desirable for their cost effectiveness, general ease of use, as well as technical and community support, but these devices provide design challenges in order to be effective. The universal software radio peripheral (USRP) X310 SDR is a relatively cheap and portable device that has all the system components of a basic cognitive radar. Design challenges of the SDR include phase coherency between channels, bandwidth limitations, dynamic range, and speed of computation and data communication / recording.

The effectiveness of various dynamic calibration targets emulating human respiration are analyzed. Potential advantages of these devices relate to easier calibration methods for human detection testing in through-wall and through-rubbles situations. The three devices examined possess spherical polyhedral geometries. Spherical characteristics were implemented due to the unique qualities spheres possess in regards to calibration purposes. The ability to use a device that is aspect independent is favorable during the calibration process. Rather than using a traditional, static calibration sphere, a dynamic, sphere-like device offers the ability to resemble breathing movements of the human body. This motion opens the door for numerous types of Doppler testing that is impossible in a static calibration device. Monostatic RCS simulations at 3 GHz are documented for each geometry. The results provide a visual way of representing the effectiveness of each design as a dynamic calibration target for human detection purposes.

The U.S. Army Research Laboratory (ARL) recently designed and tested a new prototype radar, the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar system, based on a stepped-frequency architecture to address issues associated with our previous impulse-based radars. This is a low-frequency ultra-wideband (UWB) radar with frequencies spanning from 300 to 2000 MHz. Mounted on a vehicle, the radar can be configured in either sidelooking or forward-looking synthetic aperture radar (SAR) mode. We recently conducted our first experiment at Yuma Proving Grounds (YPG). This paper summarizes the radar configurations, parameters, and SAR geometry. The radar data and other noise sources, to include the self-interference signals and radio-frequency interference (RFI) noise sources, are presented and characterized in both the raw (pre-focus) and SAR imagery domains. This paper also describes our signal processing techniques for extracting noise from radar data, as well as the SAR imaging algorithms for forming SAR imagery in both forward- and side-looking modes. Finally, this paper demonstrates our spectral recovery technique and results for a radar operating in a spectrally restricted environment.

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiere Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Proposed novel imaging technique will provide all weather high-resolution imaging and recognition capability for RF/Microwave signals with good penetration through highly scattered media: fog, snow, dust, smoke, even foliage, camouflage, walls and ground. Image resolution in proposed imaging system is not limited by diffraction and will be determined by processor and sampling frequency. Proposed imaging system can simultaneously cover wide field of view, detect multiple targets and can be multi-frequency, multi-function. Directional antennas in imaging system can be close positioned and installed in cell phone size handheld device, on small aircraft or distributed around protected border or object. Non-scanning monopulse system allows dramatically decrease in transmitting power and at the same time provides increased imaging range by integrating 2-3 orders more signals than regular scanning imaging systems.