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Proceedings ArticleDOI

Efficient Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent and Landing

TLDR
In this article, a robust and low complexity scheme to estimate and track carrier frequency from the received signals at the Earth end is proposed, which employs a hierarchical arrangement of convex linear prediction cells that is dynamically adapted to respond to channel conditions.
Abstract
In the Mars rover missions the signals transmitted back to Earth travel under low SNR conditions in highly non-stationary channels [1, 2]. During the entry, descent and landing phase (EDL), the spacecraft high dynamics yields severe Doppler effects. We propose a robust and low complexity scheme to estimate and track carrier frequency from the received signals at the Earth end. The method employs a hierarchical arrangement of convex linear prediction cells that is dynamically adapted to respond to the channel conditions. The adaptive combination is able to outperform the best individual estimator in the set, leading to a universal scheme for frequency estimation and tracking. In order to compensate the lag error effect, we explore an efficient forward and backward aggregation scheme that improves considerably the frequency RMS error as compared to the original method [3].

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Citations
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Journal ArticleDOI

Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

TL;DR: Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived and the resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment.
Proceedings ArticleDOI

Combination of adaptive filters with coefficients feedback

TL;DR: A more natural way of accelerating the convergence to steady-state is proposed, using a cyclic feedback of the overall weights to all component filters, instead of a unidirectional conditional transfer.
Journal ArticleDOI

Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent, and Landing

TL;DR: It is shown that retrieval of frequency content by a fast Fourier transform-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels.
Proceedings ArticleDOI

Incremental-cooperative strategies in combination of adaptive filters

TL;DR: Two new algorithms are derived from the new topology based on incremental strategies that rearranged the standard convexly combined parallel-independent filters into a series-cooperative configuration without changing the computational complexity.
Proceedings ArticleDOI

Incremental combination of RLS and LMS adaptive filters in nonstationary scenarios

TL;DR: This work shows that the incremental combination is extended to account for AFs with different adaptive rules, and that the new structure is meansquare universal in terms of the combining parameter, particularly in nonstationary scenarios with highly-correlated signals.
References
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Book

Fundamentals of adaptive filtering

Ali H. Sayed
TL;DR: This paper presents a meta-anatomy of Adaptive Filters, a system of filters and algorithms that automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing these filters.
Journal ArticleDOI

Single tone parameter estimation from discrete-time observations

TL;DR: Estimation of the parameters of a single-frequency complex tone from a finite number of noisy discrete-time observations is discussed and appropriate Cramer-Rao bounds and maximum-likelihood estimation algorithms are derived.
Journal ArticleDOI

Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood

TL;DR: In this paper, the frequency estimation performance of the forward-backward linear prediction (FBLP) method was improved for short data records and low signal-to-noise ratio (SNR) by using information about the rank M of the signal correlation matrix.
Journal ArticleDOI

Universal prediction

TL;DR: Both the probabilistic setting and the deterministic setting of the universal prediction problem are described with emphasis on the analogy and the differences between results in the two settings.
Journal ArticleDOI

Time series modelling and maximum entropy

TL;DR: In this article, the authors briefly review the principles of maximum entropy spectral analysis and the closely related problem of autoregressive time series modelling and discuss the important aspect of model identification.
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