74 research outputs found
The Stationary Phase Approximation, Time-Frequency Decomposition and Auditory Processing
The principle of stationary phase (PSP) is re-examined in the context of
linear time-frequency (TF) decomposition using Gaussian, gammatone and
gammachirp filters at uniform, logarithmic and cochlear spacings in frequency.
This necessitates consideration of the use the PSP on non-asymptotic integrals
and leads to the introduction of a test for phase rate dominance. Regions of
the TF plane that pass the test and don't contain stationary phase points
contribute little or nothing to the final output. Analysis values that lie in
these regions can thus be set to zero, i.e. sparsity. In regions of the TF
plane that fail the test or are in the vicinity of stationary phase points,
synthesis is performed in the usual way. A new interpretation of the location
parameters associated with the synthesis filters leads to: (i) a new method for
locating stationary phase points in the TF plane; (ii) a test for phase rate
dominance in that plane. Together this is a TF stationary phase approximation
(TFSFA) for both analysis and synthesis. The stationary phase regions of
several elementary signals are identified theoretically and examples of
reconstruction given. An analysis of the TF phase rate characteristics for the
case of two simultaneous tones predicts and quantifies a form of simultaneous
masking similar to that which characterizes the auditory system.Comment: Submitted to IEEE Trans Signal Processing 14th Aug 201
On adaptive filter structure and performance
SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Pseudo-Zernike Moments Based Sparse Representations for SAR Image Classification
We propose radar image classification via pseudo-Zernike moments based sparse
representations. We exploit invariance properties of pseudo-Zernike moments to
augment redundancy in the sparsity representative dictionary by introducing
auxiliary atoms. We employ complex radar signatures. We prove the validity of
our proposed methods on the publicly available MSTAR dataset
Graph Signal Processing-Based Imaging for Synthetic Aperture Radar
In this paper, we propose graph signal processing based imaging for synthetic
aperture radar. We present a modified version of fused least absolute shrinkage
and selection operator to cater for graph structure of the radar image. We
solve the cost function via alternating direction method of multipliers. Our
method provides improved denoising and resolution enhancing capabilities. It
can also accommodate the compressed sensing framework quite easily.
Experimental results corroborate the validity of our proposed methodology
Latent parameter estimation in fusion networks using separable likelihoods
Multi-sensor state space models underpin fusion applications in networks of
sensors. Estimation of latent parameters in these models has the potential to
provide highly desirable capabilities such as network self-calibration.
Conventional solutions to the problem pose difficulties in scaling with the
number of sensors due to the joint multi-sensor filtering involved when
evaluating the parameter likelihood. In this article, we propose a separable
pseudo-likelihood which is a more accurate approximation compared to a
previously proposed alternative under typical operating conditions. In
addition, we consider using separable likelihoods in the presence of many
objects and ambiguity in associating measurements with objects that originated
them. To this end, we use a state space model with a hypothesis based
parameterisation, and, develop an empirical Bayesian perspective in order to
evaluate separable likelihoods on this model using local filtering. Bayesian
inference with this likelihood is carried out using belief propagation on the
associated pairwise Markov random field. We specify a particle algorithm for
latent parameter estimation in a linear Gaussian state space model and
demonstrate its efficacy for network self-calibration using measurements from
non-cooperative targets in comparison with alternatives.Comment: accepted with minor revisions, IEEE Transactions on Signal and
Information Processing Over Network
- âŠ