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 RT, Bolla R, et al., 2017, Guest Editorial Special Issue on Green Communications, Computing, and Systems, IEEE Systems Journal, Vol: 11, 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
Venieris E, Manikas A, Near-far field multipath spatial-temporal localisation, IEEE International Conference on Communications 2017, Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 0536-1486
In this paper, a passive array processing algorithm isproposed for localising the near-far field multipaths of the desiredsignal in the presence of co-channel interference. By expressingthe unknown path delay as a function of the path’s range,the proposed spatiotemporal localisation algorithm estimates thelocations of all the multipath reflectors of the desired signalsource using a subspace-type cost function. The performance ofthe proposed algorithm is evaluated through computer simulationstudies.
Fang Z, Manikas A, Arrayed space optical communications: localization of the ground station, IEEE International Conference on Communications (ICC), Publisher: IEEE
In this paper, a novel ground station localizationalgorithm is proposed for space optical communications using ar-ray processing and a set of celestial objects of known locations inthe global coordinate system. First, the ground station estimatesthe directions of this set of celestial objects relative to its localcoordinate system using the sunlight reflected by these celestialobjects. Then, the ranges of the celestial objects and the locationand orientation of the ground station are estimated by solvingsystems of nonlinear and linear equations. The performance ofthe proposed approach is assessed through computer simulationstudies. It is shown to estimate the location and orientation ofthe ground station successfully with excellent accuracy.
Gabillard T, Sridhar V, Manikas A, Capacity loss and antenna array geometry, IEEE International Conference on Communications (ICC), Publisher: IEEE
The impact of antenna array geometry on it’s ability to migrate interference, and hence the channel capacity, is a topic that is seldom studied and is crucial to future systems that will employ large arrays. In this paper, for the worst-case scenario where interferers are located spatially close to the desired users, the “capacity loss” is defined and expressed as a function of array geometry and propaganda environment. Based on the analytical results, simulation studies of the capacity loss are presented for different array geometries and various key insights on antenna array design are highlighted.
Liu Q, Manikas A, Experimental Comparison of Localisation Techniques in the Presence of Array Uncertainties, European Conference on Antennas and Propagation (EuCAP)
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
Fang Z, Manikas A, 2016, DOA and Range Estimation of Multiple Sources under the Wideband Assumption, GLOBECOM 2016 - 2016 IEEE Global Communications Conference, Publisher: IEEE
Sridhar V, Manikas A, 2016, Target Tracking with a Flexible UAV Cluster Array, 2016 IEEE Globecom Workshops (GC Wkshps), Publisher: IEEE
Manikas A, Sridhar V, Kamil YI, 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
Gabillard T, Sridhar V, Akindoyin A, et al., 2015, Comparative Study of 2D Grid Antenna Array Geometries for Massive Array Systems, 2015 IEEE Globecom Workshops (GC Wkshps), Publisher: IEEE
Mak K, Manikas A, 2015, A Superresolution Wide Null Beamformer forUndersampled Signal Reconstruction in SIMO SAR, IEEE Journal of Selected Topics in Signal Processing, Vol: 9, ISSN: 1932-4553
With Single-Input Single-Output (SISO) SARsystems, employing a single transmitter and receiver beam, thereexists a high resolution, wide swath contradiction. However, byusing multiple receiver beams and employing array processingtechniques, this contradiction can be overcome, allowing greaterflexibility and a wider range of application requirements tobe met. In this paper the use of Single-Input Multiple-Output(SIMO) SAR systems for overcoming this contradiction is ofinterest, and a novel beamformer is proposed for processing inthe cross-range direction. In order to fully describe the system,the array manifold vector is utilised, which is a key concept inthe design of the beamformer. In particular, this beamformeris a superresolution beamformer capable of forming wide nullsusing subspace based approaches and allows the suppressionof ambiguities in multiple sets of received undersampled SARdata in the cross-range direction and reconstruction of theDoppler spectrum to form a single unambiguous set of SAR data.Compared to the existing reconstruction algorithm, only a singleweighting vector is required for a block of ambiguous Dopplerfrequencies compared to a weight vector required for eachambiguous Doppler frequency. The capabilities of the proposedbeamformer are shown to give an improved performance inambiguity suppression via computer simulation studies in arepresentative maritime environment.
Commin H, Luo K, Manikas A, 2015, Arrayed MIMO Radar: Multi-target Parameter Estimation for Beamforming, Beamforming, Publisher: IMPERIAL COLLEGE PRESS, Pages: 119-158, ISBN: 9781783262748
, 2015, FRONT MATTER, Publisher: IMPERIAL COLLEGE PRESS, ISBN: 9781783262748
Mak K, Manikas A, 2015, Beamforming for Wake Wave Detection and Estimation — An Overview —, Beamforming, Publisher: IMPERIAL COLLEGE PRESS, Pages: 159-187, ISBN: 9781783262748
Sridhar V, Willerton M, Manikas A, 2015, Towed Arrays: Channel Estimation, Tracking and Beamforming, Beamforming, Publisher: IMPERIAL COLLEGE PRESS, Pages: 189-219, ISBN: 9781783262748
Willerton M, Venieris E, Manikas A, 2015, Array Uncertainties and Auto-calibration, Beamforming, Publisher: IMPERIAL COLLEGE PRESS, Pages: 221-262, ISBN: 9781783262748
Zhuang J, Manikas A, 2015, Robust Beamforming to Pointing Errors, Beamforming, Publisher: IMPERIAL COLLEGE PRESS, Pages: 263-286, ISBN: 9781783262748
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
Venieris E, Manikas A, 2014, Preprocessing algorithm for source localisation in a multipath environment, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), Publisher: IEEE
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
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