A reconfigurable navigation test bed

Over the few years civilian and military applications are maneuvering with multi-sensor Unmanned Aerial Vehicles (UAV). The aim of this paper is to improve autonomous UAV positioning by using Inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) is considered. The positional accuracy of UAV is limited to GNSS signal blockage. The advancements in micro electro mechanical systems (MEMS) helps to design low-cost IMU with meter-level accuracy in positioning. The estimation of attitude, position and velocity measurements are determined from a nine degree of freedom Arduino embedded IMU sensor. The Low-cost IMU sensor has been tested under static condition and then a dynamic movement of sensor is observed under moving base-line conditions on February 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> 2020 at Koneru Lakshmaiah Education Foundation (KLEF). In this paper, covariance filters are implemented to eliminate the gyro bias and accelerometer bias for improving attitude determination of IMU navigation system. The positioning from IMU sensor is derived using standard strap-down inertial navigation system (SINS) formulation. The position error from SINS indicates that IMU sensor is able to perform well with maximum position error of 5 meters and 2.5 meters for static and dynamic environments respectively. The outcome of this work would be useful for integration of navigation sensors using kalman filter based advanced techniques for UAV applications.

Inertial Measurement Units (IMUs) using MEMS (Micro Electro Mechanical Systems) sensors, to detect rotation and acceleration, are extremely sensitive to vibration and shock. Because these IMUs are used in aircraft, missiles, and even in gun launched projectiles, the vibration and shock levels can be extremely high. We have been successful in designing isolation systems that protect the MEMS sensors from these harsh environments. These isolation systems attenuate the shock and vibration in frequency ranges that would otherwise degrade IMU performance and even damage the sensors. In designing an isolation system, for an IMU with MEMS devices, the designer must first acquire a good definition of the shock and vibration environment to which the IMU will be exposed. Then the designer must determine the frequencies and levels to which the MEMS devices are sensitive. Finally the designer must have knowledge of IMU properties such as resonant frequencies, mass properties, clock frequencies, etc. By knowing all of this information, and how it affects IMU performance, the designer can determine the proper parameters for the isolation system. A well designed isolation system using these parameters will be capable of protecting the MEMS devices while maintaining IMU performance through out all specified environments.

Precision guided munitions (PGMs) are critical to the Maneuver Battalion Commander's success on future rapidly deployed battlefields. These PGMs will provide an increase in lethality, significantly improve survivability, reduce collateral damage, minimize non-combatant casualties, and decrease the logistics burden or increase the number of kills from the standard logistics load. PGMs all require inertial measurement sensors embodied in an IMU. Until now, they have been large, expensive, and could not readily survive the high G launch environments. MEMS technology provides inherent cost savings and size reduction and is ideal for application to navigation and control systems for small missiles and munitions. Using MEMS technology, gyroscopes, accelerometers and control electronics can readily be integrated to form a tightly packaged, low-cost, extremely small, high performance IMU suitable for munition, missile, and personnel guidance as well as other applications. This paper describes the current Army development underway and its partnership with industry to achieve the necessary requirements. The program goal is to survive a 20,000 g gun launch and meet a 1/spl deg//hr, 1/2 milli-g performance capability. The MEMS IMUs will significantly reduce the cost of precision-delivered missiles and munitions, as well as expand precision delivery capability to artillery ammunitions, and thus reduce the number of required rounds by more than 30% while meeting roughly 90% of the tactical weapons fleet navigational requirements.

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

Inertial Measurement Unit (IMU) sensors are the essential components in navigation systems. Micro-Electro-Mechanical System (MEMS) sensors are a less affordable and lightweight IMU. MEMS suffer from larger stochastic errors that accumulate over time, eventually causing drifts in navigation. The integration of Global Navigation Satellite System (GNSS) measurements with IMU data solves the issue of navigation drift and provides accurate navigation. However, this integration falls short in during GNSS outage scenarios, primarily due to persistent IMU errors. This thesis introduces an effective strategy to reduce navigation drifts by rectifying IMU errors using Machine Learning (ML) algorithms such as Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost). In contrast to many other methodologies reliant on high-end and expensive IMUs for denoising low-cost MEMS IMUs, this thesis proposes Inverse Kinematics (IK). The proposed method undergoes testing in Loosely coupled and Tightly coupled schemes under varying GNSS outage durations.

A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.

Modern navigation solutions rely on a combination of inertial measurement units (IMUs) and a global navigation satellite system (GNSS) receiver to estimate a navigating body's position. In order to produce a high-fidelity solution, the current approach is to utilize a single ultra-low bias navigational grade IMU in the system. However, these high-quality IMUs are expensive, bulky, heavy, and require significant power consumption. This article proposes the fusion of multiple lower-quality IMUs to achieve near-identical or better positional accuracy as a single high-quality sensor to minimize cost, size, weight, and power (C-SWaP) without sacrificing the positional estimation accuracy. The primary focus is on a generalized method to fuse multiple position estimations from multiple co-located IMUs for a single navigating body. The proposed fusion algorithm is applied to simulated data produced by three precision micro-electromechanical systems (MEMS) grade IMU modules (Analog Devices ADIS16465) from two different simulated flight paths. The absolute error is calculated between the position estimation generated by the proposed algorithm and the “truth” position provided by the simulation to determine the accuracy of the final result. The error of the proposed algorithm using the Analog Devices modules is then compared to the error between a single navigational grade IMU (NovAtel IMU-ISA-100 C) and the simulated “truth” position. The results show that using only three precision MEMS grade IMUs; the proposed method can produce identically accurate position estimations as a navigational grade IMU while drastically reducing C-SWaP. This result is further validated in measured data from an instrumented test setup using the above mentioned IMU configurations fused with a standard GPS and compared to a real-time kinematic GNSS setup used as a the third-party ground truth. In addition, the proposed method is further validated by integrating the two navigation systems with a Ku-band radar to produce synthetic aperture radar images. The image produced using the multi-IMU configuration as opposed to the navigation-grade IMU is more focused. Given these results, the proposed method has been validated in theory, simulation, and measurement, resulting in an order of magnitude reduction in cost, size, and power consumption, as well as a three-and-a-half times weight reduction of the overall IMU solution.

In the recent decades, the integration of a MEMS-based (Micro-Electro Mechanical Systems) Inertial Measuring Unit (IMU) with a GNSS receiver-chip has become commonly used navigation techniques by virtue of their advantages such as their complementary characteristics overcome each other's shortcomings, and small sized, light weight, with low power consumption, and have extremely low cost. However, when there is no external auxiliary information, the inertial navigation error based on low-cost MEMS IMU will quickly accumulate over time. Reducing inertial navigation error has great potential in many applications. Conventional ground vehicles are restricted by Non-Holonomic Constraint (NHC), during the outage of GPS. Although the navigation performance of the inertial system with only NHC as the motion constraint is improved, the observability of the system is still poor due to the lack of forward speed constraint. According to MEMS IMU, an innovative method is proposed to improve the observability of the inertial navigation system through continuous rotation of a single MEMS IMU around a certain axis, during the outage of GPS. In this paper, although the azimuth error is still unobservable, both theoretical analysis and the results indicate that the system observability is improved by rotating IMU. The enhanced estimability of the gyro bias in the vertical direction can effectively mitigate the azimuth error accumulation.

In the ultra-tight integration, the signal processing of GNSS receiver baseband can be enhanced by the aiding of INS information. With the purpose of improving the performance of ultra-tight integration with low-cost MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit), the paper explores a deeply coupled integration scheme of GNSS and the software defined IMU (SDI) based on vibratory MEMS rate gyroscope. The error of vibratory MEMS rate gyroscope demodulated by peak detection technique is interpreted in the gyroscope signal domain and the gyroscope demodulation parameter deviations are estimated by the integration filter and adjusted accordingly. The presented integration scheme is tested by an integrated navigation system which consists of a software defined GNSS receiver, a single-axis vibratory MEMS gyroscope GI-CVG-N2100A based SDI and an Xsens MEMS IMU. The single-axis vibratory MEMS gyroscope GI-CVG-N2100A in the test, is employed to replace one of the gyroscope axes of the Xsens MEMS IMU.

According to a small, lightweight, low-cost high performance inertial Measurement Units(IMU), an effective calibration method is implemented to evaluate the performance of Micro-Electro-Mechanical Systems(MEMS) sensors suffering from various errors to get acceptable navigation results. A prototype development board based on FPGA, dual core processor's configuration for INS/GPS integrated navigation system is designed for experimental testing. The significant error sources of IMU such as bias, scale factor, and misalignment are estimated in virtue of static tests, rate tests, thermal tests. Moreover, an effective intelligent calibration method combining with Kalman Filter is proposed to estimate parameters and compensate errors. The proposed approach has been developed and its efficiency is demonstrated by various experimental scenarios with real MEMS data.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

The NewSpace landscape for microlaunchers is highly competitive. The cost is one of the main drivers in this competition. Several microlauncher companies have decided to outsource the GNC sub-system to external providers, procuring low-cost commercial-of-the-shelf solutions. Lately, Astos has been involved in several activities related with GNC for (micro-)launchers. Astos’ work has been focused on providing GNC development tools and flexible and easily reconfigurable GNC software that can be applied to a wide range of launch vehicles. Navigation is one of the main drivers for launcher orbital injection accuracy and it is also one of the main drivers for cost in the GNC sub-system. Usually, this cost is driven by the Inertial Navigation System (INS) or Inertial Measurement Unit (IMU), in which expensive technologies are employed such as ring-laser gyroscopes. In traditional inertial propagation algorithms, errors are accumulated due to integration resulting in a steady divergence of the position and attitude estimate. Therefore, the performance of the navigation mainly depends on the performance of the IMU. This makes an investment into a highly accurate inertial sensors, especially the gyroscope, necessary to meet the navigation requirements. Lately, the use of hybrid navigation solutions that fuse the inertial measurements with Global Navigation Satellite Systems (GNSS) measurements has diminished, in some degree, the reliance of the navigation algorithm on the IMU performance. Although this is true for translational states, as position and velocity, the same cannot be said with the same degree of validity for attitude determination and, more specifically, for roll angle estimation. One possible way to reduce the cost of inertial sensors is by using microelectromechanical systems (MEMS). The advantages of using this technology, apart from the large reduction in cost, are the significant saves in mass and power required to operate these sensors. The disadvantage is clearly the lower performance, especially in high-vibration environments such as encountered in launcher vehicles. Lately, efforts to improve the performance of MEMS navigation solutions were driven by the demand of industries such as the wearables and unmanned aerial systems. This led to the development of solutions that utilize multiple low-cost inertial measurement units. By that, the hardware cost is not gravely increased in exchange for higher navigation accuracy, reliability, and redundancy. In this paper, a navigation algorithm is proposed that incorporates the measurements from multiple low-grade MEMS IMUs. The algorithm implements parallel Extended Kalman Filters (EKF), one for each IMU, combining the inertial measurements with the position, velocity, and time solutions provided by a single GNSS receiver. The algorithm has a loosely coupled architecture and the inertial propagation is regularly reset using the fused estimates of the vehicle’s state and IMU biases (closed-loop implementation). The navigation solution, providing estimates of position, velocity, and attitude, is then a combination of the individual EKF results through weighted means based on the covariance of the individual estimates. This formulation utilizing parallel filters is called in the literature as Federated Kalman Filter architecture. An analysis will be presented relating the performance of the navigation architecture with the number of sensors used and their distribution on the launch vehicle’s body. The assessment is performed with relation to a baseline navigation architecture also implementing a loosely coupled, closed-loop, hybrid structure but employing a single low-grade IMU. The analysis will determine the most promising sensor constellation, based on navigation performance, cost and implementation complexity. The selected multiple IMU solution is then compared with two different navigation architectures: Strapdown inertial propagation utilizing a single high-grade IMU; and hybrid navigation scheme implementing a loosely coupled, closed-loop architecture with a medium grade IMU. The performance evaluation is conducted utilizing simulated measurements retrieved from high-fidelity 6 degrees-of-freedom non-linear simulator of a representative microlauncher scenario. The simulators’ dynamics, kinematics, environment, and sensor modelling are provided by the ASTOS tool. Astos intends to further study the feasibility and performance of the proposed Inertial/GNSS hybrid navigation architecture with multiple IMUs. In the roadmap for raising the algorithm’s technology readiness level, more advanced simulations are foreseen, with improved sensor error modelling and previously unmodelled dynamical effects such as flexibility and sloshing. Additionally, processor- and hardware-in-the-loop tests with real sensor data are planned.

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most commonly used navigation systems. They both have unique advantages and disadvantages. GNSS is capable of generating precise navigation solutions while enough satellites are in view. However, the GNSS signals are sensitive to the environment. While the signals is attenuated, the GNSS receiver will fail to provide reliable navigation solutions. INS is an advanced navigation system built based on Newton's law. Due to the random noises contained in the Inertial Measurement Unit (IMU), the INS navigation solutions errors diverge over time. Therefore, effective integration of the two common systems can obtain better navigation results than any individual system. In this paper, for enhancing the GNSS/INS tightly integration system with low cost, multiple receivers were employed in the tight integration. Pseudo-range and Pseudo-range rates from the multiple receivers were employed to compose the measurement vector of the integration filter. In order to reduce the computation load, a measurement difference method was proposed. The state vector dimension could be reduced with the measurement difference scheme. Both simulation and field test were carried out for evaluating the performance of the proposed method. Since it was hard to obtain GNSS raw measurements from commercial receivers, self-developed DSP+FPGA (Digital Signal Processor, DSP; Field Programmable Gate Array, FPGA) based GNSS receivers were employed in the field test. The statistical analysis of the results showed that the positioning errors decreased with the receiver's amount increasing. In addition, a measurement difference method was proposed to reduce the state vector dimension for saving the computation load with the identical navigation solutions accuracy.

Past and current research efforts initiated in the Government and industry for the development of gyroscopes for tactical weapon system applications are discussed in this paper. Spinning mass gyroscopes were used in the early long-range missiles for precision guidance. During the past two decades, tremendous progress has been made in advancing the performance capability of solid-state optical gyroscope for use in tactical weapon systems. A number of Army missile systems currently use ring laser gyroscopes as part of their navigation systems. A few years, ago, the Government awarded several contrasts to industry to develop small, reliable, low-cost fiber optic gyroscopes (FOG) that can operate in military environments. Recently, the Government awarded several contracts for the development of rugged, micro electromechanical systems (MEMS) gyroscopes to support emerging Army missions, which are moving toward low-cost, small diameter precision guided weapons for use against light targets. The RLG, FOG, and the MEMS-based gyroscopes are traded-off in this paper with respect to cost, size and performance for applications in current and future Army missile systems. The status of ongoing MEMS projects at the US Army Aviation and Missile Command will also be discussed.

Micro electro mechanical systems (MEMS) technology has developed considerably in recent years and many sensors utilizing this technology are available in the market. MEMS technology enables miniaturization, mass production, and cost reduction of many sensors. In particular, MEMS inertial sensors that include an acceleration sensor and an angular velocity sensor (gyroscope, or simply ldquogyrordquo) are the most popular devices. Applications of inertial sensors have now extended into the field of networked sensing systems. In this presentation, we will describe current MEMS inertial sensors and their applications.