An Exact Near-Field Model Based Localization for Bistatic MIMO Radar with COLD arrays

Most existing near-field (NF) source localization algorithms are developed based on the Fresnel approximation model, and assume that the spatial amplitudes of the target at the sensors are equal. Unlike these algorithms, an NF source parameter estimation algorithm is proposed, based on the exact spatial propagation geometry model, for bistatic multiple-input multiple-output (MIMO) radar deployed with a linear concentered orthogonal loop and dipole (COLD) array at both the transmitter and receiver. The proposed method first compresses the output signal of the matched filter at the receiver into a third-order parallel factor (PARAFAC) data model, on which a trilinear decomposition is performed, and subsequently three factor matrices can be obtained. Then, multiple parameters of interest, including direction-of-departure (DOD), direction-of-arrival (DOA), range from transmitter to target (RFTT), range from target to receiver (RFTR), two-dimensional (2-D) transmit polarization angle (TPA) and 2-D receive polarization angle (RPA), are estimated from the spatial amplitude ratio exploiting the rotation invariant property and the Khatri-Rao product. Finally, the phase uncertainties of transmit and receive arrays can be extracted from additional phase items. The proposed algorithm avoids spectrum peak search, and the estimated parameters in closed forms can be automatically matched unambiguously. In addition, it is suitable for non-uniform linear arrays (NLA) with arbitrary array element spacing and phase uncertainty. Advantages of the proposed method are demonstrated by simulation results

Analysis of the sum rate for massive MIMO using 10 GHz measurements

Orientador: Gustavo FraidenraichTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta um conjunto de contribuições para caracterização e modelagem de canais reais de rádio abordando aspectos relacionados com as condições favoráveis de propagação para sistemas massive MIMO. Discutiremos como caracterizar canais de rádio em um ambiente real, processamento de dados e análise das condições favoráveis de propagação. Em uma segunda parte, focamos na determinação teórica de alguns aspectos da tecnologia de massive MIMO utilizando propriedades de distribuições matriciais Wishart. Inicialmente, apresentamos uma contribuição sobre a aplicação do algoritmo ESPRIT, para estimar parâmetros de um conjunto de dados multidimensional. Obtivemos dados por varredura em frequência de um Analisador Vetorial de Rede e os adaptamos para o algoritmo ESPRIT. Mostramos como remover a influência do ganho de padrão de antenas e como utilizar um gerador de modelo de canal baseado nas medidas reais de canal de rádio. As medidas foram feitas na frequência de 10.1 GHz com largura de faixa de 500 MHz. Utilizando um gerador de modelo de canal, fomos além do universo das simulações por distribuições Gaussianas. Introduzimos o conceito de propagação favorável e analisamos condições de linha-de-visada usando arranjos lineares uniformes e arranjos retangulares uniformes de antena. Como novidade da pesquisa, mostramos os benefícios de explorar um número extra de graus de liberdade devido à escolha dos formatos de arranjo de antenas e ao aumento do número de elementos. Esta propriedade é observada ao analisarmos a distribuição dos autovalores de matrizes Gramianas. Em seguida, estendemos o mesmo raciocínio para as matrizes de canal geradas a partir de informações reais e verificamos se as propriedades ainda permaneceriam válidas. Na segunda parte deste trabalho, incluímos mais de uma antena no terminal móvel e calculamos a probabilidade de indisponibilidade para várias configurações de antenas e número arbitrário de usuários. Esboçamos inicialmente a formulação para a informação mútua e, em seguida, calculamos os resultados exatos em uma situação com dois usuários e duas antenas, tanto na estação base (EB) como nos terminais de usuário(TU). Visto que as formulações para a derivação exata dos casos com mais antenas e mais usuários mostrou-se muito intrincada, propusemos uma aproximação Gaussiana para simplificar o problema. Esta aproximação foi validada por simulações Monte Carlo para diferentes relações sinal/ruídoAbstract: This thesis presents a set of contributions for channel modeling and characterization of real radio channels delineating aspects related with the favorable propagation for massive MIMO systems. We will discuss about how to proceed for characterizing radio channels in an real environment , data processing, and analysis of favorable conditions. In a second part, we focused on determination of some theoretical aspects of the Massive MIMO technology using properties of Wishart distribution matrices. We initially present a contribution on the application of ESPRIT algorithm for estimating a multidimensional set of measured data. We have obtained data by frequency sweep carried out by a vector network analyzer(VNA) and adapted it to fit in the ESPRIT algorithm. We show how to remove antenna pattern gain using virtual antenna arrays and how to use a channel model generator based on radio channel measurements of real environments. The measurements were conducted at the frequency of 10.1 GHz and 500 MHz bandwidth. By using a channel model generator, we have explored beyond the simulation of Gaussian Distributions. We will introduce the concept of favorable propagation and analyze the line-of-sight conditions using ULA and URA array shapes. As a research novelty, we will show the benefits of exploiting an extra degree of freedom due to the choice of the antenna shapes and amount of antenna elements. We observe these properties through the distribution of the Gramian Matrices. Next, we extend the same rationale to channel matrices generated from real channels and we verify that the properties are still valid. In a second part of the research work, we included more than one antenna in the mobile terminals and calculated the outage probability for several antenna configurations and arbitrary number users. We introduce a formulation for mutual information and then we calculate exact results in a case with two users with two antennas in both Base Station (BS) and User Terminals (UT). Since the formulations to the exact derivation for cases with more antennas and users seems to be intricate, we propose a Gaussian approximation solution to simplify the problem. We validated this approximation with Monte Carlo simulations for different signal-to-noise ratiosDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétrica248416/2013-8CNPQCAPE

Parallel Factor-Based Model for Two-Dimensional Direction Estimation

Two-dimensional (2D) Direction-of-Arrivals (DOA) estimation for elevation and azimuth angles assuming noncoherent, mixture of coherent and noncoherent, and coherent sources using extended three parallel uniform linear arrays (ULAs) is proposed. Most of the existing schemes have drawbacks in estimating 2D DOA for multiple narrowband incident sources as follows: use of large number of snapshots, estimation failure problem for elevation and azimuth angles in the range of typical mobile communication, and estimation of coherent sources. Moreover, the DOA estimation for multiple sources requires complex pair-matching methods. The algorithm proposed in this paper is based on first-order data matrix to overcome these problems. The main contributions of the proposed method are as follows: (1) it avoids estimation failure problem using a new antenna configuration and estimates elevation and azimuth angles for coherent sources; (2) it reduces the estimation complexity by constructing Toeplitz data matrices, which are based on a single or few snapshots; (3) it derives parallel factor (PARAFAC) model to avoid pair-matching problems between multiple sources. Simulation results demonstrate the effectiveness of the proposed algorithm

Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results

Near-Field Target Localization for EMVS-MIMO Radar With Arbitrary Configuration

A three-dimensional (3-D) near-field (NF) source localization method is introduced for a bistatic multiple-input multiple-output (MIMO) radar system equipped with arbitrary electromagnetic vector sensors (EMVSs) at both the transmitter and receiver. Firstly, to obtain estimates of the steering matrices of the transmitting array and the receiving array, tensor decomposition is performed on the covariance matrix of the outputs. Then, multiple parameters of targets, including two-dimensional direction-of-departure (2D-DOD), range from transmitter to target (RFTT), along with two-dimensional direction-of-arrival (2D-DOA), range from target to receiver (RFTR), and two-dimensional receive polarization angle (2D-RPA) with respect to receiver can be obtained by exploiting the property of rotation invariance. Subsequently, a linear equation can be constructed to determine all location parameters of NF targets. The proposed method can provide automatically paired multi-parameters without peak search and has low computational complexity. Moreover, the Cramer-Rao bound (CRB) is derived as a performance benchmark for the model under consideration, and the effectiveness of the proposed method is illustrated by a series of numerical simulations

An extension of the RiMAX multipath estimation algorithm for ultra-wideband channel modeling

This work presents an extension of the high-resolution RiMAX multipath estimation algorithm, enabling the analysis of frequency-dependent propagation parameters for ultra-wideband (UWB) channel modeling. Since RiMAX is a narrowband algorithm, it does not account for the frequency-dependency of the radio channel or the environment. As such, the impact of certain materials in which these systems operate can no longer be considered constant with respect to frequency, preventing an accurate estimation of multipath parameters for UWB communication. In order to track both the specular and dense multipath components (SMC and DMC) over frequency, an extension to the RiMAX algorithm was developed that can process UWB measurement data. The advantage of our approach is that geometrical propagation parameters do not appear or disappear from one sub-band onto the next. The UWB-RiMAX algorithm makes it possible to re-evaluate common radio channel parameters for DMC in the wideband scenario, and to extend the well-known deterministic propagation model comprising of SMC alone, towards a more hybrid model containing the stochastic contributions from the DMC's distributed diffuse scattering as well. Our algorithm was tested with synthetic radio channel models in an indoor environment, which show that our algorithm can match up to 99% of the SMC parameters according to the multipath component distance (MCD) metric and that the DMC reverberation time known from the theory of room electromagnetics can be estimated on average with an error margin of less than 2 ns throughout the UWB frequency band. We also present some preliminary results in an indoor environment, which indicate a strong presence of DMC and thus diffuse scattering. The DMC power represents up to 50% of the total measured power for the lower UWB frequencies and reduces to around 30% for the higher UWB frequencies

Channel Prediction for Mobile MIMO Wireless Communication Systems

Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes