Chapter 1
Introduction
The second half of the twentieth century is marked not only by social changes; but also intensive and thorough investigations in fundamental and applied science. Two aspects are to be of the paramount importance in the field of navigation. First, is in creation of global satellite navigation systems, such as Transit, GPS; second, is the advent of a new generation of inertial navigation sensors -laser and fiber optical gyros (LG and FOG). These optical devices, making use of fundamental properties of electromagnetic waves, provide a basis for a new line of investigations in inertial navigation and make it possible to give up the fast rotating rotor. This situation opens a possibility to use new progressive technology in mass production. Exceptional properties of optical gyros like high accuracy, wide dynamic range, non-sensitivity to linear acceleration etc, stimulated the evolution of various highly accurate strap down inertial navigation systems (SINS). Integration of SINS based on optical gyros with Satellite Navigation Systems, such as GPS, allows new positive properties necessary for dual usage in military and commercial technology.
There is an increasing demand for accurate, yet low-cost and highly reliable guidance, control, and navigation systems for measuring the direction and altitude of an object. Gyroscope is the core component for providing this information. Although different type of gyroscopes are available, Fiber Optic Gyroscope (FOG)[4] is a proven technology for measuring angular velocity of an object .It has the advantages of low reaction time, wide dynamic range, high accuracy and reliability. The basic operational principle of FOG is that the optical path difference induced by counter propagating beams in a rotating reference frame is proportional to the absolute rotation rate (Sagnac effect).
The advantages of a fiber gyroscope over a mechanical gyroscope are that there are no moving parts, no warm-up time is required, and there is no sensitivity to acceleration. They also promise to be low in cost. The required accuracy for a gyroscope depends on the application. Short-f1ight missile and automobile applications can probably be satisfied with an accuracy or 10 degree per hour. To appreciate this term, consider that the earth rotates at 15 degree per hour, and the hour hand on a clock rotates at 30 degree per hour. Both of these are considered to be relatively large rotation rates from a gyroscope’s point of view. Gyroscope is the most important and critical component of the INS.
1.1 GYROSCOPE HISTORY
The advent of the fiber optic gyroscope (FOG) dates back to the mid-1970s when Vali and Short hill demonstrated the first fiber optic rotation sensor. This breakthrough followed the pioneering efforts of R. B. Brown from the Navy Laboratory in 1968, who proposed a coil of optical fiber as a rotation sensor. Fringes were demonstrated in an optical fiber ring interferometer in 1975 using low-loss, single mode fiber. During the years to follow, a number of researchers and developers worldwide made the FOG concept become a reality. A number of universities and industrial laboratories such as McDonnell Douglas, Northrop-Grumman (Litton), Honeywell have investigated the FOG. Gyroscope bias errors of 0.01°/hr were being achieved in the laboratory by the early 1980s.
The development of the FOG has flourished during the past 30 years. It has evolved from a laboratory experiment to the production floors, and thus into practical applications such as in navigation, guidance, and control of aircraft, missiles, automobiles, robots, and spacecraft. A great deal of effort has been made in the development of navigation-grade gyroscopes for aircraft and space applications with bias drift less than 0.01°/hr and scale factor of less than 10 parts per million (ppm). FOGs are currently used in the navigation system of aircraft such as the Boeing 777.
1.2 FIBER OPTIC GYROSCOPE
Fiber optic gyroscope works on the principle of Sagnac effect. That stated as “Two counter propagating coherent light waves exhibit a relative phase difference on a complete trip around a closed path”.
The schematic of fiber optic gyro is as shown in fig, here the source is Super Luminescent Diode (SLD), and the receiver is photo diode (PD). the light wave generated by SLD is passing through optical fiber to fiber polarizer to maintain the polarization of light wave in single direction, the polarized light is given to 3-db coupler to split the light wave in to two equal intensity light waves, the splitted light waves are passing through closed loop optical fiber which is in circular shape. Hence light is travelled in opposite direction and the two light waves are collected at photo diode.
When GYRO there is no rotation the two light waves have the same path length, hence there is no phase difference in the received light waves. It will get zero rotation rates. If the gyro rotate in clock wise direction the light wave in the clockwise direction will go further than the light travelled in the anti clock wise direction. This will cause a very small difference in the time of the received light beams as shown in fig 1.2. This can be detected and translated into a measurement of how far the sensor has turned.
Fig 1.1: Simple Fiber Optic Gyroscope.
The precision of the FOG sensor depends on the bias drift and noise in the measurement. FOG sensor has mainly two types of errors
1. Deterministic error
2. Stochastic error
Deterministic errors are due to the scale factor, bias and misalignment which can be eliminated by suitable calibration techniques in the laboratory environment. However, stochastic errors are due to the environmental temperature changes, electronic components and, other electronic equipment interfaced with it, it is difficult to eliminate these errors by calibration. Thus stochastic models are required to characterize these errors and signal processing techniques are required to suppress these errors.
Parameter Units Test data
Range o/s ±150
Bias over temperature range o/h max 50
Start up time Sec max 1
Operating temperature oc -30 to +60
Table 1.1: Specifications of High accuracy Fiber Optic Gyro
1.3 MOTIVATION
In the strap down inertial navigation system (SINS), FOG has been used for measuring the rotation angle. Recently, FOG is being widely used for military and defense applications, due to its significant advantages such as small size, low cost, light weight, no moving parts, large dynamic range, low power consumption, and possible batch fabrication. The performance of FOG degrades due to the variation in environmental factors such as temperature, vibration, and pressure. Among different types of error in the FOG signal, random drift error leads to decrease the FOG performance over a period of time.
1.4 PROBLEM STATEMENT
The various noises present in the fiber optic gyroscope signal and in order to reduce the deterministic errors like scale factor, bias and misalignment and the stochastic errors like quantization noise, angle random walk, bias instability, rate random walk , random bias drift with the help of Kalman filter. The Kalman filter Simulink model was developed and simulated for FOG based applications .The algorithms like discrete wavelet transform and Kalman filter are successfully denoise the Fog signal in steady state condition. These algorithms fail while denoising the dynamic condition FOG signal. So require an efficient algorithm that denoises the signal both in static and dynamic condition.
1.5 OBJECTIVE OF THE PROJECT
The main objective of the project work is to develop an efficient algorithm for performance enhancement of fiber optic gyroscope signal using Kalman filter, which reduces noise level in the signal and gives the best estimated output with much lower noise.
1.6 EXISTING SYSTEM
In the field of denoising FOG signal and Kalman filter many of them contributed by presenting several means of modeling different aspects of FOG signal. Mr. Huo Ju, Wang Shijing, Liu Jiantao are contributed and researched on Discrete wavelet transform in the denoising of FOG signal.
AMAMMKF (Adaptive moving average based multiple-model Kalman filter), Proposed by Dr. J. Nayak , Mr. K. P. Karthik, P. Rangababu, Samarat L. Sabat introduced an algorithm to impove the throughput of the signal. AMAMMKF is making use of adaptive moving average filter along with multiple gains Kalman filter to increase the signal quality efficiently.
1.7 PROPOSED SYSTEM
FOG drifts includes different noise sources so, effective signal processing method must be adopted to suppress the random drifts. There are two methods for signal processing direct filter method and model based compensation. In case of direct filter method the gyro signal directly including digital low pass filter, recursive weighted average filter and wavelet transform filter. Wavelet filter just can remove high frequency noise much effectively but cannot remove low frequency noise and can only show noise trends.
Existing algorithms like DWT and Kalman filter with properly tuned gain denoise the FOG signal only in the static condition is satisfied, but it fails in denoising the signal in dynamic condition. Because they fail identify the unpredictable changes in the dynamic signal.
Standard KF and multiple model KF techniques were used for noise reduction applications, but denoising still need to be improved in case of FOGs in dynamic condition. So, optimized KF parameters need to be calculated at each and every stage of modified angular rotation.
The proposed algorithm is efficiently denoise the FOG signal in both static and dynamic condition .the proposed algorithm is hybridization of Kalman filter with adaptive moving average (AMA) technique and named it as adaptive moving average dual gain Kalman filter (AMADGKF).
1.8 TOOLS REQUIRED
Operating system: Microsoft Windows XP.
Hardware: Inertial Measurement Unit, Rate table, Oscilloscope, Power supply, Display Unit, PC.
Software: Xilinx v.12, Real term Software, Turbo C, MatLab.
1.9 LITERATURE SURVEY
To do the project in a phased manner, it is necessary to conduct the literature survey. Many number of other trace back system exists, so literature survey must be done. In this section, let’s discuss selected important contribution from the existing literature.
Overview of fiber-optic gyroscopes
R. A. Bergh, H. C. Lefevre, and H. J. Shaw [1] the word gyroscope was first coined by a French scientist, Leon Foucault, in 1852. It is derived from the Greek words “gyro,” meaning revolution, and “skopien,” meaning to view. The gyroscope, commonly called a GYRO, has existed since the first electron was sent spinning on its axis. Electrons spin and show all the characteristics of a gyro; so does the Earth, which spins about its polar axis at over 1000 miles per hour at the Equator. The Earth’s rotation about its axis provides the stabilizing effect that keeps the North Pole pointed within one degree of Polaris (the North Star).
Fiber- Optic gyroscopes: from design to production
Jagannath Nayak RCI, Hyderabad [2] this paper gives importance of FOG development in the field of sensors and gives the brief explanation about the FOGs. Basic Principle of FOGs, Configuration of Fiber Optic Gyroscopes like Interferometer FOG, Resonator FOG and Fiber Ring Laser Gyro, Different error sources and analysis of noise is also explained in detail. The steps involved in development of FOG in Research centre Imarat, open-loop FOG and Closed loop FOG also explained here. There are different gyros based on the application, they are tactical gyros, navigation purpose and finally strategic gyros with varying accuracy and length of fiber.
Three types of control grade FOGs- single axis, two axis and three axis are developed and qualified for control and stabilization applications. Also gives the detail explanation of optical component development are Optical source, Fiber optic Directional coupler, Multifunction Integrated Optics Chip, Polarization Maintaining fiber, PIN detector and gyro sensor coil. The production of sensor is divided into following steps manufacturing of the optical modules, manufacturing of electronics, module assembly, integration and calibration.
Modeling and Simulation of Digital Closed Loop Fiber Optic Gyroscope
Junliang Han, Shengmin Ge, Yi Shen, Xiangium Li [3] this paper gives the dynamic model and stochastic model of digital closed loop, FOG are developed. The non linear dynamic model is simplified into a linear discrete dynamic model. The digital control algorithm in the closed loop control is analyzed. In stochastic modeling, the simulation is done to simulate the different types of gyro errors and noise. To compare the noise level before and after the signal processing Allan variance analysis method is used.
Kalman filter for denoising in IMU measurements
S.A.Quadri and Othman Sidek [6] Kalman filtering is a well-established methodology used in various multi-sensor data fusion applications. In our experiment, we first obtain measurements from the accelerometer and gyroscope and fuse them using Kalman filter in an inertial measurement unit (IMU). We estimate Kalman filter output and estimation error. The affect of process noise and measurement noise on estimation error is tested. It is explored that the measurement noise has significant role to increase estimation error in the data fusion process.
Application of wavelet transform threshold in the de-nosing of fiber optic gyroscopes
Huo Ju, Wang Shijing, Liu Jiantao[7] This paper presents quantification of different types of random errors present in the Fiber Optics Gyroscope (FOG) measured data using Allan Variance analysis and denoising of the measured data using Discrete Wavelet Transform (DWT). Allan Variance analysis is performed before and after denoising the measured data. The experimental result shows that after denoising the angle random walk is reduced and therefore sensitivity of FOG is increased.
Kalman Filtering
Dan Simon [9] the Kalman filter is a tool that can estimate the variables of a wide range of processes. In mathematical terms we would say that a Kalman filter estimates the states of a linear system. The Kalman filter not only works well in practice, but it is theoretically attractive because it can be shown that of all possible filters, it is the one that minimizes the variance of the estimation error. Kalman filters are often implemented in embedded control systems because in order to control a process, you first need an accurate estimate of the process variables.
This article will tell you the basic concepts that you need to know to design and implement a Kalman filter. I will introduce the Kalman filter algorithm and we’ll look at the use of this filter to solve a vehicle navigation problem. In order to control the position of an automated vehicle, we first must have a reliable estimate of the vehicle’s present position. Kalman filtering provides a tool for obtaining that reliable estimate.
An introduction to Kalman filters
G C Dean [10] this paper gives an introduction to Kalman filter with basic math’s and also with examples for understanding Kalman filter techniques. A Kalman filter is a method of estimating the true value of a set of variables from set of noisy measurements. Kalman filters are a powerful for reducing tool for reducing the effects of noise in measurements.