18,639 research outputs found
Velocity Dealiased Spectral Estimators of Range Migrating Targets using a Single Low-PRF Wideband Waveform
Wideband radars are promising systems that may provide numerous advantages, like simultaneous detection of slow and fast moving targets, high range-velocity resolution classification, and electronic countermeasures. Unfortunately, classical processing algorithms are challenged by the range-migration phenomenon that occurs then for fast moving targets. We
propose a new approach where the range migration is used rather as an asset to retrieve information about target velocitiesand, subsequently, to obtain a velocity dealiased mode. More specifically three new complex spectral estimators are devised in case of a single low-PRF (pulse repetition frequency) wideband waveform. The new estimation schemes enable one to decrease the
level of sidelobes that arise at ambiguous velocities and, thus, to enhance the discrimination capability of the radar. Synthetic data and experimental data are used to assess the performance of the proposed estimators
Multipath Parameter Estimation from OFDM Signals in Mobile Channels
We study multipath parameter estimation from orthogonal frequency division
multiplex signals transmitted over doubly dispersive mobile radio channels. We
are interested in cases where the transmission is long enough to suffer time
selectivity, but short enough such that the time variation can be accurately
modeled as depending only on per-tap linear phase variations due to Doppler
effects. We therefore concentrate on the estimation of the complex gain, delay
and Doppler offset of each tap of the multipath channel impulse response. We
show that the frequency domain channel coefficients for an entire packet can be
expressed as the superimposition of two-dimensional complex sinusoids. The
maximum likelihood estimate requires solution of a multidimensional non-linear
least squares problem, which is computationally infeasible in practice. We
therefore propose a low complexity suboptimal solution based on iterative
successive and parallel cancellation. First, initial delay/Doppler estimates
are obtained via successive cancellation. These estimates are then refined
using an iterative parallel cancellation procedure. We demonstrate via Monte
Carlo simulations that the root mean squared error statistics of our estimator
are very close to the Cramer-Rao lower bound of a single two-dimensional
sinusoid in Gaussian noise.Comment: Submitted to IEEE Transactions on Wireless Communications (26 pages,
9 figures and 3 tables
Network Tomography: Identifiability and Fourier Domain Estimation
The statistical problem for network tomography is to infer the distribution
of , with mutually independent components, from a measurement model
, where is a given binary matrix representing the
routing topology of a network under consideration. The challenge is that the
dimension of is much larger than that of and thus the
problem is often called ill-posed. This paper studies some statistical aspects
of network tomography. We first address the identifiability issue and prove
that the distribution is identifiable up to a shift parameter
under mild conditions. We then use a mixture model of characteristic functions
to derive a fast algorithm for estimating the distribution of
based on the General method of Moments. Through extensive model simulation and
real Internet trace driven simulation, the proposed approach is shown to be
favorable comparing to previous methods using simple discretization for
inferring link delays in a heterogeneous network.Comment: 21 page
A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data
In financial markets, low prices are generally associated with high
volatilities and vice-versa, this well known stylized fact usually being
referred to as leverage effect. We propose a local volatility model, given by a
stochastic differential equation with piecewise constant coefficients, which
accounts of leverage and mean-reversion effects in the dynamics of the prices.
This model exhibits a regime switch in the dynamics accordingly to a certain
threshold. It can be seen as a continuous-time version of the Self-Exciting
Threshold Autoregressive (SETAR) model. We propose an estimation procedure for
the volatility and drift coefficients as well as for the threshold level.
Parameters estimated on the daily prices of 348 stocks of NYSE and S\&P 500, on
different time windows, show consistent empirical evidence for leverageeffects.
Mean-reversion effects are also detected, most markedly in crisis periods
Comparison of SAGE and classical multi-antenna algorithms for multipath mitigation in real-world environment
The performance of the Space Alternating Generalized Expectation Maximisation (SAGE) algorithm for multipath mitigation is assessed in this paper. Numerical simulations have already proven the potential of SAGE in navigation context, but practical aspects of the implementation of such a technique in a GNSS receiver are the topic for further investigation. In this paper, we will present the first results of SAGE implementation in a real world environmen
Statistics of the MLE and Approximate Upper and Lower Bounds - Part 1: Application to TOA Estimation
In nonlinear deterministic parameter estimation, the maximum likelihood
estimator (MLE) is unable to attain the Cramer-Rao lower bound at low and
medium signal-to-noise ratios (SNR) due the threshold and ambiguity phenomena.
In order to evaluate the achieved mean-squared-error (MSE) at those SNR levels,
we propose new MSE approximations (MSEA) and an approximate upper bound by
using the method of interval estimation (MIE). The mean and the distribution of
the MLE are approximated as well. The MIE consists in splitting the a priori
domain of the unknown parameter into intervals and computing the statistics of
the estimator in each interval. Also, we derive an approximate lower bound
(ALB) based on the Taylor series expansion of noise and an ALB family by
employing the binary detection principle. The accurateness of the proposed
MSEAs and the tightness of the derived approximate bounds are validated by
considering the example of time-of-arrival estimation
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