John Soraghan

Dr. John Soraghan


Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 March 2019 in IEEE Transactions on Aerospace and Electronic Systems
Verified
Journal Article
Open access
APAdriano Rosario Persico
Adriano Rosario Persico
Christos IlioudisChristos Ilioudis
Christos Ilioudis
Carmine ClementeCarmine Clemente
Carmine Clemente
JSJohn Soraghan
John Soraghan
Nowadays the identification of ballistic missile warheads in a cloud of decoys and debris is essential for defence systems in order to optimize the use of ammunition resources, avoiding to run out of all the available interceptors in vain. This paper introduces a novel solution for the classification of ballistic targets based on the computation of the inverse Radon transform of the target signatures, represented by a high resolution range profile frame acquired within an entire period of the main rotation of the target. Namely, the precession for warheads and the tumbling for decoys are taken into account. Following, the pseudo-Zernike moments of the resulting transformation are evaluated as the final feature vector for the classifier. The extracted features guarantee robustness against target's dimensions and rotation velocity, and the initial phase of the target's motion. The classification results on simulated data are shown for different polarizations of the electromagnetic radar waveform and for various operational conditions, confirming the the validity of the algorithm

2 April 2019 in IEEE Transactions on Aerospace and Electronic Systems
Verified
Journal Article
Closed access
Christos IlioudisChristos Ilioudis
Christos Ilioudis
Carmine ClementeCarmine Clemente
Carmine Clemente
IPIan K. Proudler
Ian K. Proudler
JSJohn Soraghan
John Soraghan
In this work a generalised signal model is presented to accommodate both narrowband and wideband signals in a multi-input multi-output (MIMO) sensor system scenarios. The derived model is then used to define a MIMO ambiguity function (AF) based on the Kullback-Leibler divergence (KLD). Moreover, the proposed formulation is parametrised using the signal and channel correlation matrices to account for different waveform and sensor placement designs, thereby allowing a flexible modelling approach. A comparison between the proposed definition and the more conventional approach of summing the squared matched filter outputs is presented for different sensors and waveforms configurations.

3 December 2021 in IEEE Geoscience and Remote Sensing Letters
Verified
Journal Article
Closed access
Pallotta LucaPallotta Luca
Pallotta Luca
GGGaetano Giunta
Gaetano Giunta
Carmine ClementeCarmine Clemente
Carmine Clemente
JSJohn Soraghan
John Soraghan
This letter extends the constrained Least Squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS.

1 May 2015 in The Journal of the Acoustical Society of America
Verified
Journal Article
Open access
PDF file
AAAlan W. Archer-Boyd
Alan W. Archer-Boyd
WWWilliam M Whitmer
William M Whitmer
WBW. Owen Brimijoin
W. Owen Brimijoin
JSJohn Soraghan
John Soraghan
Sound sources at the same angle in front or behind a two-microphone array (e.g., bilateral hearing aids) produce the same time delay and two estimates for the direction of arrival: A front-back confusion. The auditory system can resolve this issue using head movements. To resolve front-back confusion for hearing-aid algorithms, head movement was measured using an inertial sensor. Successive time-delay estimates between the microphones are shifted clockwise and counterclockwise by the head movement between estimates and aggregated in two histograms. The histogram with the largest peak after multiple estimates predicted the correct hemifield for the source, eliminating the front-back confusions.

1 September 2012
Verified
Conference Paper
Closed access
Jaime ZabalzaJaime Zabalza
Jaime Zabalza
Jinchang RenJinchang Ren
Jinchang Ren
Carmine ClementeCarmine Clemente
Carmine Clemente
GCGaetano Di Caterina
Gaetano Di Caterina
JSJohn Soraghan
John Soraghan
Support Vector Machine (SVM) is a very powerful tool for signal prediction including classification and regression. With Texas Instruments TMS320C6713 DSK, an embedded SVM is implemented, where a user friendly interface is provided via peripherals like the DIPs and LEDs. The C6713 processor in combination with the SDRAM block memory can solve the complex computation that SVM requires. Also a Real-Time utilisation of the device from Matlab environment is demonstrated. An exciting application framework is finally obtained, from which some conclusions related to the implementation and final usage are derived.

15 April 2022 in Multimedia Tools and Applications
Verified
Journal Article
Closed access
PDF file
DRDeepak Rout
Deepak Rout
BSBadri Narayan Subudhi
Badri Narayan Subudhi
TVT. Veerakumar
T. Veerakumar
SCSantanu Chaudhury
Santanu Chaudhury
JSJohn Soraghan
John Soraghan