Decentralized Estimation over Orthogonal Multiple-access Fading Channels in Wireless Sensor Networks - Optimal and Suboptimal Estimators

Optimal and suboptimal decentralized estimators in wireless sensor networks (WSNs) over orthogonal multiple-access fading channels are studied in this paper. Considering multiple-bit quantization before digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate the channel coefficients and exploits the estimated CSI in an optimal way. To reduce the computational complexity, we propose suboptimal estimators. These estimators exploit both signal and data level redundant information to improve the estimation performance. The proposed MLEs reduce to traditional fusion based or diversity based estimators when communications or observations are perfect. By introducing a general message function, the proposed estimators can be applied when various analog or digital transmission schemes are used. The simulations show that the estimators using digital communications with multiple-bit quantization outperform the estimator using analog-and-forwarding transmission in fading channels. When considering the total bandwidth and energy constraints, the MLE using multiple-bit quantization is superior to that using binary quantization at medium and high observation signal-to-noise ratio levels

Parameter Estimation for Polynomial Phase Signals With a Fast and Robust Algorithm

International audiencePolynomial phase signals belong to a wide class of nonstationary signals used for modeling and engineering applications. In this paper, we take benefits of some advances in robust estimation in order to propose a new algorithm for estimating the parameters of a polynomial phase signal. This algorithm has the advantages to be fast and its structure is robust to the shape of the noise

A Computationally Efficient algorithm to estimate the Parameters of a Two-Dimensional Chirp Model with the product term

Chirp signal models and their generalizations have been used to model many natural and man-made phenomena in signal processing and time series literature. In recent times, several methods have been proposed for parameter estimation of these models. These methods however are either statistically sub-optimal or computationally burdensome, specially for two dimensional (2D) chirp models. In this paper, we consider the problem of parameter estimation of 2D chirp models and propose a computationally efficient estimator and establish asymptotic theoretical properties of the proposed estimators. And the proposed estimators are observed to have the same rates of convergence as the least squares estimators (LSEs). Furthermore, the proposed estimators of chirp rate parameters are shown to be asymptotically optimal. Extensive and detailed numerical simulations are conducted, which support theoretical results of the proposed estimators

Gravitational waves from Sco X-1: A comparison of search methods and prospects for detection with advanced detectors

The low-mass X-ray binary Scorpius X-1 (Sco X-1) is potentially the most luminous source of continuous gravitational-wave radiation for interferometers such as LIGO and Virgo. For low-mass X-ray binaries this radiation would be sustained by active accretion of matter from its binary companion. With the Advanced Detector Era fast approaching, work is underway to develop an array of robust tools for maximizing the science and detection potential of Sco X-1. We describe the plans and progress of a project designed to compare the numerous independent search algorithms currently available. We employ a mock-data challenge in which the search pipelines are tested for their relative proficiencies in parameter estimation, computational efficiency, robust- ness, and most importantly, search sensitivity. The mock-data challenge data contains an ensemble of 50 Scorpius X-1 (Sco X-1) type signals, simulated within a frequency band of 50-1500 Hz. Simulated detector noise was generated assuming the expected best strain sensitivity of Advanced LIGO and Advanced VIRGO ($4 \times 10^{-24}$ Hz$^{-1/2}$). A distribution of signal amplitudes was then chosen so as to allow a useful comparison of search methodologies. A factor of 2 in strain separates the quietest detected signal, at $6.8 \times 10^{-26}$ strain, from the torque-balance limit at a spin frequency of 300 Hz, although this limit could range from $1.2 \times 10^{-25}$ (25 Hz) to $2.2 \times 10^{-26}$ (750 Hz) depending on the unknown frequency of Sco X-1. With future improvements to the search algorithms and using advanced detector data, our expectations for probing below the theoretical torque-balance strain limit are optimistic.Comment: 33 pages, 11 figure

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals (ICI) rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs, showing the estimation accuracy improvement in terms of the mean squared error (MSE) by up to 23%. Furthermore, the highest improvement is achieved for low SNRs values, when many of the existing methods fail.Scopu

Advanced interferometric techniques for high resolution bathymetry

International audienceCurrent high-resolution side scan and multibeam sonars produce very large data sets. However, conventional interferometry-based bathymetry algorithms underestimate the potential information of such soundings, generally because they use small baselines to avoid phase ambiguity. Moreover, these algorithms limit the triangulation capabilities of multibeam echosounders to the detection of one sample per beam, i.e., the zero-phase instant. In this paper we argue that the correlation between signals plays a very important role in the exploration of a remotely observed scene. In the case of multibeam sonars, capabilities can be improved by using the interferometric signal as a continuous quantity. This allows consideration of many more useful soundings per beam and enriches understanding of the environment. To this end, continuous interferometry detection is compared here, from a statistical perspective, first with conventional interferometry-based algorithms and then with high-resolution methods, such as the Multiple Signal Classification (MUSIC) algorithm. We demonstrate that a well-designed interferometry algorithm based on a coherence error model and an optimal array configuration permits a reduction in the number of beam formings (and therefore the computational cost) and an improvement in target detection (such as ship mooring cables or masts). A possible interferometry processing algorithm based on the complex correlation between received signals is tested on both sidescan sonars and multibeam echosounders and shows promising results for detection of small in-water targets