Improved Maximum Likelihood Estimation Research Articles

Maximizing the Resolution of SAR ADC With Multisegmented DAC Using Improved MLE Calibration

Successive approximation register analog to digital converters (SAR ADCs) offer an attractive energy-efficient solution but its signal-to-noise-distortion-ratio (SNDR) is degraded due to its sensitivity to parasitic capacitance and capacitors’ mismatch. In this article, we present an improved maximum likelihood estimation (MLE) algorithm, which offers a more robust estimator for statistical-based calibration technique that can be used across different resolutions of SAR ADC. The result has demonstrated that our proposed algorithm has further improved the SNDR up to 5.86 dB for a 12–20-bit SAR ADC with multisegmented digital-to-analog converter (DAC) compared to state-of-the-art algorithms.

Improved Maximum Likelihood Estimation for the Weibull Distribution Under Length-Biased Sampling

Improved Maximum Likelihood Estimation for the Weibull Distribution Under Length-Biased Sampling

Improved maximum likelihood estimation of the shape-scale family based on the generalized progressive hybrid censoring scheme

In parametric estimates, the maximum likelihood estimation method is the most popular method widely used in the social sciences and psychology, although it is biased in situations where sample sizes are small or the data are heavily censored. Therefore, the main objective of this research is to improve this estimation method using the Runge–Kutta technique. The improved method was applied to derive the estimators of the shape scale family parameters and compare them with Bayesian estimators based on the informative and kernel priors, via Monte Carlo simulation. The simulation results showed that the improved maximum likelihood estimation method is highly efficient and outperforms the Bayesian method for different sample sizes. Finally, from a future perspective, the proposed model could be important for analyzing real data sets including data on COVID-19 deaths in Egypt, for potential comparative studies with other countries.

Iterative Doppler Frequency Offset Estimation in Satellite High-Mobility Communications

Satellite communication systems are able to provide diverse services for ground terminals in ubiquitous global coverage, which play a vital role in high-mobility communication environments. Existing technologies developed primarily for satellite communications cannot be readily applied to satellite high-mobility communication scenarios, since high Doppler frequency offset caused by the fast movement of wireless terminals, and low signal-to-noise ratio (SNR) circumstances caused by limited link budgets in satellites incur more difficulty of the synchronization, especially for short burst transmission. To solve such a problem in satellite high-mobility communications, we propose a novel method named GP-MASO-MLE, which consists of a coarse estimation algorithm based on the Gaussian process (GP) model and Newton-Raphson method, and a fine correction algorithm based on the improved maximum likelihood estimation (MLE) jointly with turbo decoding iterations. Simulation results show that the proposed algorithm can approach to the bit error rate (BER) performance bound of ideal Doppler frequency offset correction within 0.1 dB, which can be well applied in code-aided (CA) satellite high-mobility communication systems for its good performance. In addition, the computational complexity of the proposed algorithm is lower than other traditional turbo synchronization algorithms.

Improvement of OFDM Timing Synchronization Algorithm Based on Cyclic Prefix

Abstract Based on the classical maximum likelihood (ML) estimation algorithm, the performance of the multipath fading channel becomes poor. In order to improve the multipath performance of the maximum likelihood estimation algorithm, the set correlation algorithm is used to calculate the delay point in the multipath channel. Timing estimation is performed using a portion of the cyclic prefix (CP) that is not contaminated by the multipath channel. The simulation results show that the improved maximum likelihood estimation algorithm improves the timing accuracy under multipath channels.

Improved Maximum Likelihood Estimation for Optimal Phase History Retrieval of Distributed Scatterers in InSAR Stacks

Distributed scatterer (DS) decorrelation poses a challenge to multibaseline SAR interferometry. To overcome this challenge, the SqueeSAR retrieves an optimal phase time-series using a maximum likelihood estimation (MLE) method, which has been commonly used due to its remarkable effect. Unfortunately, however, the MLE’s performance is compromised for various reasons, such as inaccurate statistically homogeneous pixels (SHPs) and the bias in the estimator used. In this paper, we present an approach aiming to improve the MLE’s performance. The proposed approach includes the employment of the Kullback-Leibler divergence to realize more accurate SHP selection and the use of the second kind statistical estimator to mitigate the coherence bias. The performance of the conventional MLE is significantly improved by the proposed approach, making it close to its optimal performance. The experimental results on both simulated and real TerraSAR-X data demonstrate the improvements of the proposed approach with respect to the conventional MLE.

The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can’t meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS.

Reliability Modelling of CNC Machine Tools Based on the Improved Maximum Likelihood Estimation Method

The existing standard reliability models for computerized numerical control (CNC) machine tools are not satisfactory and they fall short of predicting failure rates or lifetime of key functional parts of CNC machine tools. This is attributed to two reasons: the small sample size of failure data and a large truncated ratio of the censored failure data. Improved correction method (ICM), maximum likelihood estimation (MLE), and empirical maximum likelihood estimation (EMLE) are presented and compared with each other in this study. In order to improve the shortage of reliability models developed by the traditional methods, an improved maximum likelihood estimation method (IMLE) is proposed through enlarging censored failure data. Moreover, the correction factors of mean ratio to extend censored time are designed, by which the censored failure data can be close to the true time between failures (TBF). Furthermore, a solution method of correction factors considering amount of calculation is proposed to meet the requirements of calculation precision. Finally, verification by the orthogonal experiment is simulated to verify the proposed model. The verifying test results show that the proposed method can be applied in reliability modelling for not only CNC machine tools but also the key functional parts of CNC machine tools.

Improved Maximum Likelihood Estimation of Heston Model and Pricing Efficiency Test: Hong Kong Hang Seng Index Option

This paper selects improved maximum likelihood method to conduct parameter estimation of Heston model, and results show that the share option pricing performance of Hang Seng Index is better and pricing error of at-the-money options is the smallest. By comparing parameter estimation of samples in different intervals, it has been found that parameter estimated results of two-year market data are obviously inferior to estimated effect of one-year data.

Grid-Based Improved Maximum Likelihood Estimation for Dynamic Localization of Mobile Robots

The dynamic localization is a kind of technology by which the mobile robot tries to localize the position by itself. According to the dynamic localization failure of mobile robots in indoor network blind areas, an autonomous-dynamic localization system which dynamically chooses beacon node and establishes grids is proposed in this paper. This method applies received signal strength indication (RSSI) for distance measurement. Furthermore, the proposed grid-based improved maximum likelihood estimation (GIMLE) fulfills the localization. Finally, the localization error correction is implemented by Kalman filter. The approach combines the classical Kalman filter with the other localization algorithms. The purpose is to smooth and optimize the results of the algorithms, in order to improve the localization accuracy. In particular, in network blind spots, the Kalman filter provides better performance than the other algorithms listed in the paper. Experimental results show the accuracy, adaptivity, and robustness of the dynamic self-localization of mobile robots.

An improved ML estimation of frequency and time offset in FDD-LTE initial cell search process

As the traditional frequency and time offset estimation method is complex and sensitive to the frequency offset, in this paper, the process of the initial cell search is described, the reason why the M-segment partial correlation bringing down the risk of incorrect detection of time offset is analyzed. Based on the time offset detected by M-segment partial correlation and the result of ML (Maximum Likelihood) estimation which is proved by Jan-Jaap, a method improving the success probability of initial cell search in the low SNR condition is proposed. In the simulation environment, the performance of the proposed method is compared with the traditional method, proving the proposed method not only less sensitive to frequency offset, but also effective.

Improved ML Channel Estimation for Uplink MC-CDMA Systems in Closely Spaced Multipath Channels

In order to attain near-single user performance in uplink multicarrier code- division multiple-access (MC-CDMA) systems, multiuser detection (MUD) methods may be employed which rely on simultaneous estimation of the channel frequency responses of multiple users. Pilot symbol assisted (PSA) channel estimation is needed in fast fading channels and it can be performed either by applying maximum likelihood (ML) criterion or minimum mean-squared error (MMSE) criterion. The performance of ML estimation technique degrades significantly in the case of fractionally spaced (FS) multipath channels where dominant paths are closely spaced with respect to the time resolution of the system. In such situation, the number of effective paths (which contribute more towards signal power) becomes considerably less than the actual number of multipaths at low and moderate SNR values. We propose an improved ML estimation method which considers only effective paths during the estimation process. The proposed method performs nearly identical to the MMSE estimation method and it can also provide significant reduction in the computational complexity when a large number of users are accommodated in the system.