Edwin Vargas, Samuel Pinilla, Jorge Bacca, H. Arguello
{"title":"Robust Formulation for Solving Underdetermined Random Linear System of Equations Via Admm","authors":"Edwin Vargas, Samuel Pinilla, Jorge Bacca, H. Arguello","doi":"10.1109/SSP.2018.8450805","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450805","url":null,"abstract":"Compressive sensing (CS) is a well-known theory which allowshighly efficient data acquisition schemes and has received much attention in diverse applications such as medical imaging, and hyperspectral imaging. Many algorithms have been proposed in the literature for solving the ill-posed inverse problem present in CS based on the sparsity of the signal of interest. Traditionally, these algorithms minimize a cost function that is a combination between an ℓ<inf>1</inf> and ℓ<inf>p</inf> norms with 1 < p < ∞. Specifically, the non-smooth ℓ<inf>1</inf>-norm has been used as a convex relaxation of the ℓ<inf>0</inf> pseudo-norm to promote sparsity, and the ℓ<inf>p</inf>-norm has been commonly used as the data fidelity term. However, in many applications, the measurements can be very noisy, and using an ℓ<inf>p</inf>-norm as a data fit term becomes useless to reduce the effect of the noise in order to obtain the desired signal. Therefore, this paper proposes a new algorithm that minimizes a combination of an ℓ<inf>1</inf> and ℓ<inf>∞</inf> norms to solve the inverse problem present in CS. In this case, the ℓ<inf>∞</inf> works as the data fidelity term which leads to a more robust algorithm against the noise. The proposed method requires less number of iterations to solve the CS inverse problem compared with the state-of-the-art-algorithms.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129264787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sparsity-Enabled Step Width Adaption For Linearized Bregman Based Algorithms","authors":"M. Lunglmayr, M. Huemer","doi":"10.1109/SSP.2018.8450706","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450706","url":null,"abstract":"Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hardand software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116044708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Limitations of Decision Based Pile-Up Correction Algorithms","authors":"C. McLean, Michael Pauley, J. Manton","doi":"10.1109/SSP.2018.8450835","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450835","url":null,"abstract":"Nuclear spectroscopy attempts to infer elemental composition by estimating the energy distribution of X-ray/gamma-ray photons. Performance at high count rates is limited by pulse pile-up. Numerous approaches attempt to estimate the properties of individual photons in the time domain. This work provides an asymptotic description of pile-up for the Neyman Pearson detector, showing that any algorithm that makes decisions regarding individual pulses will eventually suffer and be limited by pile-up.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117098241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Target Tracking Using a Distributed Particle-Pda Filter With Sparsity-Promoting Likelihood Consensus","authors":"Rene Repp, P. Rajmic, Florian Meyer, F. Hlawatsch","doi":"10.1109/SSP.2018.8450815","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450815","url":null,"abstract":"We propose a distributed particle-based probabilistic data association filter (PDAF) for target tracking in the presence of clutter and missed detections. The proposed PDAF employs a new “sparsity-promoting” likelihood consensus that uses the orthogonal matching pursuit for a sparse approximation of the local likelihood functions. Simulation results demonstrate that, compared to the conventional likelihood consensus based on least-squares approximation, large savings in intersensor communication can be obtained without compromising the tracking performance.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115401109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Misspecified Bayesian Cramér-Rao Bound for Sparse Bayesian","authors":"Milutin Pajovic","doi":"10.1109/SSP.2018.8450780","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450780","url":null,"abstract":"We consider a misspecified Bayesian Cramér-Raobound (MBCRB), justified in a scenario where the assumed data model is different from the true generative model. As an example of this scenario, we study a popular sparse Bayesian learning (SBL) algorithm where the assumed data model, different from the true model, is constructed so as to facilitate a computationally feasible inference of a sparse signal within the Bayesian framework. Formulating the SBL as a Bayesian inference with a misspecified data model, we derive a lower bound on the mean square error (MSE) corresponding to the estimated sparse signal. The simulation study validates the derived bound and shows that the SBL performance approaches the MBCRB at very high signal-to-noise ratios.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114909790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Blind Sparse Recovery Using Imperfect Sensor Networks","authors":"P. Jung, Martin Genzel","doi":"10.1109/SSP.2018.8450719","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450719","url":null,"abstract":"This work investigates blind aggregation of structured highdimensional data, using a network of imperfect wireless sensor nodes which noncoherently communicate to a central fusion center or mobile data collector. In our setup, there is an unknown subset (of size ${k}$) of all $M$ registered autonomous transceiver nodes that sporadically wake up and simultaneously transmit their sensor readings through a shared channel. This procedure does particularly not involve a training phase that would allow for apriori channel predictions. In order to improve the resolvability in this noncoherent random access channel, the nodes perform an additional randomization of their signals. Since the transmission is usually imperfect, e.g., caused by low-quality hardware and unknown channel fading coefficients, the receiver measures a superposition of non-linearly distorted signals with unknown weights. Such a recovery task can be translated into a bilinear compressed sensing problem with rank-one measurements. We present a theoretical result for the Gaussian case which shows that $m = mathcal {O}(sklog (2nM/sk))$ measurements are sufficient to guarantee recovery of an $s$-sparse vector in $mathbb {R}^{n}$. Moreover, our error bounds explicitly reflect the impact of the underlying non-linearities. The performance of our approach is also evaluated numerically for a random network generated by a compressible fading and node activity model.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"96 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126962267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multiple Target Tracking With Uncertain Sensor State Applied To Autonomous Vehicle Data","authors":"Markus Fröhle, Karl Granström, H. Wymeersch","doi":"10.1109/SSP.2018.8450842","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450842","url":null,"abstract":"In a conventional multitarget tracking (MTT) scenario, the sensor position is assumed known. When the MTT sensor, e.g., an automotive radar, is mounted to a moving vehicle with uncertain state, it becomes necessary to relax this assumption and model the unknown sensor position explicitly. In this paper, we compare a recently proposed filter that models the unknown sensor state [1], to two versions of the track-oriented marginal MeMBer/Poisson (TOMB/P) filter: the first does not model the sensor state uncertainty; the second models it approximately by artificially increasing the measurement variance. The results, using real measurement data, show that in terms of tracking performance, the proposed filter can outperform TOMB/P without sensor state uncertainty, and is comparable to TOMB/P with increased variance.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127084701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Acoustic Echo Cancellation During Doubletalk Using Convolutive Blind Source Separation of Signals Having Temporal Dependence","authors":"T. Moon, J. Gunther","doi":"10.1109/SSP.2018.8450792","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450792","url":null,"abstract":"This paper describes a new algorithm for acoustic echo cancellation during doubletalk or, more precisely, acoustic echo separation, based on blind source separation (BSS) of convolutively mixed signals. The signal model assumes independence between sources, but temporal dependence between time samples, specifically that the vector signals have first-order Markov dependence. The source separation is done using a maximum likelihood approach. The source separation does not always provide separation, because of too many degrees of freedom on the separation. However, when applied to the acoustic echo cancellation problem, the constraints of the echo system neatly solve this problem. An example shows that acoustic echoes can be cleanly separated during doubletalk.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126063902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Single Satellite Geolocation Solution of an RF Emitter Using a Constrained Unscented Kalman Filter","authors":"P. Ellis, F. Dowla","doi":"10.1109/SSP.2018.8450834","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450834","url":null,"abstract":"A real-time geolocation solution of a radio frequency (RF) emitter is presented using a constrained Unscented Kalman Filter (cUKF). Samples are processed on a single low earth orbit (LEO) satellite by a cUKF that imposes constraints by projecting the sigma points into the feasible solution space. The emitter is transmitting an RF signal from an unknown l0-cation with a known center frequency. The ease of imposing constraints within the UKF framework for single LEO satellite RF geolocation is demonstrated and results show reduced convergence time, high resiliency to noise, sub-kilometer geolocation accuracies, and the maintenance of stability. An accurate and real-time single satellite solution can be a valuable asset, as it could greatly reduce cost and computation time of existing multi-satellite systems. This paper shows the feasibility of such a solution and provides a framework to approach more realistic link budget, satellite GPS inaccuracies, and oscillator drift scenarios.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127280241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Projection-Based Rao-Blackwellized Particle Filter to Estimate Parameters in Conditionally Conjugate State-Space Models","authors":"Milan Papez","doi":"10.1109/SSP.2018.8450730","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450730","url":null,"abstract":"Particle filters constitute today a well-established class of techniques for state filtering in non-linear state-space models. However, online estimation of static parameters under the same framework represents a difficult problem. The solution can be found to some extent within a category of state-space models allowing us to perform parameter estimation in an analytically tractable manner, while still considering non-linearities in data evolution equations. Nevertheless, the well-known particle path degeneracy problem complicates the computation of the statistics that are required to estimate the parameters. The present paper proposes a simple and efficient method which is experimentally shown to suffer less from this issue.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122442529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}