The aim of the current essay is to s tudy the similarity between the epis temology of John Dewey and hermeneutical approach in philosophy. The author has sought to show that despite difference in intellectual origIns of Dewey and such hermeneuticis ts as Dilthey one can find considerable overlaps between the epis temological approach of Dewey and the hermeneutical approach, and discern that they are basically of the same philosophical orientation. What is of a key role in the emergence of this interrelation is the concept of “ experience” . On the one hand, Dewey seeks via formation of a specific notion of experience – that as we see is different from empiricis t’ s notion of experience – to found his epis temology without the mediation of any metaphysical and transcendental idea. On the other hand, in hermeneutics of Wilhelm Dilthey the notion of “ lived experience” is of central place. This comparison reveals that the unders tanding of these two approaches of the concept of experience has numerous similarities and how these similarities close their philosophical s tances to each other Insofar as one can s tate that Dewey’ s Ins trumentalis t epis temology is indeed hermeneutical in its essence.

Inertial Navigation System (Ins) and Global Positioning System (GPS), are used in various navigation and positioning applications. Because each of the Ins and GPS technologies has some limitations and advantages, during last two decades, the systems integration has been widely used for accurate and reliable navigation and positioning. In an integrated system, accurate GPS observations are used to estimate the high rate Ins errors and state vector (including Ins error vector, position, velocity and other optional parameters). A field test results are presented in this paper. The goal of this test is to compare the coordinates of a relatively low cost Ins, GPS RTK coordinates, and the integrated GPS/Ins results. The decentralized approach has been used for this integration.

A common technique for navigation and positioning applications is the Global Positioning System (GPS)/Inertial Navigation System (Ins) integration, which combines the strengths of GPS and Ins to offer accurate and reliable information. As a standalone system, the performance of the Ins deteriorates as time is passed. Kalman Filter (KF) is used for GPS/Ins integration, and its performance is excellent for simple data. However, in a complex and natural set environment, its performance degrades when the system performs relatively long; therefore, resolving the long-time problem for the GPS/Ins system is challenging. The novelty of this paper is GPS/Ins integration with the Faded Kalman Filter (FKF). In the FKF, the measurement updates are weighted differently to adapt to changes in the system. This approach allows the filter to adapt to changes or uncertainties in the system dynamics. GPS/Ins integration performance is significantly improved using this algorithm rather than a simple KF. An average of 45% reduces the positioning errors compared to traditional KF.

In this paper the feasibility of rapid alignment and calibration of a static strapdown inertial navigation system (Ins) is evaluated. Resting conditions including zero-velocity update and a known initial heading direction as virtual external measurement data are integrated with Ins data. By comparing the virtual external measurements with the estimates of those generated by the aligning Ins, estimates of the velocity and heading errors can be obtained and these errors will be propagated in the Ins as a result of alignment inaccuracies. An extended Kalman filter based on an augmented process model and a measurement model is designed to estimate alignment attitudes and biases of inertial sensors. Monte Carlo simulation results show that the integration of Ins with rest conditions is very effective in rapid and fine leveling and azimuth alignment of Ins, but this type of data fusion due to poor acceleration and angular rates of static condition has no chance of valuable calibration of all inertial sensor biases.

The navigation system of vehicles calculates the speed, position and attitude of the moving device relative to a reference frame and provides it to the guidance system. One of the most widely used navigation systems is the inertial navigation system. Due to the increasing error of the navigation system over time, the integrated navigation system is usually used for long-term navigation. One of the most common integrated navigation systems is the Ins integrated navigation system with GPS, each of them which has advantages and disadvantages that cover the other. In this paper, two GPS and Ins data integration algorithms with loosely and tightly coupled integration are implemented and compared. In the loosely coupled method, GPS measurements include positions and speeds. In the tightly coupled method, a model for GPS error is considered, which includes bias dynamics and GPS clock drift. The result of combining GPS and Ins data in this way is closer to the truth, but in the method of loosely coupled, the result of the combination follows the average of GPS data. In the implementation of the combined algorithm with tightly coupled, raw GPS data is used, which is pseudo-range and pseudo-range rate along with astronomical information. In this paper, an extended Kalman filter is used to integrate the data of two measurement data. The simulation results show the superiority of tightly over loosely connection performance. Also, the integration algorithm with a loosely approach has been implemented in hardware and car testing has been done in two scenarios of connecting and disconnecting GPS.

The importance of navigation precision in high dynamic environments has led to integrating the Inertial Navigation System (Ins) with satellite navigation systems. In one of those integration methods that Ins is integrated with GPS, GPS outage is an unavoidable challenge. Moreover, due to significant noisy signal existing in low-cost MEMS sensors, navigation precision severely decreases, and the Ins error will diverge in the long term. This paper improves the Ins/GPS navigation system using Artificial Intelligence (AI) during GPS outage. In this approach, the Ins outputs at t and t-1 are injected to the AI module as the positioning and timing information. While GPS is available, the AI module is trained, and its output is compared with the GPS output. The AI module indeed intents to drive the Ins output to the GPS output during GPS outage. To evaluate this approach and compare with some different intelligence systems, we have utilized Neural Networks (NNs) as an AI module in five different NNs: multilayer perceptron (MLP(, radial basis function )RBF(, support vector regression (SVR(, Wavelet, and adaptive neuro-fuzzy inference system (ANFIS). The required dataset to compare all five mentioned methods is gathered in a real environment by a mini-airplane. The results of all five methods represent that the proposed methods have superior performance compared to other traditional methods,so that the wavelet NN outperforms others by approximately 30%.