New insights into second and fourth-order direction finding for NonCircular sources

International audienceThese last three decades, many second order (SO) and higher order (HO) high resolution (HR) direction finding (DF) methods, such as 2q-MUSIC (q ≥ 1), exploiting the information contained in the SO or HO circular (C) cumulants of the data, have been developed. However, for 2qth-order non-circular (NC) sources such as M-PSK sources with M ≤ 2q, strong gains in performance may be obtained by taking into account the information contained in both 2qth-order C and NC cumulants of the data, giving rise to NC 2qth-order DF methods. Numerous NC DF methods have been developed these last fifteen years but mainly at the SO and under restrictive assumptions on the sources. The purpose of this paper is to give new insights into NC 2q-MUSIC methods for 1 ≤ q ≤ 2 and for arbitrary NC sources

Augmented quaternion ESPRIT-type DOA estimation with a crossed-dipole array

In this paper, a quaternion-based ESPRIT-type algorithm called augmented quaternion ESPRIT (AQ-ESPRIT) is proposed for direction of arrival (DOA) estimation with a co-located crossed-dipole array. Firstly, two quaternion models are constructed by judiciously arranging the observed signals, and then concatenated to form a new AQ model. Secondly, the AQ signal subspace is estimated by applying the quaternionic eigenvalue decomposition to the resultant AQ covariance matrix. Finally, the derived AQ signal subspaces of different subarrays are employed to form the rotational invariance equation, which is then used to obtain the ultimate DOA estimates. Numerical results show that the proposed AQ-ESPRIT has a better performance in low signal-to-noise ratio scenarios

Ein Beitrag zur effizienten Richtungsschätzung mittels Antennenarrays

Sicherlich gibt es nicht den einen Algorithmus zur Schätzung der Einfallsrichtung elektromagnetischer Wellen. Statt dessen existieren Algorithmen, die darauf optimiert sind Hunderte Pfade zu finden, mit uniformen linearen oder kreisförmigen Antennen-Arrays genutzt zu werden oder möglichst schnell zu sein. Die vorliegende Dissertation befasst sich mit letzterer Art. Wir beschränken uns jedoch nicht auf den reinen Algorithmus zur Richtungsschätzung (RS), sondern gehen das Problem in verschiedener Hinsicht an. Die erste Herangehensweise befasst sich mit der Beschreibung der Array-Mannigfaltigkeit (AM). Bisherige Interpolationsverfahren der AM berücksichtigen nicht inhärent Polarisation. Daher wird separat für jede Polarisation einzeln interpoliert. Wir übernehmen den Ansatz, eine diskrete zweidimensionale Fouriertransformation (FT) zur Interpolation zu nutzen. Jedoch verschieben wir das Problem in den Raum der Quaternionen. Dort wenden wir eine zweidimensionale diskrete quaternionische FT an. Somit können beide Polarisationszustände als eine einzige Größe betrachtet werden. Das sich ergebende Signalmodell ist im Wesentlichen kompatibel mit dem herkömmlichen komplexwertigen Modell. Unsere zweite Herangehensweise zielt auf die fundamentale Eignung eines Antennen-Arrays für die RS ab. Zu diesem Zweck nutzen wir die deterministische Cramér-Rao-Schranke (Cramér-Rao Lower Bound, CRLB). Wir leiten drei verschiedene CRLBs ab, die Polarisationszustände entweder gar nicht oder als gewünschte oder störende Parameter betrachten. Darüber hinaus zeigen wir auf, wie Antennen-Arrays schon während der Design-Phase auf RS optimiert werden können. Der eigentliche Algorithmus zur RS stellt die letzte Herangehensweise dar. Mittels einer MUSIC-basierte Kostenfunktion leiten wir effiziente Schätzer ab. Hierfür kommt eine modifizierte Levenberg- bzw. Levenberg-Marquardt-Suche zum Einsatz. Da die eigentliche Kostenfunktion hier nicht angewendet werden kann, ersetzen wir diese durch vier verschiedene Funktionen, die sich lokal ähnlich verhalten. Diese Funktionen beruhen auf einer Linearisierung eines Kroneckerproduktes zweier polarimetrischer Array-Steering-Vektoren. Dabei stellt sich heraus, dass zumindest eine der Funktionen in der Regel zu sehr schneller Konvergenz führt, sodass ein echtzeitfähiger Algorithmus entsteht.It is save to say that there is no such thing as the direction finding (DF) algorithm. Rather, there are algorithms that are tuned to resolve hundreds of paths, algorithms that are designed for uniform linear arrays or uniform circular arrays, and algorithms that strive for efficiency. The doctoral thesis at hand deals with the latter type of algorithms. However, the approach taken does not only incorporate the actual DF algorithm but approaches the problem from different perspectives. The first perspective concerns the description of the array manifold. Current interpolation schemes have no notion of polarization. Hence, the array manifold interpolation is performed separately for each state of polarization. In this thesis, we adopted the idea of interpolation via a 2-D discrete Fourier transform. However, we transform the problem into the quaternionic domain. Here, a 2-D discrete quaternionic Fourier transform is applied. Hence, both states of polarization can be viewed as a single quantity. The resulting interpolation is applied to a signal model which is essentially compatible to conventional complex model. The second perspective in this thesis is to look at the fundamental DF capability of an antenna array. For that, we use the deterministic Cramér-Rao Lower Bound (CRLB). We point out the differences between not considering polarimetric parameters and taking them as desired parameters or nuisance parameters. Such differences lead to three different CRLBs. Moreover, insight is given how a CRLB can be used to optimize an antenna array already during the design process to improve its DF performance. The actual DF algorithm constitutes the third perspective that is considered in this thesis. A MUSIC-based cost function is used to derive efficient estimators. To this end, a modified Levenberg search and Levenberg-Marquardt search are employed. Since the original cost function is not eligible to be used in this framework, we replace it by four different functions that locally show the same behavior. These functions are based on a linearization of Kronecker products of two polarimetric array steering vectors. It turns out that at least one of these functions usually exhibits very fast convergence leading to real-time capable algorithms

On data-selective learning

Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancellation, signal prediction, and stock market. Therefore, reducing the energy consumption of the adaptive filtering algorithms has great importance, particularly in green technologies and in devices using battery. In this thesis, data-selective adaptive filters, in particular the set-membership (SM) adaptive filters, are the tools to reach the goal. There are well known SM adaptive filters in literature. This work introduces new algorithms based on the classical ones in order to improve their performances and reduce the number of required arithmetic operations at the same time. Therefore, firstly, we analyze the robustness of the classical SM adaptive filtering algorithms. Secondly, we extend the SM technique to trinion and quaternion systems. Thirdly, by combining SM filtering and partialupdating, we introduce a new improved set-membership affine projection algorithm with constrained step size to improve its stability behavior. Fourthly, we propose some new least-mean-square (LMS) based and recursive least-squares based adaptive filtering algorithms with low computational complexity for sparse systems. Finally, we derive some feature LMS algorithms to exploit the hidden sparsity in the parameters.Filtros adaptativos são aplicados em diversos aparelhos eletrônicos e de comunicação, como smartphones, fone de ouvido avançados, DSP chips, antenas inteligentes e sistemas de teleconferência. Eles também têm aplicação em várias áreas como identificação de sistemas, equalização de canal, cancelamento de eco, cancelamento de interferência, previsão de sinal e mercado de ações. Desse modo, reduzir o consumo de energia de algoritmos adaptativos tem importância significativa, especialmente em tecnologias verdes e aparelhos que usam bateria. Nesta tese, filtros adaptativos com seleção de dados, em particular filtros adaptativos da família set-membership (SM), são apresentados para cumprir essa missão. No presente trabalho objetivamos apresentar novos algoritmos, baseados nos clássicos, a fim de aperfeiçoar seus desempenhos e, ao mesmo tempo, reduzir o número de operações aritméticas exigidas. Dessa forma, primeiro analisamos a robustez dos filtros adaptativos SM clássicos. Segundo, estendemos o SM aos números trinions e quaternions. Terceiro, foram utilizadas também duas famílias de algoritmos, SM filtering e partial-updating, de uma maneira elegante, visando reduzir energia ao máximo possível e obter um desempenho competitivo em termos de estabilidade. Quarto, a tese propõe novos filtros adaptativos baseado em algoritmos least-mean-square (LMS) e mínimos quadrados recursivos com complexidade computacional baixa para espaços esparsos. Finalmente, derivamos alguns algoritmos feature LMS para explorar a esparsidade escondida nos parâmetros