A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space-Time Adaptive Processing

We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments

Tensor-based subspace tracking

Für verschiedene Anwendungen auf dem Gebiet der digitalen Signalverarbeitung sind die Bestimmung der Unterräume sowie deren Tracking, zum Besispiel für die Signalparameterschätzung, die Datenkomprimierung, Radar und die Bildverarbeitung, erforderlich. Eine der vielversprechendsten Techniken zur Schätzung der Signalunterräume basiert auf dem Konzept der Singulärwertzerlegung (Singular Value Decomposition, SVD). In letzter Zeit wurde für mehrdimensionale Daten die SVD höherer Ordnung (Higher-Order SVD, HOSVD) verwendet, um verbesserte Schätzungen des Unterraums im Vergleich zum SVD-Konzept zu schaffen. Darüber hinaus kann durch Verwendung der HOSVD die Schätzung des Unterraums für die Parameterschätzung in einem harmonischen Wiedergewinnungsproblem mit mehrdimensionaler Struktur in den Daten, durchgeführt werden. Sind jedoch die multidimensionalen Daten zeitvariant, werden adaptive Algorithmen, die auf der Tensoralgebra zum Tracking des Unterraums beruhen, benötigt. Durch den Einsatz dieser Algorithmen können auch die Signalparameter wie die Richtung (direction of arrival, DOA) bestimmt werden. Außerdem, wenn die Anzahl der Messungen gering ist oder die Quellen stark korreliert sind, kann dann durch die Anwendung der Vorwärts-Rückwärts-Durchschnittsbestimmung (Forward Backward Averaging, FBA) die Leistungsfähigkeit weiter verbessert werden. In dieser Arbeit berücksichtigen wir FBA und schlagen den erweiterten FBA-PAST-Algorithmus, der auf dem Tensor-Based Subspace Tracking via Kronecker structured projections (TeTraKron) basiert, vor. Wir zeigen, dass FBA zu einer verbesserten Genauigkeit des Unterraum-Tracking und einem niedrigeren Rechenaufwand durch reellwertige Rechenoperationen führt. Außerdem bewerten wir die Leistungsfähigkeit der Parameterschätzungsalgorithmen in vielen nicht-stationären Szenarien, in denen die Unterräume durch Verwendung des Unterraum-Tracking geschätzt werden. Darüber hinaus erweitern wir den adaptiven ESPRIT-Algorithmus zu einem allgemeineren Fall, in dem die Unterarrays nicht notwendigerweise eine maximale Überlappung haben. Weiterhin entwickeln wir eine adaptive Version für Unitary ESPRIT sowie 2-D Unitary ESPRIT. Im Vergleich zu der direkten Kombination des PAST- Algorithmus mit Unitary ESPRIT oder 2-D Unitary ESPRIT, erreichen die vorgeschlagenen adaptiven Algorithmen die gleiche Leistung mit einer geringeren mathematischen Komplexität.Abstract (engl.): For different applications in the field of digital signal processing, subspaces estimation and tracking have been required, e.g., signal parameter estimation, data compressing, radar and imaging processing. One of the most fruitful techniques in estimating the signal subspaces is based on the singular value decomposition (SVD) concept. Recently, for multidimensional data, Higher-Order SVD (HOSVD) can be used to provide improved estimates of the subspace compared to the SVD concept. Moreover, the subspace estimates obtained by employing HOSVD can be used for parameter estimation in a harmonic retrieval problem where a multidimensional structure is inherent in the data. However, when the multidimensional data are time-variant, adaptive subspace tracking schemes based on tensor algebra are in demand. By employing the tensor-based subspace tracking algorithms, the signal parameters like DOA can be tracked as well. Moreover, if the number of observations is small or the sources are highly correlated, incorporating Forward Backward Averaging (FBA) can further improve the performance of tracking. In this work, based on the tensor-based subspace tracking via Kronecker structured projections (TeTraKron) framework, we include FBA and propose the Extended FBA-PAST algorithm. We show that incorporating FBA leads to an improved accuracy of the subspace tracking and a lower computational complexity due to the fact that only real-valued processing is involved. Moreover, we evaluate the performances of the parameter estimation schemes in a variety of non-stationary scenarios where the subspace estimates are obtained by employing the subspace tracking algorithms. Furthermore, we extend the adaptive ESPRIT algorithm to a general case where the subarrays are not necessarily maximum overlapping. In addition, we develop an adaptive version of Unitary ESPRIT as well as 2-D Unitary ESPRIT. Compared to the direct combination of the PAST algorithm and Unitary ESPRIT or 2-D Unitary ESPRIT, the proposed adaptive schemes achieve the same performance with a lower mathematical complexity.Ilmenau, Techn. Univ., Masterarbeit, 201

Décompositions en éléments sonores et applications musicales

National audienceIn this paper is presented the DESAM project which was divided in two parts. The first one was devoted to the theoretical and experimental study of parametric and non-parametric techniques for decomposing audio signals into sound elements. The second part focused on some musical applications of these decompositions. Most aspects that have been considered in this project have led to the proposal of new methods which have been grouped together into the so-called DESAM Toolbox, a set of Matlab® functions dedicated to the estimation of widely used spectral models for music signals. Although those models can be used in Music Information Retrieval (MIR) tasks, the core functions of the toolbox do not focus on any specific application. It is rather aimed at providing a range of state-of-the-art signal processe soumission à Traitement du signalDans cet article sont présentés de manière synthétique les résultats du projet ANR DE-SAM (Décompositions en Éléments Sonores et Applications Musicales). Ce projet comportait deux parties, la première portant sur des avancées théoriques de techniques de décompositions de signaux audionumériques et la seconde traitant d'applications musicales de ces décompo-sitions. La plupart des aspects abordés dans le projet ont donné lieu à de nouvelles méthodes et algorithmes qui sont regroupés au sein d'une boîte à outils, la DESAM Toolbox. Celle-ci rassemble un ensemble de fonctions Matlab® dédiées à l'estimation de modèles spectraux très utilisés pour les signaux musicaux. Les méthodes étudiées dans ce projet peuvent bien sûr être utiles pour la recherche automatique d'informations dans les signaux musicaux, mais elles constituent avant tout une collection d'outils récents pour décomposer les signaux selon dif-férents modèles, avec pour résultat des représentations mi-niveau variées, pouvant être utiles dans d'autres domaines d'application

Channel estimation in massive MIMO systems

Last years were characterized by a great demand for high data throughput, good quality and spectral efficiency in wireless communication systems. Consequently, a revolution in cellular networks has been set in motion towards to 5G. Massive multiple-input multiple-output (MIMO) is one of the new concepts in 5G and the idea is to scale up the known MIMO systems in unprecedented proportions, by deploying hundreds of antennas at base stations. Although, perfect channel knowledge is crucial in these systems for user and data stream separation in order to cancel interference. The most common way to estimate the channel is based on pilots. However, problems such as interference and pilot contamination (PC) can arise due to the multiplicity of channels in the wireless link. Therefore, it is crucial to define techniques for channel estimation that together with pilot contamination mitigation allow best system performance and at same time low complexity. This work introduces a low-complexity channel estimation technique based on Zadoff-Chu training sequences. In addition, different approaches were studied towards pilot contamination mitigation and low complexity schemes, with resort to iterative channel estimation methods, semi-blind subspace tracking techniques and matrix inversion substitutes. System performance simulations were performed for the several proposed techniques in order to identify the best tradeoff between complexity, spectral efficiency and system performance

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS

Sub pixel analysis and processing of sensor data for mobile target intelligence information and verification

This dissertation introduces a novel process to study and analyze sensor data in order to obtain information pertaining to mobile targets at the sub-pixel level. The process design is modular in nature and utilizes a set of algorithmic tools for change detection, target extraction and analysis, super-pixel processing and target refinement. The scope of this investigation is confined to a staring sensor that records data of sub-pixel vehicles traveling horizontally across the ground. Statistical models of the targets and background are developed with noise and jitter effects. Threshold Change Detection, Duration Change Detection and Fast Adaptive Power Iteration (FAPI) Detection techniques are the three methods used for target detection. The PolyFit and FermiFit are two tools developed and employed for target analysis, which allows for flexible processing. Tunable parameters in the detection methods, along with filters for false alarms, show the adaptability of the procedures. Super-pixel processing tools are designed, and Refinement Through Tracking (RTT) techniques are investigated as post-processing refinement options. The process is tested on simulated datasets, and validated with sensor datasets obtained from RP Flight Systems, Inc