Multipath Parameter Estimation from OFDM Signals in Mobile Channels

We study multipath parameter estimation from orthogonal frequency division multiplex signals transmitted over doubly dispersive mobile radio channels. We are interested in cases where the transmission is long enough to suffer time selectivity, but short enough such that the time variation can be accurately modeled as depending only on per-tap linear phase variations due to Doppler effects. We therefore concentrate on the estimation of the complex gain, delay and Doppler offset of each tap of the multipath channel impulse response. We show that the frequency domain channel coefficients for an entire packet can be expressed as the superimposition of two-dimensional complex sinusoids. The maximum likelihood estimate requires solution of a multidimensional non-linear least squares problem, which is computationally infeasible in practice. We therefore propose a low complexity suboptimal solution based on iterative successive and parallel cancellation. First, initial delay/Doppler estimates are obtained via successive cancellation. These estimates are then refined using an iterative parallel cancellation procedure. We demonstrate via Monte Carlo simulations that the root mean squared error statistics of our estimator are very close to the Cramer-Rao lower bound of a single two-dimensional sinusoid in Gaussian noise.Comment: Submitted to IEEE Transactions on Wireless Communications (26 pages, 9 figures and 3 tables

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

Reducing the Computational Complexity of WiFi-Based Passive Radar Processing

WiFi-based passive radar is considered in this paper as an effective technology for short range monitoring applications. Aiming at limiting its complexity and enhancing its suitability for civilian applications, appropriate modifications are proposed to the signal processing scheme originally designed for such sensor. Specifically, we show that a simple inversion in the order of the main processing stages, namely clutter cancellation and range compression, allows to both reduce the number of floating-point operations and relax the requirements on the data management. Moreover, the use of a reciprocal filter in lieu of a matched filter to implement the range compression stage is proved to yield a further simplification in the resulting processing scheme along with additional benefits in terms of achievable performance in the considered application. The alternative processing schemes are compared in terms of computational burden and the effectiveness of the proposed cost-effective solutions is proved against experimental datasets

Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing

In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access (OFDMA) and spatial division multiple access (SDMA) communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.Comment: 14 pages, 12 figures, Journal pape

OFDM passive radar employing compressive processing in MIMO configurations

A key advantage of passive radar is that it provides a means of performing position detection and tracking without the need for transmission of energy pulses. In this respect, passive radar systems utilising (receiving) orthogonal frequency division multiplexing (OFDM) communications signals from transmitters using OFDM standards such as long term evolution (LTE), WiMax or WiFi, are considered. Receiving a stronger reference signal for the matched filtering, detecting a lower target signature is one of the challenges in the passive radar. Impinging at the receiver, the OFDM waveforms supply two-dimensional virtual uniform rectangul ararray with the first and second dimensions refer to time delays and Doppler frequencies respectively. A subspace method, multiple signals classification (MUSIC) algorithm, demonstrated the signal extraction using multiple time samples. Apply normal measurements, this problem requires high computational resources regarding the number of OFDM subcarriers. For sub-Nyquist sampling, compressive sensing (CS) becomes attractive. A single snap shot measurement can be applied with Basis Pursuit (BP), whereas l1-singular value decomposition (l1-SVD) is applied for the multiple snapshots. Employing multiple transmitters, the diversity in the detection process can be achieved. While a passive means of attaining three-dimensional large-set measurements is provided by co-located receivers, there is a significant computational burden in terms of the on-line analysis of such data sets. In this thesis, the passive radar problem is presented as a mathematically sparse problem and interesting solutions, BP and l1-SVD as well as Bayesian compressive sensing, fast-Besselk, are considered. To increase the possibility of target signal detection, beamforming in the compressive domain is also introduced with the application of conve xoptimization and subspace orthogonality. An interference study is also another problem when reconstructing the target signal. The networks of passive radars are employed using stochastic geometry in order to understand the characteristics of interference, and the effect of signal to interference plus noise ratio (SINR). The results demonstrate the outstanding performance of l1-SVD over MUSIC when employing multiple snapshots. The single snapshot problem along with fast-BesselK multiple-input multiple-output configuration can be solved using fast-BesselK and this allows the compressive beamforming for detection capability

Passive radar on moving platforms exploiting DVB-T transmitters of opportunity

The work, effort, and research put into passive radar for stationary receivers have shown significant developments and progress in recent years. The next challenge is mounting a passive radar on moving platforms for the purpose of target detection and ground imaging, e.g. for covert border control. A passive radar on a moving platform has many advantages and offers many benefits, however there is also a considerable drawback that has limited its application so far. Due to the movement the clutter returns are spread in Doppler and may overlap moving targets, which are then difficult to detect. While this problem is common for an active radar as well, with a passive radar a further problem arises: It is impossible to control the exploited time-varying waveform emitted from a telecommunication transmitter. A conventional processing approach is ineffective as the time-varying waveform leads to residuals all over the processed data. Therefore a dedicated clutter cancellation method, e.g. the displaced phase centre antenna (DPCA) approach, does not have the ability to completely remove the clutter, so that target detection is considerably limited. The aim must be therefore to overcome this limitation by exploiting a processing technique, which is able to remove these residuals in order to cope with the clutter returns thus making target detection feasible. The findings of this research and thesis show that a reciprocal filtering based stage is able to provide a time-invariant impulse response similar to the transmissions of an active radar. Due to this benefit it is possible to achieve an overall complete clutter removal together with a dedicated DPCA stage, so that moving target detection is considerably improved, making it possible in the first place. Based on mathematical analysis and on simulations it is proven, that by exploiting this processing in principle an infinite clutter cancellation can be achieved. This result shows that the reciprocal filter is an essential processing stage. Applications on real data acquired from two different measurement campaigns prove these results. By the proposed approach, the limiting factor (i.e. the time-varying waveform) for target detection is negotiated, and in principle any clutter cancellation technique known from active radar can be applied. Therefore this analysis and the results provide a substantial contribution to the passive radar research community and enables it to address the next questions

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

Estudo de formas de onda e conceção de algoritmos para operação conjunta de sistemas de comunicação e radar

The focus of this thesis is the processing of signals and design of algorithms that can be used to enable radar functions in communications systems. Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier modulation waveform in communication systems. As a wideband signal, OFDM improves resolution and enables spectral efficiency in radar systems, while also improving detection performance thanks to its inherent frequency diversity. This thesis aims to use multicarrier waveforms for radar systems, to enable the simultaneous operation of radar and communication functions on the same device. The thesis is divided in two parts. The first part, studies the adaptation and application of other multicarrier waveforms to radar functions. At the present time many studies have been carried out to jointly use the OFDM signal for communication and radar functions, but other waveforms have shown to be possible candidates for communication applications. Therefore, studies on the evaluation of the application of these same signals to radar functions are necessary. In this thesis, to demonstrate that other multicarrier waveforms can overcome the OFDM waveform in radar/communication (RadCom) systems, we propose the adaptation of the filter bank multicarrier (FBMC), generalized frequency division multiplexing (GFDM) and universal filtering multicarrier (UFMC) waveforms for radar functions. These alternative waveforms were compared performance-wise regarding achievable target parameter estimation performance, amount of residual background noise in the radar image, impact of intersystem interference and flexibility of parameterization. In the second part of the thesis, signal processing techniques are explored to solve some of the limitations of the use of multicarrier waveforms for RadCom systems. Radar systems based on OFDM are promising candidates for future intelligent transport networks. Exploring the dual functionality enabled by OFDM, we presents cooperative methods for high-resolution delay-Doppler and direction-of-arrival estimation. High-resolution parameter estimation is an important requirement for automotive radar systems, especially in multi-target scenarios that require reliable target separation performance. By exploring the cooperation between vehicles, the studies presented in this thesis also enable the distributed tracking of targets. The result is a highly accurate multi-target tracking across the entire cooperative vehicle network, leading to improvements in transport reliability and safety.O foco desta tese é o processamento de sinais e desenvolvimento de algoritmos que podem ser utilizados para a habilitar a função de radar nos sistemas de comunicação. OFDM (Orthogonal Frequency Division Multiplexing) é uma forma de onda com modulação multi-portadora, popular em sistemas de comunicação. Para sistemas de radar, O OFDM melhora a resolução e fornece eficiência espectral, além disso sua diversidade de frequências melhora o desempenho na detecção do radar. Essa tese tem como objetivo utilizar formas de onda multi-portadoras para sistemas de radar, possibilitando a operação simultânea de funções de radar e de comunicação num mesmo dispositivo. A tese esta dividida em duas partes. Na primeira parte da tese são realizados estudos da adaptabilidade de outras formas de onda multi-portadora para funções de radar. Nos dias atuais, muitos estudos sobre o uso do sinal OFDM para funções de comunicação e radar vêm sendo realizados, no entanto, outras formas de onda mostram-se possíveis candidatas a aplicações em sistemas de comunicação, e assim, avaliações para funções de sistema de radar se tornam necessárias. Nesta tese, com a intenção de demonstrar que formas de onda multi-portadoras alternativas podem superar o OFDM nos sistemas de Radar/comunicação (RadCom), propomos a adaptação das seguintes formas de onda: FBMC (Filter Bank Multicarrier); GFDM (Generalized Frequency Division Multiplexing); e UFMC (Universal Filtering Multicarrier) para funções de radar. Também produzimos uma análise de desempenho dessas formas de onda sobre o aspecto da estimativa de parâmetros-alvo, ruído de fundo, interferência entre sistemas e parametrização do sistema. Na segunda parte da tese serão explorados técnicas de processamento de sinal de forma a solucionar algumas das limitações do uso de formas de ondas multi-portadora para sistemas RadCom. Os sistemas de radar baseados no OFDM são candidatos promissores para futuras redes de transporte inteligentes, porque combinam funções de estimativa de alvo com funções de rede de comunicação em um único sistema. Explorando a funcionalidade dupla habilitada pelo OFDM, nesta tese, apresentamos métodos cooperativos de alta resolução para estimar o posição, velocidade e direção dos alvos. A estimativa de parâmetros de alta resolução é um requisito importante para sistemas de radar automotivo, especialmente em cenários de múltiplos alvos que exigem melhor desempenho de separação de alvos. Ao explorar a cooperação entre veículos, os estudos apresentados nesta tese também permitem o rastreamento distribuído de alvos. O resultado é um rastreamento multi-alvo altamente preciso em toda a rede de veículos cooperativos, levando a melhorias na confiabilidade e segurança do transporte.Programa Doutoral em Telecomunicaçõe