Non-Linear Least Squares and Maximum Likelihood Estimation of Probability Density Function of Cross-Border Transmission Losses

Tolić, Ivan; Miličević, Kruno; Šuvak, Nenad; Biondić, Ivan

doi:10.1109/tpwrs.2017.2738319

Cited by 19 publications

(12 citation statements)

References 34 publications

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“…The nonlinear model for guidance error separation is intricate. In fact, the traditional thought was to solve the unconstrained optimization problem based on the nonlinear least-squares theory [17], [18]. The general separation methods for the linear model can be used to build nonlinear error separation models, like the nonlinear Bayesian estimation [19], the nonlinear PCA [20], and the nonlinear regularization estimation [21].…”

Section: B Error Separation Methodsmentioning

confidence: 99%

Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm

Zhou

Wang

Liu³

et al. 2019

IEEE Access

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For missile's accuracy assessment, an accurate separation about the guidance of systematic errors is a critical part. Based on the vehicles from a mobile launcher platform, this paper proposes a nonlinear error separation model and a corresponding method in consideration of the ill-conditioning of the environmental function matrix, and the coupling of the guidance instrumental errors and the initial errors. The nonlinear model is built in combination with the tracking data. For the error separation problem with ill-conditioning, the traditional nonlinear methods can only slightly weaken the degree of ill-conditioning rather than solve it. To address this issue, this paper puts forward a novel guidance systematic error separation method based on the artificial fish swarm algorithm (AFSA). We first provide a brief introduction to AFSA and then analyze the convergence and the optimality of parameter estimation. Furthermore, we present the details of our novel algorithm that can address the guidance systematic error separation problem. We conduct a set of simulations to verify our approach. The simulation results confirm that our approach, which is based on AFSA, can improve the error separation accuracy effectively and perform better than the Bayesian estimation based on the traditional linear model and the Bayesian maximum a posteriori estimation based on the nonlinear model.

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Section: B Error Separation Methodsmentioning

confidence: 99%

Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm

Zhou

Wang

Liu³

et al. 2019

IEEE Access

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“…Meanwhile, there are 2mn constraint equations if all receivers can receive all transmitters' signals. Additionally, if we know the distance between two receivers, there are m (m − 1)/2 constraint equations shown as Equations (24).…”

Section: Fast Calibration Schemementioning

confidence: 99%

“…To realize calibration, the relative quantity relationship between the receivers and transmitters can be expressed as Equations ( 25) based on Equations ( 22) -Equations (24).…”

Section: Fast Calibration Schemementioning

confidence: 99%

Development and Verification of a Novel Measurement and Position System for Confined Cabin

et al. 2021

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The high-accuracy key points measurement is necessary for shipbuilding, and numerous measuring systems have been invented. However, there are still limitations and challenges in measurement frequency, calibration method and environmental disturbances. This paper presents a novel and automatic measurement and position system based on laser scan technology. The system components and mathematical model are introduced. Subsequently, a fast calibration method is established, also different calibration schemes and calibration algorithms are discussed. To avoid the environmental disturbances to this system, a robust positioning algorithm based on maximum likelihood estimation is presented. A series of simulation and verification experiments in normal and interferential environment are designed to evaluate the precision of the proposed system. The results shows that the system's position accuracy can reach 1.00mm with 50Hz output whether there are disturbances or not, which can meet the need of position in the confined cabin.

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“…It is shown that such a simple model can also be very useful in applications itself, not only related to the image analysis. For instance, in Tolić et al (2017) the sum of uniform and normal distributions is confirmed to be the most representative distribution for modelling transmission loss data.…”

Section: Introductionmentioning

confidence: 97%

LeArEst: Length and Area Estimation from Data Measured with Additive Error

Benšić¹,

Taler²,

Hamedovic³

et al. 2017

The R Journal

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This paper describes an R package LeArEst that can be used for estimating object dimensions from a noisy image. The package is based on a simple parametric model for data that are drawn from uniform distribution contaminated by an additive error. Our package is able to estimate the length of the object of interest on a given straight line that intersects it, as well as to estimate the object area when it is elliptically shaped. The input data may be a numerical vector or an image in JPEG format. In this paper, background statistical models and methods for the package are summarized, and the algorithms and key functions implemented are described. Also, examples that demonstrate its usage are provided.

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Non-Linear Least Squares and Maximum Likelihood Estimation of Probability Density Function of Cross-Border Transmission Losses

Cited by 19 publications

References 34 publications

Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm

Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm

Development and Verification of a Novel Measurement and Position System for Confined Cabin

LeArEst: Length and Area Estimation from Data Measured with Additive Error

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