Cross-ambiquity function domain multipath channel parameter estimation

Cataloged from PDF version of article.A new array signal processing technique is proposed to estimate the direction-of-arrivals (DOAs), time delays, Doppler shifts and amplitudes of a known waveform impinging on an array of antennas from several distinct paths. The proposed technique detects the presence of multipath components by integrating cross-ambiguity functions (CAF) of array outputs, hence, it is called as the cross-ambiguity function direction finding (CAF-DF). The performance of the CAF-DF technique is compared with the space-alternating generalized expectation-maximization (SAGE) and the multiple signal classification (MUSIC) techniques as well as the Cramer-Rao lower bound. The CAF-DF technique is found to be superior in terms of root-mean-squared-error (rMSE) to the SAGE and MUSIC techniques. (C) 2011 Elsevier Inc. All rights reserved

Multipath channel identification by using global optimization in ambiguity function domain

Cataloged from PDF version of article.A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels. (C) 2011 Elsevier B.V. All rights reserved

Detection of sparse targets with structurally perturbed echo

Cataloged from PDF version of article.In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels. © 2013 Elsevier Inc

Magnetic resonance imaging for diagnosis of bipartite patella : usefulness and relationship with symptoms

Purpose: Bipartite patella is a rare developmental variation of the knee cap. We aimed to identify the magnetic resonance imaging (MRI) features of bipartite patella and evaluate the association with clinical symptoms. Material and methods: MRI exams of 61 patients with bipartite variant were evaluated for types of bipartite patella, oedema around the synchondrosis, bipartite fragment height (FH), distance between the fragment and the patella (FPD), and signal characteristics within the synchondrosis. The study was designed with two observers in order to achieve intra- and interobserver compliance. Any other major knee pathologies that can cause pain were also recorded. Results: Of the 61 participants the average age was 40.1 ± 14.3 years, 44 were males, and 17 were females. Fifty-nine of the bipartite fragments were located at the superolateral quadrant of the patella. There was oedema at the bipartite area in 35 patients. Ten of these patients had no major MRI diagnosis other than oedema, and they were classified as the symptomatic group. The age of the patients in the symptomatic group was statistically lower than in the asymptomatic group (p 0.05). High concordance correlation coefficients were observed on measurements Conclusions: MRI of the knee is highly accurate in evaluation of bipartite patella. To our knowledge; a detailed MRI analysis, like in our study, has not previously been performed, and our report is unique in showing that the symptomatic occurrence of bipartite patella is statistically higher in young patients

Equal Improvability: A New Fairness Notion Considering the Long-term Impact

Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Although researchers have proposed various ways of defining group fairness, most of them only focused on the immediate fairness, ignoring the long-term impact of a fair classifier under the dynamic scenario where each individual can improve its feature over time. Such dynamic scenarios happen in real world, e.g., college admission and credit loaning, where each rejected sample makes effort to change its features to get accepted afterwards. In this dynamic setting, the long-term fairness should equalize the samples' feature distribution across different groups after the rejected samples make some effort to improve. In order to promote long-term fairness, we propose a new fairness notion called Equal Improvability (EI), which equalizes the potential acceptance rate of the rejected samples across different groups assuming a bounded level of effort will be spent by each rejected sample. We analyze the properties of EI and its connections with existing fairness notions. To find a classifier that satisfies the EI requirement, we propose and study three different approaches that solve EI-regularized optimization problems. Through experiments on both synthetic and real datasets, we demonstrate that the proposed EI-regularized algorithms encourage us to find a fair classifier in terms of EI. Finally, we provide experimental results on dynamic scenarios which highlight the advantages of our EI metric in achieving the long-term fairness. Codes are available in a GitHub repository, see https://github.com/guldoganozgur/ei_fairness.Comment: Codes are available in a GitHub repository, see https://github.com/guldoganozgur/ei_fairness. ICLR 2023 Poster. 31 pages, 10 figures, 6 table

Multipath Separation-Direction of Arrival (MS-DOA) with Genetic Search Algorithm for HF channels

Cataloged from PDF version of article.Direction-of-Arrival (DOA) defines the estimation of arrival angles of an electromagnetic wave impinging on a set of sensors. For dispersive and time-varying HF channels, where the propagating wave also suffers from the multipath phenomena, estimation of DOA is a very challenging problem. Multipath Separation-Direction of Arrival (MS-DOA), that is developed to estimate both the arrival angles in elevation and azimuth and the incoming signals at the output of the reference antenna with very high accuracy, proves itself as a strong alternative in DOA estimation for HF channels. In MS-DOA, a linear system of equations is formed using the coefficients of the basis vector for the array output vector, the incoming signal vector and the array manifold. The angles of arrival in elevation and azimuth are obtained as the maximizers of the sum of the magnitude squares of the projection of the signal coefficients on the column space of the array manifold. In this study, alternative Genetic Search Algorithms (GA) for the maximizers of the projection sum are investigated using simulated and experimental ionospheric channel data. It is observed that GA combined with MS-DOA is a powerful alternative in online DOA estimation and can be further developed according to the channel characteristics of a specific HF link. (C) 2009 COSPAR. Published by Elsevier Ltd. All rights reserve

Comparison of the CAF-DF and sage algorithms in multipath channel parameter estimation

In this paper, performance of the recently proposed Cross Ambiguity Function - Direction Finding (CAF-DF) technique is compared with the Space Alternating Generalized Expectation Maximization (SAGE) technique. The CAF-DF, iteratively estimates direction of arrival (DOA), time-delay, Doppler shift and amplitude corresponding to each impinging signal onto an antenna array by utilizing the cross ambiguity function. On synthetic signals, based on Monte Carlo trials, performances of the algoritms are tested in terms of root Mean Squared Error (rMSE) at different Signal-to-Noise Ratios (SNR). Cramer-Rao lower bound is included for statistical comparisons. Simulation results indicate the superior performance of the CAF-DF technique over SAGE technique for low and medium SNR values. © 2008 IEEE

Particle swarm optimization based channel identification in cross-ambiguity domain

In this paper, a new array signal processing technique by using particle swarm optimization (PSO) is proposed to identify multipath channel parameters. The proposed technique provides estimates to the channel parameters by finding a global minimum of an optimization problem. Since the optimization problem is formulated in the cross-ambiguity function (CAF) domain of the transmitted signal and the received array outputs, the proposed technique is called as PSO-CAF. The performance of the PSO-CAF is compared with the space alternating generalized expectation maximization (SAGE) technique and with another recently proposed PSO based technique for various SNR values. Simulation results indicate the superior performance of the PSO-CAF technique over mentioned techniques for all SNR values. ©2010 IEEE