Distributed data fusion algorithms for inertial network systems

New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and new distributed data fusion algorithms are developed for inertial network systems. The distributed data fusion algorithm comprises two steps: inertial measurement fusion and state fusion. The inertial measurement fusion allows each node to assimilate all the inertial measurements from an inertial network system, which can improve the performance of inertial sensor failure detection and isolation algorithms by providing more information. The state fusion further increases the accuracy and enhances the integrity of the local inertial states and navigation state estimates. The simulation results show that the two-step fusion procedure overcomes the disadvantages of traditional inertial sensor alignment procedures. The slave inertial nodes can be accurately aligned to the master node

Flight test results of the Strapdown hexad Inertial Reference Unit (SIRU). Volume 3: Appendices A-G

Results of flight tests of the Strapdown Inertial Reference Unit (SIRU) navigation system are presented. The fault tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance. Selected facets of the flight tests are also described in detail and include some of the following: (1) flight test plans and ground track plots; (2) navigation residual plots; (3) effects of approximations in navigation algorithms; (4) vibration spectrum of the CV-340 aircraft; and (5) modification of the statistical FDICR algorithm parameters for the flight environment

Code Optimization for Strapdown Inertial Navigation System Algorithm

Inertial navigation systems are in common use for decades due to its advantages. Since INS outputs are usually used for inputs in different control algorithms (depending on applications), INS will induce certain errors and limitations. This chapter deals with optimization of the inertial navigation algorithm against limitations due to the accuracy and stability of signals from the sensors and constraints resulting from the integration step and processor speed used for embedded applications. Inertial navigation considered here is “strapdown” inertial navigation system (SINS) which assumes a fixed inertial measurement unit (IMU). In this chapter, fundamentals of strapdown inertial navigation will be presented as well as three different algorithms which will be analyzed in regard to numerical stability, time consumption and processor load criteria

Failure detection and isolation analysis of a redundant strapdown inertial measurement unit

The objective of this study was to define and develop techniques for failure detection and isolation (FDI) algorithms for a dual fail/operational redundant strapdown inertial navigation system are defined and developed. The FDI techniques chosen include provisions for hard and soft failure detection in the context of flight control and navigation. Analyses were done to determine error detection and switching levels for the inertial navigation system, which is intended for a conventional takeoff or landing (CTOL) operating environment. In addition, investigations of false alarms and missed alarms were included for the FDI techniques developed, along with the analyses of filters to be used in conjunction with FDI processing. Two specific FDI algorithms were compared: the generalized likelihood test and the edge vector test. A deterministic digital computer simulation was used to compare and evaluate the algorithms and FDI systems

RuDaCoP: The Dataset for Smartphone-based Intellectual Pedestrian Navigation

This paper presents the large and diverse dataset for development of smartphone-based pedestrian navigation algorithms. This dataset consists of about 1200 sets of inertial measurements from sensors of several smartphones. The measurements are collected while walking through different trajectories up to 10 minutes long. The data are accompanied by the high accuracy ground truth collected with two foot-mounted inertial measurement units and post-processed by the presented algorithms. The dataset suits both for training of intellectual pedestrian navigation algorithms based on learning techniques and for development of pedestrian navigation algorithms based on classical approaches. The dataset is accessible at http://gartseev.ru/projects/ipin2019

Step Characterization using Sensor Information Fusion and Machine Learning

A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications

Feature Guided Image Registration Applied to Phase and Wavelet-Base Optic Flow

Optic Flow algorithms are useful in problems such as computers vision, navigational systems, and robotics. However, current algorithms are computationally expensive or lack the accuracy to be effective compared with traditionally navigation systems. Recently, lower accuracy inertial navigation systems (INS) based on Microelectromechanical systems (MEMS) technology have been proposed to replace more accurate traditional navigation systems

Deep Integration of INS and DP: from Theory to Experiments

The recent progress of measurement devices and algorithms of inertial navigation opens up the perspective of deep integration between inertial navigation systems (INS) and dynamic positioning (DP) systems. In the literature, novel mathematical algorithms for INS-guided sensor fusion and sensor fault isolation have recently been proposed, aimed primarily at robust and resilient observation of the vessel’s attitude and position. Much less has been done for experimental testing of INS-guided DP systems in real environmental conditions. In this paper, we report experimental results (from bench tests to real sea trials), demonstrating the efficacy of a fiber-optic inertial measurement sensor in a real DP system. Besides this, we discuss some new applications of INS systems in DP operations and relevant mathematical algorithms