Abstract
The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE.
Highlights
The ability to navigate has always been of great importance when discovering new and unknown territories of the world
In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of root mean squared error (RMSE)
In order to analyze the performance of the proposed system, we compared the proposed learning to prediction model with conventional alpha–beta filter to observe the improvement in the prediction accuracy of the alpha–beta filter algorithm results
Summary
The ability to navigate has always been of great importance when discovering new and unknown territories of the world. The evolution of various navigation techniques has helped us spread across the planet. Navigation remains an important part of our society. The technologies of today enable us to use the navigation in a whole new way than our ancestors could. It is possible to use navigation to find your way to a certain address or a point of interest, for example, the closest gasoline station or restaurant. All these functions are available because of Global positioning system (GPS) which has been integrated into those applications [1]
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