• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.232-236
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• 2001

In this paper, we consider the problem of estimation of average radar cross section (RCS) for Swerling 1 fluctuation model, based on the maximum likelihood (ML) estimation method. In a mathematical development we take into account the event that target strength is lower than detection threshold, or the target is not detected. Our ML estimation for the SWR uses the score function that is the joint probability-pdf of the events and random variables. The solution to the ML estimation reduces to an expression in the from of a contraction mapping. The computational efficiency of the contraction mapping theorem is significant in computing the ML estimation as compared with other root-finding algorithms fur most radar tracking conditions.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.337-354
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• 2002

This paper derives three estimation methods for the between group variance component for serially correlated random model. To compare their estimation capability, three designs having different degree of unbalancedness are considered. The so-called empirical quantile dispersion graphs(EQDGs) used to compare estimation methods as well as designs. The proposed conditional ANOVA estimation is robust for design unbalancedness, however, ML estimation is preferred to the conditional AOVA and REML estimation regardless of design unbalancedness and correlation coefficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1063-1075
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• 2012

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

• ETRI Journal
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• v.40 no.2
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• pp.218-226
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• 2018

Multiuser detection (MUD) and channel estimation techniques in space-division multiple-access aided orthogonal frequency-division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean-squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML-MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.56-60
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• 2006

Based on the fact that audio signals in the time domain have the generalized Gaussian distribution. an optimal parameter estimation of the ML (maximum likelihood) test based audio watermark decoder. which leads to the minimal bit error rate, is Proposed. Its superiority of performance over the existing estimation and the conventional correlation based decoder is demonstrated experimentally.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1807-1810
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• 2002

This paper presents a technique for avoid- ing indefiniteness in Maximum Likelihood (ML) criteria for Direction-of-Arrival (DOA) finding using a sensor ar- ray with arbitrary configuration. The ML criterion has singular points in the solution space where the criterion becomes indefinite. Solutions fly iterative techniques for ML bearing estimation may oscillate because of numerical instability which occurs due to the indefiniteness, when bearings more than one approach to the identical value. The oscillation makes the condition for terminating iterations complex. This paper proposes a technique for avoiding the indefiniteness in ML criteria.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.85-95
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• 1995

The error intensity models for the ML estimation of a signal parameter have been developed in a companion paper [1]. While the methods described in [1] are applicable to any estimation problem with continuous parameters, our main application in this paper is the time delay estimation, and comparisons among the models derived in [1] (i.e. LC, LM, and ALM models)have been made. We first consider the case where only additive Gaussian noise is involved, and then the shot noise environment where coherent impulsive noise is also involved in addition to the Gaussian noise. We compare the models in terms of the probability of error, MSE(Mean Squared Error), and the computational complexity, which are the most important performance criteria in the analysis of parameter estimation. In conclusion, the ALM model turned out to be the most adequate model of all from the viewpoints of the criteria mentioned above.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.886-893
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• 2017

Recently, for detection of low-RCS targets, bistatic radar and multistatic radar have been widely employed. In this paper, we present the process of deriving the received signal modeling of the bistatic MIMO radar system and deals with the performance analysis of applying the bistatic signal to the ML arrival angle estimation algorithm. In case of the ML algorithm, as the number of the targets increases, azimuth search dimension for DOA estimation also increases, which implies that the ML algorithm for multiple targets is computationally very intensive. To solve this problem a closed-form expression of estimation error is presented for performance analysis of the algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.882-888
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• 2006

Multiuser detection can significantly increase system capacity and improve service quality compared with the existing matched filter. In this paper, we introduce an method which efficiently calculates the maximum likelihood (ML) metric based on the gradient search (GS). The ML detection needs user powers as well as their spreading codes. A method is also proposed that allows us to detect data bits with the estimation of user powers when they are unknown. Computer simulation shows that the proposed method can nearly achieve the same performance as the GS with perfectly hewn user powers.

• The Journal of the Acoustical Society of Korea
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• v.13 no.4
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• pp.20-31
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• 1994

The maximum likelihood(ML) estimation has excellent accuracy for frequency estimation of multiple sinusoids, but the maximum likelihood function requires much loss owing to the high nonlinearity. This paper presents a simplified maximum likelihood estimation, in order to improve the nonlinearity of the maximum likelihood estimation for frequencies of sinusoids in signals. This method is applied to the frequency estimation of sinusoidal signals corrupted by white or colored measurement noise. Monte-carlo simulations are conducted for the comparison of ML method with the best MFBLP method, in terms of sampled mean, root mean square and relative bias. The power spectral density and the position of frequency in unit circle are appeared in figures.