This PDF file contains the front matter associated with SPIE Proceedings Volume 12535, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Determining what radar parameters to use for a given scenario is a non-trivial task. When working in a new radar domain, it is quite common to turn to published literature to understand how to approach a new problem. When reviewing research, there can be such a wide range of values used in a radar design that it can become difficult to determine what values to use when designing a new system. An ideal scenario would be to turn to a single source that provides base listings for different radar parameters, but at the time of writing no source is known. In this work, we aim to statistically analyze published radar literature to determine a base set of radar parameter values for a given domain. These parameters include things such as the carrier frequency, bandwidth, pulse repetition frequency, and target range, among many others. To do this, a base set of parameters that are included in nearly all radar systems design will need to be established. Then, by selecting published research in specific domains (ground penetrating radar, atmospheric sensing, imaging, etc . . . ), we can determine the most common values for these parameters. In this paper, we examine the most common values for a given domain, as well as analyze the relationships between these parameters. This information could then be used to develop simulations, optimization problems, or provide insight when developing a new radar system.

Target tracking has become more complicated as radar operating environments have become increasingly congested with clutter, interference, and countermeasures for jamming of radio frequency (RF) signals. As a result, the accuracy and performance of target tracking radars are further degraded. Three configurations of radar systems, namely, monostatic radar, bistatic radar, and passive radar, are commonly used today. The most conventional one is the monostatic radar defined by a co-located transmitter and receiver. In the bistatic radar system, the radar transmitter and receiver are physically separated by a large distance. A passive radar system is a derivative of the bistatic radar system, wherein radar functions are performed without the use of one’s own transmitter. Instead, existing signals-of-opportunity (SOP) within the RF environment are opportunistically exploited to perform the radar functions. This paper presents a concept based on metacognition which entails dynamically switching the mode of operation between the three radar configurations to optimize target tracking accuracy. The paper provides an overview of the three radar configurations followed by the description of an approach for switching radar configuration among the three radars. Modeling and simulation of a passive radar system for target tracking in MATLAB is presented and followed by analysis and discussion of its performance.

This study focuses on developing an extremely low C-SWaP radar sensing solution for single target tracking over a marine environment. The radar system-on-chip (SoC) Utilizes ultra-wideband (UWB) low-probability of intercept (LPI) and low-probability of detection (LPD), which provides a networked, range-based tracking solution when combined with an active phased array antenna. A key innovation of the technical implementation is range extension utilizing digital delay-line triggering, which synchronizes multiple SoC ICs to sample arbitrary ranges simultaneously.

The concept of a unified, portable, and precise navigation solution for future precise manned and unmanned air vehicle landing support would need both enhanced GNSS technology and non-GNSS (such as precise approach radar) together in one package. This concept and initial implementations are discussed. Initial evaluations using different Software-Defined-Radio (SDRs) to implement the GNSS augmentation functions are performed and compared to certified ground augmentation system receivers. The results show the promising feasibility of integrating GNSS augmentation and precise approach radar function to support future landing missions.

Recent work on optimizing antenna installations on large flight inspection (FI) aircraft is presented. The innovative aspect of this study is an automatic procedure and tool than can optimize, analyze, and design the best antenna installation locations using a unified performance index, considering all practical constraints, including signal quality requirements, environmental effects, actual flight geometries, and others. Example design procedures and results for actual flight mission aircraft is provided.

Intelligent reflective surfaces (IRS) have garnered significant attention due to their potential to intelligently shape the wireless environment for beyond-fifth and sixth-generation wireless communication and sensing. IRSs modify radio channels by dynamically adjusting the phase shifts of reflected signals, thereby enhancing signal reception and suppressing interference. For robust beam steering and signal-to-noise ratio optimization, the IRS’s phase profile must be accurately estimated and optimized. This work presents a unique approach for estimating the position and orientation of an indoor IRS using active tags and optimizing the phase shifts with a tag-FMCW radar system for robust radar-based IRS-assisted indoor coverage enhancement. The proposed method offers a practical scheme for localizing the IRS elements and calculating the phase delays effectively, ultimately improving the overall performance of IRS-assisted wireless networks. For implementation, we proposed non-volatile phase-change radio-frequency switches that can operate over a broad range and can provide flexibility in implementation from DC to 67 GHz for 5G-Advanced applications.

Frequency diverse arrays (FDAs), which employ a small frequency increment across each array element, are of recent interest as a popular beam steering technique since they generate a range-angle dependent beampatterns. This paper explores the use of permutations of integers to specify multiples of a transmit frequency offset from carrier for the elements of a multi-sub-FDA, a new architecture in FDA research. The resulting patterns have a peak at a user-specified range and aspect angle, and are periodic in range. Range peak sidelobe ratio (PSLR) and integrated sidelobe level (ISL) analyses with respect to bandwidth reveal the utility of sequences obtained from solved Sudoku puzzles in providing far-field patterns for use in a beacon or for area surveillance.

Because of its simple approximate representation to nonlinear time variant frequency modulations, polynomial chirplet has been used in radar, gravity analysis, electronic warfare and acoustic signal processing. However, due to its high dimension parameter spaces, direct polynomial chirplet transform has extremely high computational cost. In addition, the discrete implementation of polynomial chirplet transform causes a limited parameter estimation accuracy which may not satisfy the requirement in real applications. In this paper, by combining a connected spectrogram graph fitting with random optimization, we develop a new technique to address these computational cost and parameter estimation accuracy issues. We first convert a high dimensional polynomial chirplet transform into a low dimensional spectrogram implementation which significantly reduces computational cost. We then introduce interpolation and random optimization methods to improve the parameter estimation accuracy.

The sliding innovation filter is a newly developed filter that was derived in 2020 to be a predictor-corrector filter. The filter uses the measurement as a hyperplane, and then applies a force that makes the estimates fluctuating around it. The filter works on systems with full ranked measurement matrix (all states are measured). However, once the rank becomes partial, the filter depends highly on the pseudo inverse of the measurement matrix. This means that if the measurement matrix does not have a direct link to the hidden states, then these states will not be correctly estimated. When the system is nonlinear, the problem becomes worse as the Jacobean matrix must be calculated for the measurement matrix before the pseudo inverse is applied. To solve this issue, this paper proposes a new formulation of the SIF that is based on the extended Luenberger filter. The proposed method is tested on extracting the damping ration for a third order system.

Modern approaches to quantum radar implementation utilize the intrinsic correlations of two-mode squeezed vacuum photon pairs emerging from a nonlinear interaction. The most popular approach has been the use of delay lines for the idler and performing joint measurements on the idler and returning signal together. In this paper, it is argued and shown that this sort of implementation is not necessary to extract the quantum cross correlation terms. Immediate detection of the idler and later cross correlation on a large enough data set will yield identical covariance terms. Moreover, immediate idler detection facilitates the use of conventional radar signal processing which allows existing waveform toolboxes of classical radar to be utilized for quantum radar. This allows a much more relaxed set of constraints on the implementation of quantum radar techniques. This paper discusses these concepts, including new detection techniques from the author, and validates the framework with some preliminary experimental data. The presented data, as well as the recent work of others allows for the possibility of a much larger quantum advantage than previously thought, particularly when comparing to real-world practical classical sensors.

Different techniques have been developed and evaluated to address both the RCS minimization and EMC/EMI issues related to propeller modulations of small UAVs, which are part of the issues of survivability of small UAS in counter-UAS environments: (1) surface impedance characterization, (2) radar cross section measurements, and (3) nearfield probing. Initial results from some of these evaluations and analyses, especially the new techniques developed, are presented.

Micro-Doppler radar signatures of helicopters and drones are gaining increasing importance. However, collecting data under controlled conditions on drones in flight can be difficult. The ability to use predictive codes to produce moving target and micro-Doppler radar data is becoming more important. In order to demonstrate the potential use of computer code predictions, this report will describe the X, V, and W-Band micro-Doppler signatures for the DJI Phantom 2 quadcopter. The predictions are generated using the Xpatch prediction code. The motion of all four propellers are simulated for realistic flight conditions. Predictions were performed at multiple viewing angles and using various PRF values. Additionally, different range resolutions were also predicted. The data is analyzed using a series of Range-Doppler spectrograms and short time Fourier transforms. The equations for the motion of the blades are examined in the context of the minimum PRF that is needed for capturing the micro-Doppler information. A discussion is included for finding the best frequency band to operate which balances the tradeoff of information content with operating frequency and PRF value. It is shown in the standard analysis that the unique shape of the blades produced patterns in the micro-Doppler signature that may be of use in target identification. Application of Time-Frequency-Analysis is also demonstrated. The predicted data is compared with micro-Doppler data measured in the laboratory using a 100 GHz compact range on a real Phantom 2 drone.

Coherent change detection (CCD) is an approach for detecting changes in a region that is based on interferometric-type processing of existing data or imagery. It is utilized in satellite and SAR imagery and is quite successful in the detection and localization of subtle scene changes. This work provides further expansion of the theoretical framework for this approach through the application of fundamental wave scattering principles. It is shown that this approach has an interesting physical interpretation, since it implicitly incorporates, as priors, power or energy-budget issues relevant to wave scattering and reflection phenomena. Moreover, a discussion of an alternative form that is more broadly applicable is provided. This general variant enables the detection of changes or threats (such as intrusion) even if the propagation medium is complex and completely unknown.

The addition of a dielectric object into an electromagnetic cavity has the effect of modifying the resonant frequency. We find that the introduction of a highly polarizable cylinder changes the frequency in a way that is not linear with its volume, which is not expected from perturbation theory in the way it is usually applied. From the Boltzmann-Ehrenfest theorem, the ratio of frequency to the total energy remains constant for an adiabatic change to the oscillating system. The perturbed frequency can be computed accurately from the change in energy associated with the work performed when introducing the cylindrical volume to the cavity.

Dielectric measurements of plastic explosives using a loaded waveguide technique via vector network analyzer and banded MMW extender modules operating at V-band (50 – 75 GHz) are performed. A portion of explosive sample is inserted into a waveguide shim 2mm in length and trimmed flush with the faces of the shim. Two-port S-parameter measurements were conducted on the explosive; the empty shim is similarly characterized. Using standard waveguide equations and the measured length of the shim, the complex S-parameter data obtained with the filled shim is optimized to four free parameters — complex permittivity and distance offsets for the two sample faces relative to the calibration planes. Permittivity data obtained from measurements of the plastic explosives C-4, Primasheet 1000, Primasheet 2000 and Semtex 10 are presented. Results obtained for C-4 are comparable to other data in open literature. Excellent agreement between the experiment and the fit is obtained using a constant permittivity across the waveguide band, indicating that dispersion is not significant for these materials.

Active incoherent millimeter-wave imaging is a recently introduced technique that combines incoherent signal transmission with interferometric antenna arrays. This essentially minimizes coordination between transmit and receive apertures and reduces the very high sensitivity requirements found in passive interferometric antenna arrays which capture very low power thermal signals. In this work, we explore short-range image reconstructions of conductive and dielectric targets from a compact 24-element 38 GHz active incoherent imaging array emitting random noise. We include experimental measurements in a semi-anechoic environment.

We present a new approach to nondestructive evaluation that uses the transmission of noncooperative 5G signals of opportunity and a passive millimeter-wave interferometric imaging system. Interferometric imaging samples scene information in the Fourier domain and reconstructs the image via a two-dimensional Fourier transform, providing an imaging mechanism that does not require mechanical or electronic scanning. To accurately form an image, the incident fields must be spatially and temporally incoherent, a criteria that is satisfied by the transmission of multiple independent 5G communications signals. We demonstrate the ability to image cracks in conducting walls using a sparse interferometric receiver capturing the transmitted 5G signals from two independent transmitters. The 38 GHz interferometric array consists of 24 receiving elements and generates images in real time. We employ deconvolution to remove artifacts resulting from the system point spread function, demonstrating the ability to determine the location of cracks in conducting walls.

Millimeter-wave imaging is used in Advanced Imaging Technology (AIT) screening systems to detect concealed objects based on the received radio frequency (RF) signals. The RF signals are collected and digitized from multiple frequencies and multiple antenna positions. We show that the measured frequency response of a targeted object can determine its complex refractive index and provide information on the material composition. This paper shows a method to compute the frequency spectrum from an image reconstructed using a partial set of antennas for which the radar reflection is most likely to be specular.

In recent years, low-cost millimeter-wave CMOS-IC radar have become available. Millimeter-wave radar has been applied to automotive and other applications because of its superior environmental tolerance compared to cameras and LiDAR. On the other hand, it is inferior to other sensors in terms of spatial resolution(recognition), making target detection accuracy an issue. MIMO Radar, also known as virtual array, is one of the promising technologies to improve spatial resolution, but a commercial CMOS-IC generally has only a small number of antennas, which limits the performance improvement. To solve this problem, a recent trend is to increase the number of real antennas by cascading multiple CMOS-ICs on a single substrate, thereby increasing the array aperture size of the virtual array and significantly improving spatial resolution. However, when using such a method, reflected waves from targets in short range areas cannot be regarded as plane waves but become spherical waves, where target DoA estimation with conventional(far-field) mode vector degrades accuracy. This paper presents a method to achieve improved spatial resolution and suppressed performance degradation in near-field areas without any compensations of conventional mode vector, by simply laying the multiple boards side by side to form overlapped virtual array elements, even if the board consists of a single CMOS-IC. Computer simulations and fundamental experiments show that the proposed method can ideally achieve spatial resolution and DoA estimation accuracy, even when the target is at 0.5m.

We present recent developments of a standoff imaging system based on a frequency-diverse phase hologram and deep neural networks. The single-pixel imaging system operates in a monostatic configuration consisting of a 340-GHz FMCW radar and a frequency-diverse phase hologram to interrogate the radar down range direction with spatially varying, frequency-dependent field patterns. The measured back-reflected signal contains spatial reflectivity information from the target, and the fast chirp rate of the radar enables real-time imaging performance. Together with simultaneously acquired visible-light images, a deep neural network integrated into the submillimeter-wave data readout electronics can map the received signal onto a 2D image without mechanical or active electrical beam scanning. In experiments, we have collected submillimeter-wave and visible-light data of a moving target in the region of interest with a 60-Hz frame rate. The results suggest that the system can image the moving target with a resolution comparable to the theoretical diffraction limit. The minimal hardware complexity and good imaging performance of the demonstrated computational submillimeter-wave imaging system support its potential as a cost-effective and easily deployable solution for various imaging applications.

Focal plane arrays (FPA) constructed using glow discharge detectors (GDD) as the pixel elements proved to be an inexpensive methodology for generating MMW (millimeter wave)/THz (terahertz) images. In the abnormal glow mode of operation, the weakly ionized plasma (WIP) in GDDs can be more responsive while interacting with the incident MMW/THz radiations. It has explicitly been found that the major influence of MMW/THz radiation on the emitted light spectrum from the GDD is located in the near-infrared (NIR) zone of the electromagnetic spectrum which is around 800 nm– 1000 nm. Also, there is no influence of the MMW/THz on the visual band ranging from 500 nm–600 nm emitted from the GDD. The up-conversion method utilized here refers to the detection of variations in the intensity of emitted light from the GDD due to the incident MMW radiation. A charge-coupled device (CCD) camera is employed here to generate MMW/THz images by capturing the light output from the GDD pixel elements on the FPA located in the image plane. The DC bias voltage emitted light from the GDD is much stronger than the modulated light produced as a result of the incident MMW/THz radiation. The major challenge of this work is to measure this minute variation in the GDD light output caused by the MMW/THz radiation and to distinguish it from the intense DC bias operation light of the GDD. For achieving this, we propose using an optical long-pass filter as a part of the CCD camera component, thus enhancing the performance of the suggested up-conversion method. The addition of the long-pass filter eliminates most of the highly intense visual spectrum from the light output of the GDD, thereby decreasing noise and making the up-conversion imaging more effective. Here, we demonstrated the feasibility of implementing GDD-based FPAs using up-conversion readout for MMW/THz imaging applications in the NIR regime by testing with a single GDD whose detection impact was captured using a CCD camera whose zoom lens was coupled with a long pass optical filter.

This talk aims to cover the state-of-the-arts of automotive radar technologies, which include multiple-input multiple-output (MIMO) techniques, interference detection and mitigation, sensor fusion, target classification, and road condition detection. Besides the traditional applications that use radar to survey the surrounding environment, this talk also provides an overview of the radar-based in-cabin sensing, including gesture sensing for human-vehicle interaction and driver/passenger vital signs and presence monitoring.

Software Defined Radar (SDR) systems have gained popularity in recent years as a flexible, adaptable, and scalable solution for automotive applications. SDR integrates within a Software Defined Vehicle (SDV) stack by separating the vehicle’s higher level perception applications from the radar hardware. Interoperability between these components is crucial to building more robust safety systems as the improvement in perception through radar control allows the vehicle to react to changing environments and scenarios. SDR is emerging as a critical component for enabling automotive safety technologies to be fielded in a safe and robust manner.

Research into autonomous vehicles has focused on purpose-built vehicles with Lidar, camera, and radar systems. Many vehicles on the road today have sensors built into them to provide advanced driver assistance systems. In this paper we assess the ability of low-end automotive radar coupled with lightweight algorithms to perform scene segmentation. Results from a variety of scenes demonstrate the viability of this approach that complement existing autonomous driving systems.

This study investigates the potential interference between 5G New Radio (NR) and radar altimeters in various environments, focusing on the impact on different radar types at varying heights and distances in urban and rural macrocell settings. The research also explores the development of a Convolutional Neural Network (CNN) deep learning model to classify and identify 5G NR and radar altimeter signals, aiming to detect harmful interference and enhance overall aviation safety. The results reveal significant interference effects on radar altimeters from various 5G base station configurations, especially at lower altitudes, and demonstrate the exceptional performance of the CNN model in classifying signals with high accuracy, sensitivity, and specificity. These findings highlight the importance of ongoing research to address interference mitigation techniques and improve signal classification methods, ensuring the safe coexistence of 5G and radar altimeter systems.

We present experimental results on computational submillimeter-wave ghost imaging schemes. The schemes include a dispersive element introducing quasi-incoherent field patterns to the field of view and bucket detection of the back-reflected field across a significantly broad bandwidth. A single bucket detection without discrimination of the field of view into image pixels is used. The imaging experiments at 220-330GHz with dispersive hologram show successful computational ghost imaging of a corner-cube reflector target at 600-mm distance. Two separate image-forming methods are compared: correlation and machine-learning. In the correlation method, the image is formed by integrating the predetermined quasi-incoherent field patterns weighted with the bucket detections. In the machine-learning method, high image quality can be achieved after non-trivial training campaigns. The great benefit of the correlation method is that, while the quasi-incoherent patterns need to be known, no a priori iterative training to the images is required. The experiments with the correlation method demonstrate resolving of the target at 600-mm distance.

Deep machine learning computer vision algorithms have been widely explored for the purpose of multisensory data fusion. The ability to combine feature, pixel, and decision level information from multiple sensors in order to enhance accurate assessments and decisions made by the platforms has been a significant point of interest for the remote sensing community. In this paper, we propose a dual branch 3D convolutional neural network (CNN) to bi-long short-term memory network (BILSTM) algorithm that seeks to fuse sparse multiresolution, multi-pose and multimodal VV and HV polarizations of synthetic aperture radar (SAR) vehicle image information to enhance vehicle identification in unfamiliar and uncoherent environments. We cultivated and explored the proposed algorithm using the SDMS CV Data Domes repository of 14,430 augmented images per modality, equally represented over ten vehicle classes under similar and dissimilar vehicle pose augmentations with low to high levels of added testing set noise via zero-mean white Gaussian noise. Our results indicated that the local individual modality 3D convolution fusion of multiple poses and resolutions as well as dual-modality fusion of both polarizations enhanced the developed algorithm’s ability to classify SAR vehicle image information in unfamiliar pose, elevation angle and moderate to low noise environments.

Ultra-wideband (UWB) ground-penetrating radar (GPR) technology has been widely employed for detecting underground targets, structures, or anomalies. However, the backscatter signals from the ground surface pose a critical challenge for downward-looking GPR systems since 1) these ground return signals have significant power compared to the backscatter signal from subsurface targets, and 2) the ground return and target signals completely overlap in both the time and frequency domains. This paper presents a technique for reconstructing and extracting the GRI signals from downward-looking UWB GPR signals. This simultaneous low-rank and sparse algorithm models the GRI signals as a low-rank matrix, while the return signals from the targets are represented by sparse signals. The solver simultaneously optimizes both objectives, resulting in the separation of the target signals from the GRI signals. Our technique performs this GRI extraction directly in the phase history data domain prior to synthetic aperture radar (SAR) image formation. Thus, it can be implemented as an additional step, completely independent from all other steps, in the pre-processing stage. Recovery results from both simulated and real data sets illustrate the robustness and effectiveness of our proposed technique.

In recent years, dynamic metasurface antennas (DMAs) have been proposed as an efficient alternative platform for computational imaging, which can drastically simplify the hardware architecture. In this paper, we first mathematically describe the existing solution to be able to convert raw measurements obtained by a DMA in the frequency-space domain into raw data on Fourier bases. Next, an optimization problem based on compressive sensing theory is defined, through which only a limited share of the total frequency/spatial data will be needed. The converted/retrieved data are used to reconstruct the image in the Fourier domain. The performance of the corresponding image reconstruction techniques (with/without Stolt interpolation operation) is evaluated in terms of the quality of the reconstructed image (both visually and quantitatively) and computational time with computer simulations.

The radar signatures of UAVs can be analyzed with micro-Doppler signal processing to produce features that may allow classification based on size or type as well as recognition of actions. This paper explores different micro-Doppler processing approaches to create more consistent UAV micro-Doppler signatures and improve the separability of relevant UAV features. The techniques are demonstrated through theory and simulation of UAV rotor blades. The feature extraction from a blade flash phenomenology is adapted to use with HeRM line phenomenology and found to be feasible but less accurate. HeRM lines are shown to capture the radar brightness structures shown in the blade flash, and HeRM lines do allow recognition of a change in the motion.

Radar track classification or discrimination is a capability that unlocks the potential of the common radar sensor that typically reports only track information. For example, in counter-UAV analysis there are many more tracks from birds and other moving clutter than from UAVs, so UAV radars can be overwhelmed with uninteresting tracks of birds. Track classification tries to identify the characteristics of UAVs in flight to discriminate UAVs from other tracks and thus to reduce false alarms. We have performed a pilot project on machine learning of the track characteristics to develop this capability, finding better than 90% sensitivity and specificity on recognizing multiple types of target UAVs.

In this paper, first, the structure of a linear sparse periodic array for two-dimensional scanning is described. Then, based on its characteristics, an algorithm is presented for fast image reconstruction of the scene in a near-field (NF) multistatic terahertz imaging scenario. Although the basis of this algorithm is developed in the Fourier domain, it is compatible with the non-uniform structure of the array and also takes into account the phase deviations caused by multistatic imaging in NF. The performance of the proposed approach is evaluated with numerical data obtained from electromagnetic simulations in FEKO as well as experimental data. The results are discussed in terms of computational time on the central processing unit and graphics processing unit as well as the quality of the reconstructed image.