S
Stephen D. Howard
Researcher at Defence Science and Technology Organization
Publications - 47
Citations - 595
Stephen D. Howard is an academic researcher from Defence Science and Technology Organization. The author has contributed to research in topics: Tree (data structure) & Radar. The author has an hindex of 15, co-authored 47 publications receiving 549 citations. Previous affiliations of Stephen D. Howard include Defence Science and Technology Organisation.
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Journal ArticleDOI
Fast Essentially Maximum Likelihood Decoding of the Golden Code
TL;DR: This paper presents a simple algorithm with quadratic complexity for decoding the Golden code that can be employed by mobile terminals with either one or two receive antennas, that is resilient to near singularity of the channel matrix, and that gives essentially maximum likelihood (ML) performance.
Journal ArticleDOI
A Simple Signal Processing Architecture for Instantaneous Radar Polarimetry
TL;DR: A new radar primitive is described that enables instantaneous radar polarimetry at essentially no increase in signal processing complexity and avoids the information loss inherent in single-channel matched filters.
Journal ArticleDOI
Fast Optimal Decoding of Multiplexed Orthogonal Designs by Conditional Optimization
TL;DR: A new code is presented that tests commonly accepted design principles and for which decoding by conditional optimization is both fast and ML, and shows that it is possible to give up on cubic shaping without compromising code performance or decoding complexity.
Journal ArticleDOI
Geometry of the Welch Bounds
TL;DR: In this article, a geometric perspective involving Grammian and frame operators is used to derive the entire family of Welch bounds, which unifies a number of observations made regarding tightness of the bounds and their connections to symmetric k-tensors, tight frames, homogeneous polynomials, and t-designs.
Journal ArticleDOI
Bayesian Analysis of Interference Cancellation for Alamouti Multiplexing
TL;DR: This correspondence provides new theoretical insight into different algorithms for interference cancellation through a Bayesian analysis that expresses performance as a function of signal-to-noise ratio (SNR) in terms of the ldquoanglesrdquo between different space-time coded data streams.