Wideband multilinear array processing through tensor decomposition

International audienceOur goal is to devise a wideband High-Resolution technique that does not require a priori knowledge of DoA rough estimates, and that is able to exploit multiple spatial invariances.Existing tensor array processing techniques are limited to the narrowband case. On the other hand, wideband Esprit has only been proposed with focusing matrices, requiring a priori DoA knowledge.We resort to the decomposition of tensors built on space, space translation and frequency diversities, and demonstrate the good behavior of the algorithm proposed

Statistical Nested Sensor Array Signal Processing

Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N 2 ) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods

Next generation positioning in 5G

This thesis is a study of the 5G technologies evaluating a specific case of 5G positioning and mapping. Hence, the main purpose of this project is to improve and study a previous work where a 5G positioning and mapping is already done. The purpose of this project is to evaluate and study different techniques of channel modeling in order to achieve a high accuracy detection position in angle and time domain using high frequency antenna arrays. Concretely, study the cases of LOS/NLOS paths in order to improve the estimation accuracy.Esta tesis es un estudio de las tecnologías 5G evaluando un caso específico de posicionamiento y mapeo 5G. De ahí que el objetivo principal de este proyecto sea mejorar y estudiar un trabajo previo donde ya se realiza un posicionamiento y mapeo 5G. El propósito de este proyecto es evaluar y estudiar diferentes técnicas de modelado de canales con el fin de lograr una posición de detección de alta precisión en el dominio del ángulo y el tiempo utilizando arreglos de antenas de alta frecuencia. Concretamente, estudiar los casos de trayectos LOS / NLOS para mejorar la precisión de la estimación.Aquesta tesi és un estudi de les tecnologies 5G que avaluen un cas específic de posicionament i mapatge 5G. Per tant, l'objectiu principal d'aquest projecte és millorar i estudiar un treball previ on ja es realitza un posicionament i mapatge 5G. L'objectiu d'aquest projecte és avaluar i estudiar diferents tècniques de modelatge de canals per tal d'aconseguir una posició de detecció d'alta precisió en el domini de l'angle i del temps mitjançant matrius d'antenes d'alta freqüència. Concretament, estudiar els casos de camins LOS / NLOS per millorar la precisió de l'estimació

Efficient multidimensional wideband parameter estimation for OFDM based joint radar and communication systems

In this paper, we propose a new pre-processing technique for efficient multidimensional wideband parameter estimation. One application is provided by an orthogonal frequency division multiplexing-(OFDM) based joint radar and communication system, which uses SIMO architecture. In this paper, the estimated parameters are given by the range (time delay), the relative velocity, and the direction of arrival (DoA) pairs of the dominant radar targets. Due to the wideband assumption, the received signals on different subcarriers are incoherent and, therefore, cannot fully exploit the frequency diversity of the OFDM waveform. To estimate the parameters jointly and coherently on different subcarriers, we propose an interpolation-based coherent multidimensional parameter estimation framework, where the wideband measurements are transformed into an equivalent narrowband system. Then, narrowband multidimensional parameter estimation algorithms can be applied. In particular, a wideband $R$ -D periodogram is introduced as a benchmark algorithm, and we develop the $R$ -D Wideband Unitary Tensor-ESPRIT algorithm. The simulations show that the proposed coherent parameter estimation method significantly outperforms the direct application of narrowband parameter estimation algorithms to the wideband measurements. If the fractional bandwidth is significant and the SNR is not too low, the estimates provided by the narrowband estimation algorithms can become inconsistent. Moreover, the interpolation order should be chosen according to the SNR regime. In the low SNR regime, interpolation with a lower-order (i.e., linear interpolation) is recommended. For higher SNRs, we propose an interpolation with higher-order polynomials, e.g., fourth-order (cubic splines) or even higher