141 results found
Luo K, Manikas A, 2017, Joint Transmitter–Receiver Optimization in Multitarget MIMO Radar, IEEE Transactions on Signal Processing, Vol: 65, Pages: 6292-6302, ISSN: 1053-587X
Wu J, Watson R, Bolla R, et al., 2017, Guest Editorial on Green Communications, Computing, and Systems, IEEE Systems Journal, Vol: 11, Issue:2, Pages: 546-550, ISSN: 1932-8184
Kamil YI, Manikas A, 2017, Multisource Spatiotemporal Tracking Using Sparse Large Aperture Arrays, IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, Vol: 53, Pages: 837-853, ISSN: 0018-9251
Sridhar V, Manikas A, 2017, Target Tracking with a Flexible UAV Cluster Array, IEEE GLOBECOM 2016, Publisher: IEEE
Unmanned aerial vehicle (UAV) cluster applications,for tasks such as target localisation and tracking, are required tocollect and utilise the data received on “flexible” sensor arrays,where the sensors, i.e. UAVs in this scenario, have time-variantpositions. In this paper, using a parametric channel model, a UAVcluster mobility model and a kinematic model of the targets, anextended Kalman based state space model is proposed that tracksthe unknown UAV positions and target parameters snapshot bysnapshot. Simulation studies illustrating the tracking capabilitiesof the proposed technique have been presented.
Fang Z, Manikas A, 2017, DOA and Range Estimation of Multiple Sources Under the Wideband Assumption, IEEE GLOBECOM 2016, Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 0895-1195
In this paper, two novel channel parameter estimationalgorithms are proposed under the “wideband assumption,”where a wavefront varies significantly when traversing throughthe sensors of the array. The first covariance-based approachutilizes the cross-covariance matrix between two subvectors of thereceived signal vector and the singular value decomposition to reconstructthe parameter-dependent signal subspace. Meanwhile,the second reference-based approach employs the rotation of thearray reference point so that the estimation techniques underthe “narrowband assumption” are readily applicable. Throughcomputer simulation studies, the two proposed approaches areshown to successfully estimate the channel parameters under thewideband assumption with outstanding accuracy in terms of theestimation root mean squared error
Sridhar V, Gabillard T, Manikas A, 2016, Spatiotemporal-MIMO Channel Estimator and Beamformer for 5G, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 15, Pages: 8025-8038, ISSN: 1536-1276
Manikas A, Sridhar V, Kamil Y, 2016, Array of sensors: A spatiotemporal-state-space model for target trajectory tracking, 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Publisher: IEEE, ISSN: 2151-870X
In this paper, with the objective of tracking the trajectoryof multiple mobile targets, a novel spatiotemporal-state-space modelis introduced for an array of sensors distributed in space. Underthe wideband assumption, the proposed model incorporates the arraygeometry in conjunction with crucial target parameters namely (i) ranges,(ii) directions, (iii) velocities and (iv) associated Doppler effects. Computersimulation studies show some representative examples where the proposedmodel is utilised to track the locations of sources in space with a veryhigh accuracy.
Gabillard T, Sridhar V, Manikas A, 2015, Comparative Study of 2D Grid Antenna Array Geometries for Massive Array Systems, IEEE GLOBECOM 2015, Publisher: IEEE
In upcoming trends of wireless communications, such as massive MIMO, the number of antennas at the transmitter(TX) and receiver (RX) are expected to increase dramatically, aiming to provide a substantial improvement in system performance and spectral efficiency. However, an increase in the number of antennas also results in an increase in hardware, computational complexity and energy dissipation of the MIMO system. Therefore, the antenna array geometry plays a crucial role in the overall system performance. This paper is concerned with planar antenna array geometries with emphasis given to the family of 2D "grid" arrays and presents an insight into the relation between the array geometry and various performance metrics, such as detection, resolution and data-rate maximization, that may be used in different applications.
Mak K, Manikas A, 2015, A Superresolution Wide Null Beamformer for Undersampled Signal Reconstruction in SIMO SAR, IEEE Journal of Selected Topics in Signal Processing, Vol: 9, Pages: 1548-1559, ISSN: 1932-4553
Commin H, Luo K, Manikas A, 2015, Arrayed MIMO Radar: Multi-target Parameter Estimation for Beamforming, Beamforming Sensor Signal Processing for Defence Applications, Editors: Manikas, Publisher: Imperial College Press, Pages: 119-158, ISBN: 978-1-78326-276-2
Sridhar V, Willerton M, Manikas A, 2015, Towed Arrays: Channel Estimation, Tracking and Beamforming, Beamforming Sensor Signal Processing for Defence Applications, Editors: Manikas, Publisher: Imperial Colege Press, Pages: 159-187, ISBN: 978-1-78326-274-8
Willerton M, Venieris E, Manikas A, 2015, Array Uncertainties and Auto-calibration, Beamforming - Sensor Signal Processing for Defence Applications, Editors: Manikas, Publisher: Imperial College Press, Pages: 221-262, ISBN: 978-1-78326-274-8
Zhuang J, Manikas A, 2015, Robust Beamforming to Pointing Errors, Beamforming - Sensor Signal Processing for Defence Applications, Editors: Manikas, Publisher: Imperial College Press, Pages: 263-286, ISBN: 978-1-78326-274-8
Mak K, Manikas A, 2015, Digital Beamforming for Synthetic Aperture Radar, Beamforming Sensor Signal Processing for Defence Applications, Editors: Manikas, Publisher: Imperial College Press, Pages: 63-117, ISBN: 978-1-78326-275-5
Manikas A, 2015, Beamforming - Sensor Signal Processing and Defence Applications, Publisher: Imperial College Press - Communications and Signal Processing Series, ISBN: 978-1-78326-274-8
This book is concerned with adaptive sensor array processing and in particular with superresolution beamformers and their applications to sonar and radar. In the book both narrowband and wideband beamformers will be presented as well as space-only and spatiotemporal beamformers, which may operate in the presence of clutters and jammers. Furthermore, transmitter (Tx), receiver (Rx) and both Tx/Rx (MIMO) beamformers will be considered and their role in radar and sonar designs will be discussed. Design, integration and auto-calibration approaches incorporating off-the-shelf components will be also presented.
Mak K, Manikas A, 2015, Beamforming for Wake Wave Detection and Estimation — An Overview —, Beamforming - Sensor Signal Processing for Defence Applications, Publisher: Imperial College Press, Pages: 159-187, ISBN: 978-1-78326-275-5
Venieris E, Manikas A, 2014, Preprocessing algorithm for source localisation in a multipath environment, Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th, Publisher: IEEE, ISSN: 1551-2282
Several methods have been developed which allow the estimation of the location of an existing source with considerable accuracy in the absence of multipaths. However, if, in addition to the Line-of-Sight (LOS) path, non-LOS (NLOS) paths are also present, then all existing localisation algorithms dramatically fail to estimate the location of the source. In this paper, a passive array processing algorithm is proposed, which, if used prior to a localisation approach, suppresses all the multipath contributions in the received signal except for that of the LOS path. The performance of the proposed algorithm is evaluated through computer simulation studies.
Akindoyin A, Willerton M, Manikas A, 2014, Localization and array shape estimation using software defined radio array testbed, Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th, Publisher: IEEE, Pages: 189-192
Efstathopoulos G, Manikas A, 2013, Existence and Uniqueness of Hyperhelical Array Manifold Curves, IEEE Journal on Selected Topics in Signal Processing, Vol: Special Issue on Differential Geometry in Signal Processing
Significant open issues in array processing have been successfully investigated based on the concept of the array manifold and taking advantage of our understanding of its physical geometrical shape in an N-dimensional complex space - using differential geometry. Array ambiguities, arrayuncertainties, array design and performance characterisation are just some of the areas that have benefited from this approach.Unfortunately, the investigation of the shape of the array manifold itself for most but a few array geometries has been proven to be extremely complex and restrictive - especially in the numberof geometric properties that can actually be calculated. However, special array geometries have been identified, for which the arraymanifold curve assumes a specific “hyperhelical” shape. This is one of the most important manifold shapes and its properties greatly simplifies its geometric analysis and, consequently, the analysis of the associated array os sensors. Hence, the goal of this paper is twofold: to provide the necessary and sufficient conditions for the existence of array manifold curves of hyperhelical shape; and to determine which array geometries can actually give rise to manifold curves of thisshape
Manikas A, Commin H, Sleiman A, 2013, Array Manifold Curves in C^N and their Complex Cartan Matrix, IEEE Journal of Selected Topics in Signal Processing, Vol: 7, Pages: 670-680, ISSN: 1932-4553
The differential geometry of array manifold curves has been investigated extensively in the literature, leading to numerous applications. However, the existing differential geometric framework restricts the Cartan matrix to be purely real and so the vectors of the moving frame U(s) are found to be orthogonal only in the wide sense (i.e. only the real part of their inner product is equal to zero). Imaginary components are then accounted for separately using the concept of the inclination angleof the manifold. The purpose of this paper is therefore to present an alternativetheoretical framework which allows the manifold curve in CN to be characterised in a more convenient and direct manner. A continuously differentiable strictly orthonormal basis is established and forms a platform for deriving a generalised complexCartan matrix with similar properties to those established under the previous framework. Concepts such as the radius of circular approximation, the manifold curve radii vector and the frame matrix are also revisited and rederived under this new framework.
Luo K, Manikas A, 2013, Superresolution Multitarget Parameter Estimation in MIMO Radar, IEEE Transactions on Geoscience and Remote Sensing, Vol: 51, Pages: 3683-3693, ISSN: 0196-2892
Manikas A, Zhuang J, 2013, Interference cancellation beamforming robust to pointing errors, IET Signal Processing, Vol: 7, Pages: 120-127, ISSN: 1751-9675
Manikas A, Kamil YI, Willerton M, 2012, Source Localization Using Sparse Large Aperture Arrays, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 60, Pages: 6617-6629, ISSN: 1053-587X
Zhou Y, Adachi F, Wang X, et al., 2012, Broadband Wireless Communications for High Speed Vehicles, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: VOL. 30, Pages: 673-674, ISSN: 0733-8716
Willerton M, Banavar M, Zhang X, et al., 2012, SEQUENTIAL WIRELESS SENSOR NETWORK DISCOVERY USING WIDE APERTURE ARRAY SIGNAL PROCESSING, 20th European Signal Processing Conference (EUSIPCO), Publisher: IEEE COMPUTER SOC, Pages: 2278-2282, ISSN: 2076-1465
Willerton M, Manikas A, 2012, Auto-Calibration of Sparse Arrays of Sensors, IEEE Transactions on Signal Processing
Manikas T, Thomas P, 2012, Editorial: Multi-sensor signal processing for defence: Detection, localisation & classification, IET Signal Processing, Vol: 6, Pages: 393-393, ISSN: 1751-9675
Talantzis F, Pnevmatikakis A, Constantinides AG, 2011, AUDIO-VISUAL PERSON TRACKING:A Practical Approach, ISBN: 978-1-84816-581-6
This book deals with the creation of the algorithmic backbone that enables a computer to perceive humans in a monitored space. This is performed using the same signals that humans process, i.e., audio and video. Computers reproduce the same type of perception using sensors and algorithms in order to detect and track multiple interacting humans, by way of multiple cues, like bodies, faces or speech. This application domain is challenging, because audio and visual signals are cluttered by both background and foreground objects. First, particle filtering is established as the framework for tracking. Then, audio, visual and also audio-visual tracking systems are separately explained. Each modality is analyzed, starting with sensor configuration, detection for tracker initialization and the trackers themselves. Techniques to fuse the modalities are then considered. Instead of offering a monolithic approach to the tracking problem, this book also focuses on implementation by providing MATLAB code for every presented component. This way, the reader can connect every concept with corresponding code. Finally, the applications of the various tracking systems in different domains are studied.
Manikas A, 2011, Extended Array Manifolds: Functions of Array Manifolds (vol 59, pg 3272, 2011), IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 59, Pages: 4501-4501, ISSN: 1053-587X
Efstathopoulos G, Manikas A, 2011, Extended Array Manifolds: Functions of Array Manifolds, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 59, Pages: 3272-3287, ISSN: 1053-587X
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