Image stabilization technology based on inertial sensors and piezoelectric fast pendulum mirrors

AlliedSignal Defense & Space Systems is ‘developing a miniaturized low-cost Inertial Measurement Unit (IMU) with tactical performance for missiles and munitions and for commercial applications. Many technology solutions are available that satisfy performance. This IMU design architecture ias established’ addressing cost as the primary performance parameter and then selecting the best technical performance solution to meet the cost goals. The, salient feature of the IMU is the use of two new inertial sensors designed primarily to significantly reduce cost through the use of micro-machined optoelectronic systems (MOES) for the gyro and’ micromachined electromechanical systems (MEMS) accelerometer technology. The SiMMA, Silicon Micro-Machined Accelerometer, provides the first product launch of tactical and inertial grade precision accelerometers. The SiFORS, Silicon Fiber-Optic Rotation Sensor, is an all silicon integrated optics gyro with fiber-optic sensing coil. ~The focus of theis, paper will be on the SiFORS gyro. In this paper we will be describe the design architecture and error budgets and present validating performance.’ 1. SiFORSTG IMU Introduction ’ AlliedSignal has developed a highly integrated very low cost Inertial Measurement Unit (IMU) based on Silicon integrated optics Fiber Optics Rotation Sensor’ (SiFORS) using MOES, Micro-machined OpticalElectronic Systems, and MEMS accelerometer technologies. The SiFORS IMU is environmentally robust and is currently, used in gun-hardened applications. The SiFORS lMU provides 600 Hz ,body rate and acceleration autopilot information and generates accurate. 100 Hi ‘measurements of angle (A@) and velocity (AV) for navigation applications. Its programmable data format and scaling capabilities allow a wide range of inertial navigation applications with minimum ‘design effort. Extensive performance 1 American Institute of Aeronautics and Astronautics 3 evaluations have been performed and the results are in close agreement with the predicted performance envelope. 1.1. Accelerometers in SiFORS IMU L Many solutions are available that satisfy the role of the accelerometer for all IMU applications and the. modular architecture of the’SiFORS lMU provides the flexibility for accelerometer selection. AlliedSignal’s RRA500 is a good accelerometer choice for the l-5 mg bias stability ‘range and the AlliedSignal SiMMATM, Silicon Micro-Machined Accelerometer, a. developing MEMS device, will soon be available in this performance range to further lower the IMU price. The gun-hardened system uses the Fndevco 7591 that provides, 10 mg performance after 12,000g launch shock. 1.2. Focus on SiFORS Rotation Sensor In this paper, we focus on the development and performance of the SiFORS, Silicon Fiber Optic Rotation Sensor.’ This sensor has a unique design employing the best features of both fiber optics and MOES processing. A key component of the SiFORS rotation sensor is the silicon integrated optics MultiFunction Chip (MFC), which provides the optical ( bench and low noise electronic detection capabilities for this Fiber Optic Gyroscope (FOG). An important feature of FOG technology is its performance scalability. The well understood FOG theory has facilitated performance predictions during ,.’ our SiFORS design and development phase. It also provides a low risk approach to developing performance’grade levels for various applications with the benefit being the lowest cost IMU for each performance grade. Since the entire MFC is fabricated using silicon technology, it can be produced with very high yield and is therefore ‘ideally suited for high volume, .low ‘. Copyright

Fiber optic gyroscopes (FOGs) are vital components for manned, unmanned, and autonomous platforms, as well as applications requiring extremely accurate data for stabilization, pointing, orientation of payloads, and navigation and control. FOG-based inertial systems also provide highly accurate data for all types of navigation, positioning, and georeferencing systems. Substantial improvements in the performance of FOGs and FOG-based inertial systems at KVH are directly related to advancements in the design and manufacture of optical fiber, as well as in manufacturing operations and signal processing. Open-loop FOGs, such as those developed and manufactured by KVH Industries, offer tactical-grade performance in a robust, small package. The success of KVH FOGs and FOG-based inertial systems is due to a series of key innovations including the development of proprietary D-shaped fiber with an elliptical core, as well as KVH's ThinFiber. KVH continually improves its FOG manufacturing processes and its signal processing, with the goal of improving accuracies across its entire FOG product line. KVH acquired its FOG capabilities, including its patented E·Core® fiber, when the company purchased the assets of Andrew Corporation's Sensor Products Group in 1997. E·Core fiber is unique in that the light-guiding fiber core — critical to the FOG's performance — is elliptically-shaped. The elliptical core results in an optical path with low loss and high polarization-maintaining ability. In 2011, KVH developed its ThinFiber, a 170-micron diameter dual acrylate-coated fiber that retains the full performance characteristics of E·Core fiber. ThinFiber has enabled the development of compact, high-performance open-loop FOGs, which are also used in a line of FOG-based inertial measurement units (IMUs) and inertial navigation systems (INS).

A platform inertial platform system (IPS) is a kind of independent navigation system. A fiber optic gyro (FOG) based IPS can stabilize its inertial measurement unit (IMU) in inertial space using its FOG readouts, and calculate its carrier's real-time displacement and velocity using its accelerometer readouts. Alignment is the initialization process of the IPS. During the coarse alignment of the FOG based IPS, the instrument errors, random noise and base vibration can cause decline of the alignment accuracy. To improve the precision of coarse alignment, the IMU rotation modulation is used to suppress the errors of inertial instruments by utilizing the equivalent integration process contained in the averaging computation. On the stationary base, calculate the attitude matrix of the rotating IMU in each sampling period, and averaging computation can compensate the errors caused by biases of inertial instruments. When the carrier is in vibration condition, the alignment coordinate frame (CF) is introduced to assist the rotation modulation. Carry out the attitude updating within the alignment CF in each sampling period to correct the estimated attitude matrix, and the rotation modulation based method can still improve the precision of coarse alignment in vibration condition.

High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers' read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers' colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10-7 m s-2 and 10-5 m s-2 (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is 10-5 rad s-1 and 5×10-4 rad s-1. Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, 5×10-11 m s-2, linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around 10-10 rad s-1 at one second and 5×10-7 rad s-1 at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled.

Gyroscopes with high reliability and long lifespan are very important to the development of high quality space aerocrafts. In this article, various redundant configurations of fiber optic gyroscope (FOG) inertial measurement unit (IMU) is proposed. The reliability of these different configurations is studied. Moreover, this article analyses the error judgement rule when several FOGs work at the same time. Finally, the issues of measurement precision of different redundant configurations methods are discussed.

Six-degree of freedom (DOF) inertial measurement units (IMUs) are widely used for attitude estimation. However, such systems' accuracy is limited by the accuracy of calibration of bias, scale factors, and non-orthogonality of sensor measurements. This paper reports a stable adaptive estimator of measurement bias in six-DOF IMUs and preliminary simulation results employing a commercially available IMU comprising a 3-axis fiber optic gyroscope (FOG) with a 3-axis micro-electro-mechanical systems (MEMS) accelerometer. A stability proof of the adaptive estimator and preliminary numerical simulation results are reported. The simulation results for a rotating IMU configuration are promising, and further experimental evaluation and extension of the algorithm for the case of a translating IMU configuration typically found on moving robotic vehicles are needed.

Camera positioning units for surveillance applications are often mounted on mobile supports or vehicles. In such circumstances, the motion of the supporting base affects the camera field of view, thus making the task of pointing and tracking a specific target problematic, especially when using low cost devices that are usually not equipped with rapid actuators and fast video processing units. Visual tracking capabilities can be improved if the camera field of view is preliminarily stabilized against the movements of the base. Although some cameras available on the market are already equipped with an optical image stabilization (OIS) system, implemented either in the camera lenses or in the image sensor, these are usually too expensive to be installed on low-end positioning devices. A cheaper approach to image stabilization consists of stabilizing the camera motion using the motors of the positioning unit and the inertial measurements provided by a low-cost MEMS Inertial Measurement Unit (IMU). This paper explores the feasibility of applying such image stabilization system to a low cost pan-tilt-zoom (PTZ) camera positioning unit driven by hybrid stepper motors (HSMs), in order to aid the task of pointing and tracking of a specific target on the camera image plane. In the proposed solution, a two-level cascaded control structure, consisting of inner inertial stabilizing control loop and an outer visual servoing control loop, is used to control the PTZ unit. Several tests are carried out on a real device mounted on a moving table actuated by a 6 degrees-of-freedom pneumatic hexapod. Realistic motions are recreated by using the data recordings taken aboard of a patrolling ship.

The scheme based on the inertial measurement of measurement while drilling (MWD) is the developing direction of MWD, but the gyroscope is the bottleneck which arbitrarily restricts the application of the scheme. This paper discusses on the Inertial Measurement Unit (IMU) consisting of three fiber optic gyroscope and three accelerometers, as well as the measurement while drilling algorithm based on the IMU. In the structural design of this system, IMU rotating framework is utilized to modulate the output signal of the FOG, which will reduce the constant drift of gyroscopes and improve the accuracy of the attitude angle and well trajectory. What's more, the paper simulates the MWD algorithm with and without the rotating modulation, and proves the accuracy and effectiveness of the algorithm with the rotating modulation.

Inertial navigation provides a unique ability to know where one has been, where one is currently, and where one is going, given only a starting position. The laws of physics permit the sensing of dynamic motion without external information, making inertial systems impervious to jamming, masking, or spoofing. Measurements of six degrees of freedom are required - three linear accelerations, and three angular rates - to fully propagate the velocity, position, and orientation of the system. The first inertial sensors are traced to the early 19th century and specialized inertial guidance systems appeared in the 1940s, yet inertial navigation systems did not become commonplace until the 1960s. This is largely due to the fact that requirements for navigation accuracy inertial sensors - accelerometers and gyroscopes - are very challenging. In the past fifty years, significant evolutionary and revolutionary changes have taken place in the designs of inertial sensors and systems. These include the progression from fluid-filled to dry instruments and the transition from mechanically complex stabilized inertial platforms to computationally intensive strapdown systems. Gyroscopes have evolved from large mechanical devices to highly refined precision mechanical sensors. Optical rotation sensors such as the ring laser gyro and the fiber optic gyro have enabled new system designs and capabilities. Coriolis vibratory gyroscopes such as the hemispherical resonator gyro are capable of extreme accuracy and reliability; new opportunities for miniaturizing these types of sensors will lead to new classes of accuracy for inertial navigation systems. Advanced gyroscope technologies such as the nuclear magnetic resonance gyroscope which uses atomic spin to detect rotation have already been demonstrated to achieve navigation accuracy requirements. Cold atom technologies may also provide the opportunity for very high accuracy accelerometers and gyroscopes in the future. Inertial navigation technologies and applications of the past, present, and future are discussed.

This paper will focus on the technology trends for inertial sensors, GPS accuracies, and integrated GPS/INS systems, including considerations of jamming, for military platforms and weapons, that will lead to one meter accuracy, global, navigation systems of the future. For inertial sensors, trend-setting sensor technologies applicable to military systems will be described. They are: fiber-optic gyros, silicon micromechanical gyros, resonating beam accelerometers, and silicon micromechanical accelerometers. A vision of the inertial sensor instrument field, and inertial systems for military applications for the next few decades will be given. GPS specified and observed current accuracies will be described, as well as, planned accuracy improvements due to various stages of the WAGE implementation, intersatellite ranging, and all-in-view tracking. Uses of relative and differential GPS will be discussed. The trend towards tightlycoupled GPS/INS, where both code and carrier tracking loops are aided with inertial sensor information, will be described and the synergistic benefits explored. Some examples of the effects of jamming will be described and expected technology trends to improve system antijam capability will be presented.

Research at the University of Michigan's Mobile Robotics Lab aims at the development of an accurate Proprioceptive (i.e. without external references) Position Estimation (PPE) system for planetary rovers. Much like other PPE systems, ours uses an Inertial Measurement Unit (IMU) comprising three Fibre Optic Gyroscopes (FOGs) and a two-axes accelerometer, as well as odometry based on wheel encoders. Our PPE system combines data based on expert rules that implement our in-depth understanding of each sensor modality's behaviour under different driving and environmental conditions. Since our system also uses Fuzzy Logic operations in conjunction with the Expert Rules for finer gradation, we call it Fuzzy Logic Expert navigation (FLEXnav) PPE system. The paper presents detailed experimental results obtained with our FLEXnav system integrated with our planetary rover clone, Fluffy and operating in a Mars-like environment. In addition, we compare the results of our FLEXnav system with the results obtained from a conventional Kalman Filter (KF). The paper also introduces new methods for wheel slippage detection and correction, along with comprehensive experimental results.

This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.

High performance inertial sensors based on Micro-Electro-Mechanical Systems (MEMS) have been progressively replacing quartz accelerometers, mechanical gyroscopes (gyros) and even Fiber Optic Gyros in navigation and positioning systems, at least in the less demanding applications. But until now, the IMU/INS system manufacturers usually had to interface two different types of sensors, with different communication protocols (Analog or SPI/ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IC or RS for digital), two different type of packages, that were often bulky metal housing with pins or connectors and not compatible with standard Surface Mountable Device (SMD) assembly, and two sets of passive components. This was resulting on a complex and expensive integration and still rather large Inertial Measurement Units (IMU). In this paper, we present a technical approach selected to achieve higher performances SMD MEMS accelerometer and gyro sensors built with the same package and the same digital SPI interface to reduce the bill of material and simplify the sensor integration and thus reduce the effort and duration of the development of MEMS IMUs. It is based on a generic Sigma-Delta closed-loop capacitive MEMS architecture for both the accelerometer and the gyro combined with a hermetic J-lead ceramic package compatible with SMT assembly.

In recent years, with the progress of society and the development of the economy, the number of cars in China has been increasing. In wireless communication networks, the choice of wireless nodes has a greater impact on the improvement of system performance (such as channel capacity, coverage area, etc.). The intelligent vehicle target detection system can perceive and recognize the surrounding objects such as pedestrians and vehicles through sensors, which is the basis for realizing the unmanned driving of intelligent vehicles. In a wireless environment where multiple wireless nodes coexist, current research focuses on how long it takes to re-plan the selection of wireless nodes (for mobile environments) and how to allocate and manage wireless nodes, such as where under the conditions of wireless, who will wireless with whom, and in what way (such as auto focus, digital fusion, or other) wireless, etc.; the dedicated centralized controller (such as the base station of the infrastructure-based wireless access network) determines the wireless partner (i.e., centralized type selection), or the wireless node decides by itself (i.e., distributed selection); select the appropriate number of wireless nodes to take into account the system performance gain and implementation complexity. The popularity of automobiles has brought great convenience to people's lives, but it has also brought greater traffic pressure. At present, the amount of domestic automobile traffic has increased exponentially, and urban traffic congestion has become more serious, and even caused serious traffic accidents, directly affecting people's quality of life. The emergence of intelligent transportation systems has effectively alleviated road traffic pressure and reduced the incidence of traffic accidents. As an important part of the intelligent transportation system, the research of intelligent vehicles has received extensive attention. Intelligent vehicles use a variety of sensors installed on the body to sense the environment, and realize intelligent driving functions such as lane line detection, obstacle detection, dynamic cruise control, and unmanned driving, which is conducive to reducing the incidence of traffic accidents and improving the safety of vehicle driving. At present, in the research of target recognition and tracking of intelligent vehicles, the traditional target detection method is mainly based on artificial feature extraction, which is difficult to describe more complex or higher-order image features, and the tracking effect is not good, which limits the target detection. An intelligent vehicle is an intelligent system with functions such as environment perception, planning decision-making, and operation control, and is an important part of the intelligent transportation system. Identify the effect. In response to these problems, we present a study of the target recognition and tracking of intelligent vehicles based on grid map and lidar sensor technology. To verify the proposed method in the actual scene, we identify and track the vehicle ahead on the road through a car equipped with a lidar sensor. The meaning of target detection: target detection, also called target extraction, is an image segmentation based on target geometric and statistical features. The results show that the proposed research method can accurately identify the vehicle in front and detect the moving targets such as pedestrians. The tracking trajectory is also consistent with the expected route. The results show that the laser radar-based vehicle target recognition and tracking in this study is effective. In the 10 frames of images, the average time taken for the recognition of one frame of image is 9.8 ms, while the traditional method is 12.9 ms. Technology can improve reliable road information for intelligent vehicle driving and improve the transportation efficiency and safety performance of intelligent vehicles. There are two main target detection methods for lidar: detection methods based on feature extraction and detection methods based on grid maps.

This paper presents field tests of challenging flight applications obtained with a new family of lightweight low-power INS/GNSS (inertial navigation system/global satellite navigation system) solutions based on MEMS (micro-electro-mechanical- sensor) machined sensors, being used for UAV (unmanned aerial vehicle) navigation and control as well as for aircraft motion dynamics analysis and trajectory surveying. One key is a 42+ state extended Kalman-filter-based powerful data fusion, which also allows the estimation and correction of parameters that are typically affected by sensor aging, especially when applying MEMS-based inertial sensors, and which is not yet deeply considered in the literature. The paper presents the general system architecture, which allows iMAR Navigation the integration of all classes of inertial sensors and GNSS (global navigation satellite system) receivers from very-low-cost MEMS and high performance MEMS over FOG (fiber optical gyro) and RLG (ring laser gyro) up to HRG (hemispherical resonator gyro) technology, and presents detailed flight test results obtained under extreme flight conditions. As a real-world example, the aerobatic maneuvers of the World Champion 2016 (Red Bull Air Race) are presented. Short consideration is also given to surveying applications, where the ultimate performance of the same data fusion, but applied on gravimetric surveying, is discussed.