Showing 6 results for Dop
M. Aliramezani, Sh. Mohammad Nejad,
Volume 8, Issue 2 (6-2012)
Abstract
In this paper, a novel design of all-solid photonic bandgap fiber with ultra-low
confinement loss is proposed. The confinement loss is reduced remarkably by managing the
number of rods rings, up-doping level, pitch value, and rods diameters. Moreover, the
designed PCF shows ultra-flattened dispersion in L- and U-band. Furthermore, a new
design, based on introducing of an extra ring of air holes on the outside of the all-solid
bandgap structure, is then proposed and characterized. We demonstrate that it significantly
reduces the fiber diameter to achieve negligible confinement loss. The validation of the
proposed design is carried out by employing a two dimensional finite difference frequency
domain with perfectly matched layers.
M. R. Mosavi, Z. Shokhmzan,
Volume 11, Issue 3 (9-2015)
Abstract
The Global Positioning System (GPS) signals are very weak signal over wireless channels, so they are vulnerable to in-band interferences. Therefore, even a low-power interference can easily spoof GPS receivers. Among the variety of GPS signal interference, spoofing is considered as the most dangerous intentional interference. The spoofing effects can mitigate with an appropriate strategy in the receiver. In this paper, we use methods of adaptive filter based on Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) algorithms in-order to defense against spoofing. The proposed techniques are applied in the acquisition stage of the receiver. The proposed methods have been implemented on real dataset. The results explain that the suggested algorithms significantly decrease spoofing. Also, they improve Position Dilution of Precision (PDOP) parameter. Based on the results, NLMS algorithm has better performance than LMS algorithm.
A. A. Abedi, M. R. Mosavi, K. Mohammadi, M. R. Daliri,
Volume 12, Issue 3 (9-2016)
Abstract
One of the instruments for determination of position used in several applications is the Global Positioning System (GPS). With a cheap GPS receiver, we can easily find the approximate position of an object. Accuracy estimation depends on some parameters such as dilution of precision, atmospheric error, receiver noise, and multipath. In this study, position accuracy with GPS receiver is classified in three classes. Nine classification methods are utilized and compared. Finally, a new method is selected for classification. Results are verified with experimental data. Success rate for classificationis approximately 84%.
Z. Shokhmzan, M. R. Mosavi, M. Moazedi,
Volume 13, Issue 4 (12-2017)
Abstract
The vulnerability of civil GPS receiver to interference may be intentional or unintentional. Among all types of interference, replay attack intended as the most dangerous intentional one. The signal structure of replay attack is almost the same with the satellite signal. The interference effects can be reduce with the design of an appropriate filter in the receiver. This paper presents two methods based on Finite Impulse Response (FIR) filter in frequency and time domain to mitigate the interference effect on GPS signals. Designed FIR filter protects GPS against the replay attack. The suggested filter is applied in the acquisition of the receiver. The proposed method has been implemented on collected dataset. The results show that the proposed algorithms significantly reduce interference. Also, they improve Position Dilution of Precision (PDOP) parameter. Based on the results, the FIR filter technique in time domain has better performance than the frequency domain.
M. H. Refan, A. Dameshghi,
Volume 16, Issue 2 (6-2020)
Abstract
Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.
S. M. Ejabati, S. H. Zahiri,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the number of algorithm individuals and the creation of feasible subspaces appropriate to environmental conditions. Accordingly, to prevent early convergence along with the increasing speed of local search, the search space is divided with respect to the conditions of each moment into subspaces labeled as focused search area, and focused individuals are recruited to make search for it. Moreover, the structure of the design is in such a way that it often adapts itself to environmental condition, and there is no need to identify any change in the environment. The framework proposed for particle swarm optimization algorithm has been implemented as one of the most notable static optimization and a new optimization method referred to as ant lion optimizer. The results from moving peak benchmarks (MPB) indicated the good performance of the proposed framework for dynamic optimization. Furthermore, the positive performance of practices was assessed with respect to real-world issues, including clustering for dynamic data.
