scholarly journals Multitarget Parameter Estimation of Monopulse Radar Based on RJ-MCMC Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Gong ◽  
Yiduo Guo ◽  
Hui Yuan ◽  
Qun Wan
Keyword(s):  
Parameter Estimation ◽  
Target Location ◽  
Estimation Method ◽  
Observation Time ◽  
Joint Estimation ◽  
Estimation Methods ◽  
Mcmc Algorithm ◽  
Target Number ◽  
Real Time Processing ◽  
Location Parameters

A multiple parameter estimation method based on RJ-MCMC for multiple nondiscernible targets is proposed in this paper. Different from the traditional estimation methods, the proposed method can simultaneously complete the joint estimation of the target number and the target location parameters. More importantly, the method proposed in this chapter is applicable to many situations with different power and nondistinguishable target. The simulation results show that the method proposed in this chapter requires less observation time to obtain similar and even better estimation performance than the ML-MDL method, which is of great significance for real-time processing.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models

Batteries ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
S M Rakiul Islam ◽  
Sung-Yeul Park ◽  
Balakumar Balasingam
Keyword(s):  
Parameter Estimation ◽  
Equivalent Circuit ◽  
Electrochemical Impedance ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Parameter Extraction ◽  
Internal Resistance ◽  
Least Square ◽  
Estimation Methods ◽  
Li Ion

Internal resistance is one of the important parameters in the Li-Ion battery. This paper identifies it using two different methods: electrochemical impedance spectroscopy (EIS) and parameter estimation based on equivalent circuit model (ECM). Comparing internal resistance, the conventional parameter estimation method yields a different value than EIS. Therefore, a hysteresis-free parameter identification method based on ECM is proposed. The proposed technique separates hysteresis resistance from the effective resistance. It precisely estimated actual internal resistance, which matches the internal resistance obtained from EIS. In addition, state of charge, open circuit voltage, and different internal equivalent circuit components were identified. The least square method was used to identify the parameters based on ECM. A parameter extraction algorithm to interpret impedance spectrum obtained from the EIS. The algorithm is based on the properties of Nyquist plot, phasor algebra, and resonances. Experiments were conducted using a cellphone pouch battery and a cylindrical 18650 battery.

A novel parameter estimation method for the Weibull distribution on heavily censored data

2019 ◽  
pp. 1748006X1988764 ◽  
Author(s):  
Renyan Jiang
Keyword(s):  
Parameter Estimation ◽  
Censored Data ◽  
Estimation Method ◽  
Least Square ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Distribution Models ◽  
Data Set ◽  
Hybrid Censoring ◽  
Parameter Estimation Method

It is desired to build the life distribution models of critical components (which are assumed to be non-repairable) of a repairable system as early as possible based on field failure data in order to optimize the operation and maintenance decisions of the components. When the number of the systems under observation is large and the observation duration is relatively short, the samples obtained for modeling are large and heavily censored. For such samples, the classical parameter estimation methods (e.g. maximum likelihood method and least square method) do not provide robust estimates. To address this issue, this article develops a hybrid censoring index to quantitatively describe censoring characteristics of a data set, proposes a novel parameter estimation method based on information extracted from censored observations, and evaluates the accuracy and robustness of the proposed method through a numerical experiment. Its applicable range in terms of the hybrid censoring index is determined through an accuracy analysis. The experiment results show that the proposed approach provides much accurate estimates than the classical methods for heavily censored data. A real-world example is also included.

scholarly journals Cooperative Localization and Time Synchronization Based on M-VMP Method

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6315
Author(s):  
Zhongliang Deng ◽  
Shihao Tang ◽  
Buyun Jia ◽  
Hanhua Wang ◽  
Xiwen Deng ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Message Passing ◽  
Clock Synchronization ◽  
Dynamic Networks ◽  
Gaussian Function ◽  
Estimation Method ◽  
Convergence Speed ◽  
Joint Estimation ◽  
Estimation Methods ◽  
Cooperative Localization

Localization estimation and clock synchronization are important research directions in the application of wireless sensor networks. Aiming at the problems of low positioning accuracy and slow convergence speed in localization estimation methods based on message passing, this paper proposes a low-complexity distributed cooperative joint estimation method suitable for dynamic networks called multi-Gaussian variational message passing (M-VMP). The proposed method constrains the message to be a multi-Gaussian function superposition form to reduce the information loss in the variational message passing algorithm (VMP). Only the mean, covariance and weight of each message need to be transmitted in the network, which reduces the computational complexity while ensuring the information completeness. The simulation results show that the proposed method is superior to the VMP algorithm in terms of position accuracy and convergence speed and is close to the sum-product algorithm over a wireless network (SPAWN) based on non-parametric belief propagation, but the computational complexity and communication load are significantly reduced.

scholarly journals A New Quantile Estimation Method of Weibull-Rayleigh Distribution

2020 ◽  
pp. 28-37
Author(s):  
A. S. Ogunsanya ◽  
E. E. E. Akarawak ◽  
W. B. Yahya
Keyword(s):  
Parameter Estimation ◽  
Estimation Method ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Absolute Error ◽  
Rayleigh Distribution ◽  
Least Square ◽  
Estimation Methods ◽  
Data Set ◽  
Total Deviation

In this paper, we compared different Parameter Estimation method of the two parameter Weibull-Rayleigh Distribution (W-RD) namely; Maximum Likelihood Estimation (MLE), Least Square Estimation method (LSE) and three methods of Quartile Estimators. Two of the quartile methods have been applied in literature, while the third method (Q1-M) is introduced in this work. The methods have been applied to simulate data. These methods of estimation were compared using Error, Mean Square Error and Total Deviation (TD) which is also known as Sum Absolute Error Estimate (SAEE). The analytical results show that the performances of all the parameter estimation methods were satisfactory with data set of Weibull-Rayleigh distribution while degree of accuracy is determined by the sample size. The proposed quartile (Q1-M) method has the least Total Deviation and MSE. In addition, the quartile methods perform better than MLE for the simulated data. In particular, the proposed quartile methods (Q1-M) have an added advantage of simplicity in usage than MLE methods.

Parameter estimation of UAV from flight data using neural network

2018 ◽  
Vol 90 (2) ◽  
pp. 302-311 ◽  
Author(s):  
Dhayalan R. ◽  
Subrahmanyam Saderla ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Parameter Estimation ◽  
Design Methodology ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Process Noise ◽  
Content Type ◽  
Flight Data ◽  
Aerial Vehicle

Purpose The purpose of this paper is to present the application of the neural-based estimation method, Neural-Gauss-Newton (NGN), using the real flight data of a small unmanned aerial vehicle (UAV). Design/methodology/approach The UAVs in general are lighter in weight and their flight is usually influenced by the atmospheric winds because of their relatively lower cruise speeds. During the presence of the atmospheric winds, the aerodynamic forces and moments get modified significantly and the accurate mathematical modelling of the same is highly challenging. This modelling inaccuracy during parameter estimation is routinely treated as the process noise. Furthermore, because of the limited dimensions of the small UAVs, the measurements are usually influenced by the disturbances caused by other subsystems. To handle these measurement and process noises, the estimation methods based on neural networks have been found reliable in the manned aircrafts. Findings Six sets of compatible longitudinal flight data of the designed UAV have been chosen to estimate the parameters using the NGN method. The consistency in the estimates is verified from the obtained mean and the standard deviation and the same has been validated by the proof-of-match exercise. It is evident from the results that the NGN method was able to perform on a par with the conventional maximum likelihood method. Originality/value This is a partial outcome of the research carried out in estimating parameters from the UAVs.

Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method

2009 ◽  
Vol 113 (1142) ◽  
pp. 243-252 ◽  
Author(s):  
N. K. Peyada ◽  
A. K. Ghosh
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Dynamic Model ◽  
Newton Method ◽  
Initial Conditions ◽  
Estimation Method ◽  
Wind Tunnel Test ◽  
Neural Model ◽  
Estimation Methods ◽  
Flight Data

Abstract A new parameter estimation method based upon neural network is proposed. The method proposed here uses feed forward neural networks to establish a neural model that could be used to predict subsequent time histories given the suitable measured initial conditions. The proposed neural model would not represent a generic flight dynamic model. The neural model in this case develops point to point fitting of the input and the output data. Thus, it could at best be referred to as flight dynamic model in restricted sense. Gauss-Newton method is then used to obtain optimal values of the aerodynamic parameters by minimising a suitable defined error cost function. The method has been validated using longitudinal and lateral-directional flight data of various test aircraft. The results thus obtained were compared with those obtained through wind tunnel test, or those obtained using Maximum likelihood and/or Filter error methods. Unlike, most of the parameter estimation methods, the proposed method does not require a prior description of the model. It also bypasses the requirement of solving equations of motion. This feature of the proposed method may have special significance in handling flight data of an unstable aircraft.

Comparison of parameter estimation methods for detecting climate-induced changes in productivity of Pacific salmon (Oncorhynchus spp.)

2000 ◽  
Vol 57 (1) ◽  
pp. 181-191 ◽  
Author(s):  
Randall M Peterman ◽  
Brian J Pyper ◽  
Jeff A Grout
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Linear Regression ◽  
Pacific Salmon ◽  
Estimation Method ◽  
Estimation Methods ◽  
Management Actions ◽  
Wide Range ◽  
Standard Linear ◽  
Induced Changes

Pacific salmon (Oncorhynchus spp.) populations can experience persistent changes in productivity, possibly due to climatic shifts. Management agencies need to rapidly and reliably detect such changes to avoid costly suboptimal harvests or depletion of stocks. However, given the inherent variability of salmon populations, it is difficult to detect changes quickly, let alone forecast them. We therefore compared three methods of annually updating estimates of stock-recruitment parameters: standard linear regression, Walters' bias-corrected regression, and a Kalman filter. We used Monte Carlo simulations that hypothesized a wide range of future climate-induced changes in the Ricker a parameter of a salmon stock. We then used each parameter estimation method on the simulated stock and recruitment data and set escapement targets and harvest goals accordingly. In these situations with a time-varying true Ricker a parameter, Kalman filter estimation resulted in greater mean cumulative catch than was produced by the standard linear regression approach, Walters' bias correction method, or a fixed harvest rate policy. This benefit of the Kalman filter resulted from its better ability to track changing parameter values, thereby producing escapements closer to the optimal escapement each year. However, errors in implementing desired management actions can significantly reduce benefits from all parameter estimation techniques.

scholarly journals Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Wu ◽  
Hanyang Xie ◽  
Jyun-You Chiang ◽  
Gang Peng ◽  
Yan Qin
Keyword(s):  
Parameter Estimation ◽  
Weibull Distribution ◽  
Glass Fiber ◽  
Fiber Strength ◽  
Estimation Method ◽  
Real Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Strength Data ◽  
Parameter Estimation Method

Glass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while the poor parameter estimation method cannot obtain reliable analysis results. Therefore, a new parameter estimation method is proposed. Based on the simulation results, it is found that the proposed parameter estimation method outperforms the other competitors to obtain reliable estimates of the Weibull parameters. Finally, the proposed parameter estimation method is applied to two real data sets of glass fiber strength for illustration. The results of data analysis show that our proposed parameter estimation method is more suitable for these data sets than other estimation methods.

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