gps

Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.

Taftan is a semi-active volcano located in southeast Iran with many craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan Peak. 58 Sentinel 1-A images acquired from January 2015 to December 2020 in the ascending orbit mode, and 102 Sentinel 1-A Sentinel 1-B images acquired from October 2014 to June 2020 in the descending orbit mode were preprocessed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS softwares, in which atmospheric corrections were made automatically, and then the surface displacement of Taftan volcano estimated.The Line of Sight (LOS) displacement corresponding to the uplift was 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1for the descending orbit. Because no GPS station was close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method. The GPS station of SARAVAN has situated inside the town ,and it is appropriate to use and analyze PSI technique in this station. As a result, the researchers found that the PSI method is in accordance with the GPS data.

This study aimed to design a system utilizing an ESP8266 microcontroller, GPS, and MPU6050 sensors to observe and record the movement patterns of elderly patients. A scale prototype was developed by integrating an MPU6050 IMU sensor and a GPS module with an ESP8266 module configured for Wi-Fi communication with a nearby server. Data were collected and evaluated by sensors positioned on test subjects to imitate the movements of elderly patients. Upon successful real-time tracking of the test subject's movements, the data was transmitted to the network using Blynk and email applications for analysis and subsequent display. The collected data yielded valuable insights into the patient's conduct, encompassing frequent walking, pacing, and wandering. The utilization of this device has the potential to improve the safety and well-being of elderly patients and provide valuable data to healthcare professionals and caregivers through its real-time tracking capabilities.

The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural Network (MLP NN) and Convolutional Neural Network (CNN). Using the proposed method reduces data complexity, which can reduce the number of learning data requirements. The simulation results indicate that, by applying the proposed image processing algorithm for different dimensions of CAF images, the CNN performs better than MLP NN in terms of training accuracy; the MLP NN is superior to CNN in terms of convergence speed of training. In addition, the results demonstrate that the operation of the proposed method is appropriate in the case of small-delay spoofed signals. Therefore, for the intervals above 0.25 code chip, the proposed method detects spoofing attacks with a correct detection probability close to one.

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%.