Качественная оценка некоторых методов спектрального анализа

Parametric methods of spectral analysis play an important role when solving the problems of primary radar data processing. The paper mainly focuses on comparing methods to obtain a spectrum by means of two criteria, i.e. computational complexity and resolution. The latter represents the most important characteristic of the algorithms used to process a spectrum of the reflected radar signal.The paper considers mathematical foundations of parametric methods, which use white noise as an excitation sequence, including the models: an autoregressive, moving average model and an autoregressive-moving average one. Reveals their differences from the classical approach to the spectral analysis represented by periodogram and correlogram methods. Emphasizes the greatest popularity of the autoregressive model due to linearity of dependencies used in it. Shows the main advantages and disadvantages of a number of autoregressive methods. Considers non-classical methods based on the analysis of eigenvalues.An experimental study of the spectral analysis methods in the MATLAB environment has been carried out for to compare them in terms of computational complexity and resolution. The simulation results with the input signal, represented by the superposition of harmonics on white noise, are shown. The character of the spectra peaks obtained and the order of each model, desirable for their resolution, are estimated.An analysis of the results obtained has shown that among the methods considered, those of based on the analysis of eigenvalues possess the best resolution and the greatest computational complexity. The autoregressive parametric methods have revealed similar properties. Classical methods are characterized by the least computational complexity and the least accuracy. A conclusion has been that the spectrum quality depends on the order of the model used and on the input signal noisiness.The results obtained can be used when solving the problems of primary radar data processing to provide a reasonable choice of spectral analysis methods based on resolution and computational complexity.В задачах первичной обработки радиолокационной информации заметную роль играют параметрические методы спектрального анализа. В настоящей работе основное внимание уделяется сравнению методов получения спектра по двум критериям – вычислительной сложности и разрешающей способности. Последняя представляет собой наиболее важную характеристику алгоритмов, применяемых для обработки спектра отраженного радиолокационного сигнала.Рассмотрены математические основы параметрических методов, использующих белый шум в качестве возбуждающей последовательности, в том числе модели: авторегрессионная, скользящего среднего и авторегрессии – скользящего среднего. Выявлены их отличия от классического подхода к анализу спектра, представленного периодограммным и коррелограммным методами. Подчеркнута наибольшая популярность авторегрессионной модели, обусловленная линейностью использующихся в ней зависимостей. Показаны основные достоинства и недостатки ряда авторегрессионных методов. Рассмотрены неклассические методы, базирующиеся на анализе собственных значений.Проведено экспериментальное исследование методов спектрального анализа в среде MATLAB с целью их сравнения по вычислительной сложности и разрешающей способности. Показаны результаты моделирования на входном сигнале, представленном наложением гармоник на белый шум. Выполнена оценка характера пиков полученных спектров и порядок каждой модели, необходимый для их разрешения.Анализ полученных результатов показал, что среди рассмотренных методов наилучшим разрешением и наибольшей вычислительной сложностью обладают методы, основанные на анализе собственных значений. Схожие свойства выявлены у авторегрессионных параметрических методов. Классическим методам свойственны наименьшая вычислительная сложность и наименьшая точность. Сделан вывод о зависимости качества спектра от порядка используемой модели и зашумленности входного сигнала.Результаты работы могут быть использованы при решении задач первичной обработки радиолокационных данных для обоснованного выбора методов спектрального анализа по разрешающей способности и вычислительной сложности

Low-Complexity MUSIC-Based Direction-of-Arrival Detection Algorithm for Frequency-Modulated Continuous-Wave Vital Radar

This paper proposes a low complexity multiple-signal-classifier (MUSIC)-based direction-of-arrival (DOA) detection algorithm for frequency-modulated continuous-wave (FMCW) vital radars. In order to reduce redundant complexity, the proposed algorithm employs characteristics of distance between adjacent arrays having trade-offs between field of view (FOV) and resolution performance. First, the proposed algorithm performs coarse DOA estimation using fast Fourier transform. On the basis of the coarse DOA estimation, the number of channels as input of the MUSIC algorithm are selected. If the estimated DOA is smaller than 30◦, it implies that there is an FOV margin. Therefore, the proposed algorithm employs only half of the channels, that is, it is the same as doubling the spacing between arrays. By doing so, the proposed algorithm achieves more than 40% complexity reduction compared to the conventional MUSIC algorithm while achieving similar performance. By experiments, it is shown that the proposed algorithm despite the low complexity is enable to distinguish the adjacent DOA in a practical environment. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.1

Passive Synthetic Aperture Radar Imaging Using Commercial OFDM Communication Networks

Modern communication systems provide myriad opportunities for passive radar applications. OFDM is a popular waveform used widely in wireless communication networks today. Understanding the structure of these networks becomes critical in future passive radar systems design and concept development. This research develops collection and signal processing models to produce passive SAR ground images using OFDM communication networks. The OFDM-based WiMAX network is selected as a relevant example and is evaluated as a viable source for radar ground imaging. The monostatic and bistatic phase history models for OFDM are derived and validated with experimental single dimensional data. An airborne passive collection model is defined and signal processing approaches are proposed providing practical solutions to passive SAR imaging scenarios. Finally, experimental SAR images using general OFDM and WiMAX waveforms are shown to validate the overarching signal processing concept

Multiple moving target detection with ultra wideband radar using super-resolution algorithms

The improvements in microwave electronics opened the way to build microwave components such as low noise amplifiers, samplers and pulse generators that are broadband. As these building blocks are being developed, new applications become subject of research. Ultra wideband radar is one of these subjects. Major applications of ultra wideband radars are behind the wall imaging, biomedical imaging and buried land mine detection. In this study we aimed to locate multiple scatterers that are moving. Even though there are many scatterers in an environment, detection of moving targets is possible using differences of successive radar snapshots. This is generally the case when behind the wall human targets are to be detected. We investigated the effectiveness of various types Multiple Signal Classification (MUSIC) algorithms on the data acquired by our ultra wideband radar prototype. In ideal computer simulations, Time Reversal MUSIC (TRM) algorithm provides successful estimations of both directions and distances of multiple targets. However in practice where non-ideal effects are existent, the performance of TRM algorithm is estimating the target distances degrades. On the other hand, Delay Estimation MUSIC algorithm provides better estimates for the distances of the targets since it is less sensitive to phase noise. Combining the output of TRM algorithm for target directions and the output of Delay Estimation MUSIC method for target distances resulted in successful localization of targets. Experiments are performed using two moving targets in order to test the effectiveness the proposed processing scheme. The problem of detection ambiguities is also considered and several methods to resolve actual targets are presented

Signal Subspace Processing in the Beam Space of a True Time Delay Beamformer Bank

A number of techniques for Radio Frequency (RF) source location for wide bandwidth signals have been described that utilize coherent signal subspace processing, but often suffer from limitations such as the requirement for preliminary source location estimation, the need to apply the technique iteratively, computational expense or others. This dissertation examines a method that performs subspace processing of the data from a bank of true time delay beamformers. The spatial diversity of the beamformer bank alleviates the need for a preliminary estimate while simultaneously reducing the dimensionality of subsequent signal subspace processing resulting in computational efficiency. The pointing direction of the true time delay beams is independent of frequency, which results in a mapping from element space to beam space that is wide bandwidth in nature. This dissertation reviews previous methods, introduces the present method, presents simulation results that demonstrate the assertions, discusses an analysis of performance in relation to the Cramer-Rao Lower Bound (CRLB) with various levels of noise in the system, and discusses computational efficiency. One limitation of the method is that in practice it may be appropriate for systems that can tolerate a limited field of view. The application of Electronic Intelligence is one such application. This application is discussed as one that is appropriate for a method exhibiting high resolution of very wide bandwidth closely spaced sources and often does not require a wide field of view. In relation to system applications, this dissertation also discusses practical employment of the novel method in terms of antenna elements, arrays, platforms, engagement geometries, and other parameters. The true time delay beam space method is shown through modeling and simulation to be capable of resolving closely spaced very wideband sources over a relevant field of view in a single algorithmic pass, requiring no course preliminary estimation, and exhibiting low computational expense superior to many previous wideband coherent integration techniques

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Signal Subspace Processing in the Beam Space of a True Time Delay Beamformer Bank

A number of techniques for Radio Frequency (RF) source location for wide bandwidth signals have been described that utilize coherent signal subspace processing, but often suffer from limitations such as the requirement for preliminary source location estimation, the need to apply the technique iteratively, computational expense or others. This dissertation examines a method that performs subspace processing of the data from a bank of true time delay beamformers. The spatial diversity of the beamformer bank alleviates the need for a preliminary estimate while simultaneously reducing the dimensionality of subsequent signal subspace processing resulting in computational efficiency. The pointing direction of the true time delay beams is independent of frequency, which results in a mapping from element space to beam space that is wide bandwidth in nature. This dissertation reviews previous methods, introduces the present method, presents simulation results that demonstrate the assertions, discusses an analysis of performance in relation to the Cramer-Rao Lower Bound (CRLB) with various levels of noise in the system, and discusses computational efficiency. One limitation of the method is that in practice it may be appropriate for systems that can tolerate a limited field of view. The application of Electronic Intelligence is one such application. This application is discussed as one that is appropriate for a method exhibiting high resolution of very wide bandwidth closely spaced sources and often does not require a wide field of view. In relation to system applications, this dissertation also discusses practical employment of the novel method in terms of antenna elements, arrays, platforms, engagement geometries, and other parameters. The true time delay beam space method is shown through modeling and simulation to be capable of resolving closely spaced very wideband sources over a relevant field of view in a single algorithmic pass, requiring no course preliminary estimation, and exhibiting low computational expense superior to many previous wideband coherent integration techniques

Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling, and Antenna Designs

77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadays’ development of advanced automotive radar systems because radar simulation can accelerate the design and testing process and help people to better understand and process the radar data. The main challenge in automotive radar simulation is to simulate the complex scattering behavior of various targets in real time, which is required for sensor fusion with other sensory simulation, e.g. optical image simulation. In this thesis, an asymptotic method based on a fast-wideband physical optics (PO) calculation is developed and applied to get high fidelity radar response of traffic scenes and generate the corresponding radar images from traffic targets. The targets include pedestrians, vehicles, and other stationary targets. To further accelerate the simulation into real time, a physics-based statistical approach is developed. The RCS of targets are fit into statistical distributions, and then the statistical parameters are summarized as functions of range and aspect angles, and other attributes of the targets. For advanced radar with multiple transmitters and receivers, pixelated-scatterer statistical RCS models are developed to represent objects as extend targets and relax the requirement for far-field condition. A real-time radar scene simulation software, which will be referred to as Michigan Automotive Radar Scene Simulator (MARSS), based on the statistical models are developed and integrated with a physical 3D scene generation software (Unreal Engine 4). One of the major challenges in radar signal processing is to detect the angle of arrival (AOA) of multiple targets. A new analytic multiple-sources AOA estimation algorithm that outperforms many well-known AOA estimation algorithms is developed and verified by experiments. Moreover, the statistical parameters of RCS from targets and radar images are used in target classification approaches based on machine learning methods. In realistic road traffic environment, foliage is commonly encountered that can potentially block the line-of-sight link. In the second part of the thesis, a non-line-of-sight (NLoS) vehicular propagation channel model for tree trunks at two vehicular communication bands (5.9 GHz and 60 GHz) is proposed. Both near-field and far-field scattering models from tree trunk are developed based on modal expansion and surface current integral method. To make the results fast accessible and retractable, a macro model based on artificial neural network (ANN) is proposed to fit the path loss calculated from the complex electromagnetic (EM) based methods. In the third part of the thesis, two broadband (bandwidth > 50%) omnidirectional antenna designs are discussed to enable polarization diversity for next-generation communication systems. The first design is a compact horizontally polarized (HP) antenna, which contains four folded dipole radiators and utilizing their mutual coupling to enhance the bandwidth. The second one is a circularly polarized (CP) antenna. It is composed of one ultra-wide-band (UWB) monopole, the compact HP antenna, and a dedicatedly designed asymmetric power divider based feeding network. It has about 53% overlapping bandwidth for both impedance and axial ratio with peak RHCP gain of 0.9 dBi.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163001/1/caixz_1.pd

Software defined radar system

Software defined radar concept and simulation -- Signal processing methods of synthetic software defined radar -- Mixer-based synthetic software defined radar -- Six-port-based syunthetic software defined radar -- Performance study of synthetic software defined radar