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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 Radar and Communications Laboratory (RCL) at The Pennsylvania State University is at the forefront of radar technology and is engaged in cutting edge research in all aspects of radar, including modeling and simulation studies of novel radar paradigms, design and development of new types of radar architectures, and extensive field measurements in realistic scenarios. This paper summarizes the research at The Pennsylvania State University’s Radar and Communications Laboratory and relevant collaborative research with several groups over the past 15 years in the field of radar and related technologies, including communications, radio frequency identification (RFID), and spectrum sensing.

The Eigen-Template (ET) based closed-set feature extraction approach is extended to an open-set HRR-ATR framework to develop an Open Set Probabilistic Support Vector Machine (OSP-SVM) classifier. The proposed ET-OSP-SVM is shown to perform open set ATR on HRR data with 80% PCC for a 4-class MSTAR dataset.

Passive Coherent Location (PCL) is a developing radar technique, in which the system processes reflections from opportunistic illumination sources in the environment for detection and tracking. Many developments and improvements of PCL implement pseudo-monostatic and bistatic radar configurations; however, with the proliferation of commercial communication systems, the spectrally dense environment suggests the use of a heterogenous multistatic PCL system. This paper develops error minimization criteria to adjust and optimize available resources to a wideband PCL receiver. The method introduces the concept of self ambiguity as an error metric and implements this as a criterion to test varying PCL scenarios with differing transmitter modulation waveforms. The paper compares this to available techniques and the global minimum error available.

We present a prototype bistatic compact radar range operating at 160 GHz and capable of collecting fullypolarimetric radar cross-section and electromagnetic scattering measurements in a true far-field facility. The bistatic ISAR system incorporates two 90-inch focal length, 27-inch-diameter diamond-turned mirrors fed by 160 GHz transmit and receive horns to establish the compact range. The prototype radar range with its modest sized quiet zone serves as a precursor to a fully developed compact radar range incorporating a larger quiet zone capable of collecting X-band bistatic RCS data and 3D imagery using 1/16th scale objects. The millimeter-wave transmitter provides 20 GHz of swept bandwidth in the single linear (Horizontal/Vertical) polarization while the millimeter-wave receiver, that is sensitive to linear Horizontal and Vertical polarization, possesses a 7 dB noise figure. We present the design of the compact radar range and report on test results collected to validate the system’s performance.

The article of record as published may be found at http://dx.doi.org/10.1117/12.2263096 This paper examines the implications pertaining to the problem of attempting to invert synthetic aperture radar (SAR) measurement data to yield unique estimates of the underlying motion of slow targets in the imaged scene. A recent analysis has demonstrated that ambiguities exist in estimating the kinematics parameters of surface targets for general bistatic SAR collection data. In particular, a procedure has been developed which generates alternate target trajectories which give the same SAR measurements as that of the true target motion. The current paper extends the earlier analysis by generating specific numeric examples of alternate target trajectories corresponding to the motion of a given slowly moving target. This slow-target case reveals the counter-intuitive result that a single SAR collection data set can be generated by target trajectories with significantly different, and possibly opposing, heading directions. For example, the true motion of a given target can be moving towards the mean radar position during the SAR collection interval, whereas a valid alternate trajectory can correspond to a target that is moving away from the radar. The present analysis demonstrates the extent of the challenges associated with attempting to estimate of the underlying motion of targets using SAR measurement data. AFRL for partial sponsorship

Accurate classification of phase modulated radar waveforms is a well-known problem in spectrum sensing. Identification of such waveforms aids situational awareness enabling radar and communications spectrum sharing. While various feature extraction and engineering approaches have sought to address this problem, the use of a machine learning algorithm that best utilizes these features is becomes foremost. In this effort, a comparison of a standard shallow and a deep learning approach are explored. Experiments provide insights into classifier architecture, training procedure, and performance.

There are known only few ballistic object tracking algorithms. To develop such algorithms and to its further testing, it is necessary to implement possibly simple and reliable objects’ dynamics model. The article presents the dynamics’ model of a tactical ballistic missile (TBM) including the three stages of flight: the boost stage and two passive stages – the ascending one and the descending one. Additionally, the procedure of transformation from the local coordinate system to the polar-radar oriented and the global is presented. The prepared theoretical data may be used to determine the tracking algorithm parameters and to its further verification.

Synthetic aperture radar (SAR) imaging of construction materials offers civil engineers an opportunity to better assess the condition of aging civil infrastructures such as reinforced concrete (RC) structures. Corrosion of steel rebar in RC structures is a major problem responsible for their premature failure and unexpected collapse. In this paper, SAR imaging is applied to the quantitative assessment of corroded steel rebar in free space as the first step toward the use of SAR imaging for subsurface sensing of aging RC structures. A 10 GHz stripmap SAR system was used inside an anechoic chamber. The bandwidth of the radar system was 1.5 GHz. Steel rebar specimens were artificially corroded to different levels by regularly applying a mist of 5% NaCl solution for different durations of time in order to simulate the condition of natural corrosion. Two sizes (No. 3 and No. 4) of steel rebar were used in this research. Different orientations of steel rebar were considered. Corrosion level was determined by measuring the mass loss of corroded steel rebar specimens. From our results, feasibility of SAR images for the condition assessment of corroded steel rebar was experimentally demonstrated. It was found that the presence of surface rust on corroded steel rebar reduces the amplitude in SAR images. The SAR image of corroded steel rebar also exhibited a distribution of SAR amplitudes different from the one of intact steel rebar. In addition, it was also found that there is an optimal range for the condition assessment of corroded steel rebar in free space. In our experiment, the optimal range was determined to be 30.4 cm.

The backward scattering characteristic of the jet engine nozzle is one of the most challenging problem in applied electromagnetics. Radar cross section (RCS) data of a double S-shaped nozzle (DSN) with and without radar absorbing material (RAM) were measured in the microwave chamber using stepping test system. The experiment results show that S-shaped structure has a certain effect on the backwards scattering characteristics. Coating RAM can reduce the RCS of DSN effectively.

Aiming at the simultaneous measurement of the size, shape, fall velocity of precipitation particles, a ground-based optical instrument, the precipitation micro-physical characteristics sensor (PMCS) was developed, it can record the double-exposure image of each particle in a single frame, by which the size, equivalent diameter, axis ratio of raindrops can be calculated, its fall velocity can be calculated according to its displacement and time interval. The PMCS is calibrated by some glass balls with certain diameters in sunny, cloudy, and night conditions, based on which a fitted relationship between the threshold and image area is obtained and used to ensure the measurement accuracy of raindrop sizes. The field measurement of PMCS has obtained plenty of raindrops images, based on which the shape, axis ratio, and fall velocity of raindrops were calculated and discussed. The typical shape of large raindrop takes on an oblate ellipsoid with a flatter base and a more raised top, the axis ratio of raindrops decrease linearly with the increasing of diameter, the fall velocity of raindrops increases exponentially with the increasing of the diameter, and above observed data are in good agreement with the empirical model. These results validate the performance of PMCS, which can be widely applied for interpreting weather radar data, investigating regional precipitation features, and studying the physical process of precipitation.

In the single frequency continuous wave radar life detection system, based on the Doppler effect, the theory model of radar life signal is expressed by the real function, and there is a phenomenon that can’t be confirmed by the experiment. When the phase generated by the distance between the measured object and the radar measuring head is л of integer times, the main frequency spectrum of life signal (respiration and heartbeat) is not existed in radar life signal. If this phase is л/2 of odd times, the main frequency spectrum of breath and heartbeat frequency is the strongest. In this paper, we use the Doppler effect as the basic theory, using three different mathematical expressions——real function, complex exponential function and Bessel's function expansion form. They are used to establish the theoretical model of radar life signal. Simulation analysis revealed that the Bessel expansion form theoretical model solve the problem of real function form. Compared with the theoretical model of the complex exponential function, the derived spectral line is greatly reduced in the theoretical model of Bessel expansion form, which is more consistent with the actual situation.

The distribution of analogue RF signals within a high performance radar system is challenging due to the limited space available and the high levels of performance required. This work investigates the gain, linearity and noise performance that can be achieved by an externally modulated direct detection link designed for operation up to 20 GHz using commercially available components. The aim was to assess the suitability of such links for use in future radar systems. Good correlation has been shown between modelled and measured results demonstrating that the performance should satisfy the linearity requirements for many radar applications.

The technology of Radio over Fiber (ROF) not only has broad prospects in the field of communications, but also has great potential in the field of radar. ROF technology will be able to change the traditional structure of radar and radar network, improve their performance. The radar can be reduced to a system that has only transmitter, receiver, transmission line and antenna. Other equipment can be concentrated to the command center. The command center will be not only a data processing center, but also a signal processing center. At first, this paper analyzed the factors that influence the phase stability of microwave signal in fiber. For a short fiber, the stress in the fiber direction is the major point that influence the phase stability, other factors can be neglected. For a long fiber, all factors should be considered. And then, this paper analyzed the technical requirements of radar signal transmission, concluded that the phase stability of ROF system is the most important factor for radar, and chosen the method of phase compensation to solve this problem. At last, this paper designed a ROF link for RF transmission of radar.

The hardware-in-the-loop simulation system, which provides a precise, controllable and repeatable test conditions, is an important part of the development of the semi-active laser (SAL) guided weapons. In this paper, laser energy chain characteristics were studied, which provides a theoretical foundation for the SAL guidance technology and the hardware-in-the-loop simulation system. Firstly, a simplified equation was proposed to adjust the radar equation according to the principles of the hardware-in-the-loop simulation system. Secondly, a theoretical model and calculation method were given about the energy chain characteristics based on the hardware-in-the-loop simulation system. We then studied the reflection characteristics of target and the distance between the missile and target with major factors such as the weather factors. Finally, the accuracy of modeling was verified by experiment as the values measured experimentally generally follow the theoretical results from the model. And experimental results revealed that ratio of attenuation of the laser energy exhibited a non-linear change vs. pulse number, which were in accord with the actual condition.

By using Intel Visual Fortran and Visual Studio 2013, a software for calculating radar cross section of the metal and the plasma three-dimensional composite target are given, based on the method of MOM and FDTD. This software can calculate radar cross section of the near space aircraft covered the plasma sheath, and this work has an important strategic significance in the future.

The space-based long-distance ranging of space debris will help to avoid collision. Compared with radar and telescope, the infrared binocular monitoring system can track and range space debris quickly. Because the measurement range is related to the baseline length, two cameras are placed on different satellites. Due to the lack of rigid connection between satellites, femtosecond laser ranging is used to measure the attitude of the camera.

Radar, as the main detection means, plays an extremely important role in modern warfare. However, it is easily attacked by the electromagnetic pulse (EMP) weapon. In order to defense the threat, a radome formed by plasma is designed. The plasma radome is used to protect the radar systems against EMP coupling through both ‘front door’ and ‘back door’. What’s more, the damage effects of EMP to the radar systems are analyzed, and the protection mechanism of plasma to EMP weapon is discussed. In addition, a simulation experiment is carried out to verify the feasibility of the plasma radome to defense against EMP. The results indicate that the structure performs well, which makes it a new method against EMP weapon.