• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.234-240
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• 2011

In this paper, the performances of Cram$\acute{e}$r-Rao Lower Bound (CRLB) with Frequency Difference of Arrival (FDOA) measurements for Unmanned Aerial Vehicle (UAV) tracking are investigated. We focus on two cases: the influence on CRLB with FDOA measurements collected by time, and random distribution of Ground Stations (GSs). We derived the performance by gauging the size of CRLB through Complementary Cumulative Distribution Function (CCDF). From the simulation results, broader distribution of GSs and FDOA measurements by longer time bring about better performance.

• ETRI Journal
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• v.34 no.4
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• pp.511-517
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• 2012

The best linear unbiased estimator (BLUE) is most suitable for practical application and can be determined with knowledge of only the first and second moments of the probability density function. Although the BLUE is an existing algorithm, it is still largely unexplored and has not yet been applied to channel estimation in amplify and forward (AF)-based wireless relay networks (WRNs). In this paper, a BLUE-based algorithm is proposed to estimate the overall channel impulse response between the source and destination of AF strategy-based WRNs. Theoretical mean square error (MSE) performance for the BLUE is derived to show the accuracy of the proposed channel estimation algorithm. In addition, the Cram$\acute{e}$r-Rao lower bound (CRLB) is derived to validate the MSE performance. The proposed BLUE channel estimation algorithm approaches the CRLB as the length of the training sequence and number of relays increases. Further, the BLUE performs better than the linear minimum MSE estimator due to the minimum variance characteristic exhibited by the BLUE, which happens to be a function of signal-to-noise ratio.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.373-379
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• 2019

If two signals with the same single-tone frequency and differing phases impinge simultaneously on an antenna at slightly differing angles, then a large error in the angle estimation might occur if the phase difference is either $0^{\circ}$ or $180^{\circ}$. This phenomenon might arise with an airborne fire-control radar, which has a relatively small bandwidth, for a low-flying target over the sea or terrain surface. In this paper, we show that the $Cram{\acute{e}}r$-Rao lower bound for such a target can be significantly lowered with the use of two frequencies.

• ETRI Journal
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• v.34 no.4
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• pp.641-644
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• 2012

The problem of sinusoidal parameter estimation at two channels with common frequency in white Gaussian noise is addressed. By making use of the linear prediction property, an iterative linear least squares (LLS) algorithm for accurate frequency estimation is devised. The remaining parameters are then determined according to the LLS fit with the use of the frequency estimate. It is proven that the variance of the frequency estimate achieves Cram$\acute{e}$r-Rao lower bound at sufficiently small noise conditions.

• ETRI Journal
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• v.34 no.5
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• pp.684-689
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• 2012

A mode-singular-value-decomposition (SVD) maximum likelihood (ML) estimation procedure is proposed for the source localization problem under an additive measurement error model. In a practical situation, the noise variance is usually unknown. In this paper, we propose an algorithm that does not require the noise covariance matrix as a priori knowledge. In the proposed method, the weight is derived by the inverse of the noise magnitude square in the ML criterion. The performance of the proposed method outperforms that of the existing methods and approximates the Taylor-series ML and Cram$\acute{e}$r-Rao lower bound.