Modified maximum likelihood space registration method for shipborne multi-radar signal processing

Bolus tracking of contrast agent with MRI can be used to measure local cerebral haemodynamic parameters including flow and volume. This can provide useful information for assessment of treatment options related to ischemic damage following stroke. The analysis of the acquired dynamic data requires the use of de-convolution to reconstruct the residue function (R) of the contrast agent. Measurement of the tissue time course and the arterial input function are obtained by T2 or T2* weighted sequences. Reconstruction of R provides estimates of flow, volume and mean transit time. The raw MRI signal intensity is well approximated by Rician statistics. The standard approach to estimation involves logarithmic transformation and least squares deconvolution. At low signal to noise this approach may not be efficient. A maximum likelihood (ML) deconvolution method involving an iterative re-weighted non-linear least squares algorithm has been developed. Studies are presented to evaluate improvements achieved by this approach relative to the standard deconvolution method. Mean square error properties of the residue function as well as the derived functionals of flow and blood volume parameters are considered. The results show that over a range of realistic signal to noise values, significant improvements in estimation accuracy are achieved by ML.

This paper presents a method to counteract the detrimental effects of carrier frequency offsets (CFOs) in the uplink of OFDMA-based wireless networks. The CFO values of different users are first estimated from the received decomposed signal using the best linear unbiased estimator (BLUE). The CFOs effects are then compensated based on the estimated values and also taking advantage of an iterative parallel interference cancellation (PIC) scheme. A novel contribution of this paper is introducing an iterative concatenated estimation and compensation algorithm for suppressing the residual interference due to imperfect estimators. The introduced method is successful where the estimator converges after only one iteration with a significant improvement in estimation accuracy. Also very close BER performance results can be achieved to that of systems with perfect knowledge or without CFOs particularly at practical SNRs.

This paper presents a hybrid approach for estimating the admittance matrix of open-loop distribution networks by integrating Total Least Squares (TLS) regression with deep learning (DL) and graph neural networks (GNNs). The proposed model refines initial TLS estimates by leveraging deep learning to handle nonlinearities and graph neural networks to incorporate network topology, thereby addressing the challenges posed by noisy and incomplete data. Physical constraints-including Kirchhoff's current and voltage laws-are embedded within the optimization process to ensure that the estimated admittance matrix remains physically consistent. Extensive testing on the IEEE 33-bus and 123-bus systems demonstrates significant improvements in estimation accuracy, convergence speed, and adherence to physical principles, underscoring the model's potential for real-world applications in modern power system analysis and operation.

Accurate estimation of submodule capacitance in modular multilevel converters (MMCs) is essential for optimal performance and reliability, particularly in motor drive applications such as permanent magnet synchronous motor (PMSM) drives. This paper presents a novel approach utilizing recurrent neural networks with long short-term memory (RNN–LSTM) to precisely estimate capacitance in MMC-based PMSM drives. By leveraging simulation data from MATLAB, the LSTM neural network is trained to predict capacitance based on voltage, current, and their temporal variations. The proposed LSTM architecture effectively captures the dynamic behavior of MMCs in PMSM drives, providing high-precision capacitance estimates. The results demonstrate significant improvements in estimation accuracy, validated through mean squared error (MSE) metrics and comparative analysis of actual versus estimated capacitance. The method’s robustness is further confirmed under varying operating conditions, highlighting its practical utility for real-time applications in power electronic systems.

We propose a novel bit error rate (BER) estimation scheme based on a k-means clustering algorithm assisted Gaussian approach (GA). This method can implement accurate BER estimation within a short symbol sequence and is applicable for arbitrary modulation formats. First, after carrier phase recovery (CPR), the k-means clustering algorithm is used to partition clusters and find the respective centroids of each cluster. The means and variances are calculated from the symbols of each cluster. Compared with the decision-directed method based on power normalization, our method can find more precise means and further obtain more accurate variances. Subsequently, with the accurate means and variances, the resultant probability density function (PDF) of each symbol under Gaussian assumption is integrated over the respective decision zone to calculate symbol error rate (SER). Finally, the general conversion factor from SER to BER is introduced by taking into account the coding information of adjacent symbols. Therefore, the accurate BER estimation is attributed to the more accurate statistical parameter calculation and general SER-to-BER conversion schemes. The proposed scheme is verified in 34 GBaud polarization division multiplexing (PDM)-QPSK/8-QAM/16-QAM experiments. Compared with error vector magnitude (EVM)-to-BER conversion and GA+ common approximation (CA) scheme, the better estimation accuracy in the BER range from 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> -10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> is achieved successfully with only 10000 symbols. More specifically, our method has a significant improvement in estimation accuracy for non-Gray-mapped signals under low optical signal-to-noise ratio (OSNR). The BER estimation error can be reduced from 60 to 14% with PDM-8-QAM signal considered when actual BER is around 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> .

Flaring is a routine practice in the upstream gas industry to dispose of waste gases, but its efficiency can drop significantly under non-ideal conditions such as crosswinds, over-aeration, or over-steaming. These inefficiencies lead to incomplete combustion, producing harmful substances like carbon monoxide and unburned methane, which contribute significantly to global warming. Current solutions for monitoring flare efficiency are often complex or expensive, limiting their widespread adoption. This work introduces a novel framework for estimating flare combustion efficiency (CE) using a multi-modal machine learning architecture enhanced by a Temporal Standard Deviation (TSD) preprocessing technique. Our approach combines synchronized visual data with minimal field measurements (FM) for accurate efficiency estimation. We first extract sequential frames from an RGB video stream of flares and process them to extract TSD images, which essentially highlight the variability and dynamic changes in the combustion process. Next, we extract image feature representations from TSD images using the state-of-the-art Vision Transformer (ViT) and fuse them with experimentally selected FM data to create a comprehensive combustion dataset. A CatBoost regression model is then trained on this dataset to estimate the final CE. Our proposed framework is validated using real-world data from industrial flare upstream operations. The results demonstrate significant improvements in estimation accuracy and reliability compared to traditional methods, achieving an R-squared score of 94.77% with minimal FM data. This approach not only enhances the understanding of combustion dynamics but also offers a scalable, cost-effective solution for continuous flare monitoring and optimization.

This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, and (ii) sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first distributed state estimation framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.

The major challenge of current strapdown inertial navigation system/celestial navigation system (SINS/RCNS) is failing to calibrate all the error coefficients of gyro and star sensor in-flight accurately, which will lead to the attitude accuracy degradation. To address this question, this paper proposes an optimal calibration method based on observability analysis. Here, we derive the star sensor measurement model with respect to initial alignment errors, gyro errors, and star sensor installation errors. And the observability analysis is employed to explain the reason why all the error coefficients can be estimated effectively by three maneuvers merely. Finally, the optimal observation sequence is determined based on the fisher information matrix (FIM). The proposed optimal calibration method is evaluated by a representative suborbital flight vehicle trajectory, which represents significant improvements in estimation accuracy of error coefficients, and realizes the attitude accuracy enhancements.

In this paper, we consider a mixture of two uniform distributions and derive L-moment estimators of its parameters. Three possible ways of mixing two uniforms, namely with neither overlap nor gap, with overlap, and with gap, are studied. The performance of these L-moment estimators in terms of bias and efficiency is compared to that obtained by means of the conventional method of moments (MM), modified maximum likelihood (MML) method and the usual maximum likelihood (ML) method. These intensive simulations reveal that MML estimators are the best in most of the cases, and the L-moment estimators are less subject to bias in estimation for some mixtures and more efficient in most of the cases than the conventional MM estimators. The L-moment estimators are, in some cases, more efficient than the ML and MML estimators.

ABSTRACTCensoring can be occurred in many statistical analyses in the framework of experimental design. In this study, we estimate the model parameters in one-way ANOVA under Type II censoring. We assume that the distribution of the error terms is Azzalini's skew normal. We use Tiku's modified maximum likelihood (MML) methodology which is a modified version of the well-known maximum likelihood (ML) in the estimation procedure. Unlike ML methodology, MML methodology is non-iterative and gives explicit estimators of the model parameters. We also propose new test statistics based on the proposed estimators. The performances of the proposed estimators and the test statistics based on them are compared with the corresponding normal theory results via Monte Carlo simulation study. A real life data is analysed to show the implementation of the methodology presented in this paper at the end of the study.

Bubble detection in sodium flow using EVFM and correlation coefficient calculation

The pairwise comparisons or post-hoc methods are used for determining the source of the difference of group means in one-way ANOVA. These methods are mostly depend on normality assumption. However, nonnormal distributions are more prevalent than normal distribution. Therefore, robust estimation methods become very important tools in statistical analysis. In this paper, we assume that the distribution of the error terms is Azzalini's skew $t$ and obtain the robust estimators in order to make post-hoc tests in one-way ANOVA. We use maximum likelihood (ML) methodology and compare this methodology with some of robust estimators like M estimator, Wave estimator, trimmed mean and modified maximum likelihood (MML) methodology with Monte Carlo simulation study. Simulation results show that the proposed methodology is more preferable. We also compare power values of the test statistics and conclude that the test statistics based on the ML estimators are more powerful than the test statistics based on other methods.

Three-dimensional point clouds captured by sensors can quantify plant phenotype, which plays an important role in agricultural intelligence. Many scanned objects in agriculture and forestry are tall and obscured by leaves, so point clouds captured by either terrestrial or airborne methods may be incomplete. In order to obtain a more complete point cloud, this paper proposes a point cloud registration method based on the fast point feature histogram (FPFH), which aligns point clouds collected from different viewpoints. This method calculates the FPFH feature of each point and the Bhattacharyya distance between point pairs. Effective strategies pick out reliable sets of point pairs. Singular value decomposition is used to obtain the transformation relationship between point clouds. Experimental results show that the proposed method has high accuracy for plant point cloud registration in real scenes, and the root-mean-square error is smaller than that of common registration methods of SAC-IA and NDT.KeywordsFast point feature histogramBhattacharyya distanceRandom sample consensusPoint cloud registration

Improving battery state estimation accuracy through the addition of a series capacitor

In this paper, we propose a disturbance covariance matrix estimation method for radar signal processing when the number of training samples is severely limited. Several maximum likelihood (ML) estimation methods based on the structured of the covariance matrix, which is modeled as a sum of an unknown positive semi-definite Hermitian matrix and a matrix proportional to identity, have been proposed to solve the problem. Based on the covariance structure, we incorporate additional constraint on the low rank property of the covariance matrix, and propose a low rank regularized ML estimation method for covariance matrix estimation. The low rank is achieved by solving a reweighted trace minimization problem. Experimental results demonstrate the effective of the proposed method, both in the accuracy of rank estimation and the improvement of signal interference and noise ratio (SINR).