Intelligent autonomous control of representative unmanned systems, such as unmanned aircraft, unmanned vehicles, unmanned ships and robots, is a current research hotspot in the field of automatic control and a core technology for improving the autonomy and intelligence of unmanned systems.
Independent navigation technology utilizes the corresponding independent navigation system to obtain the position, speed and attitude information of the unmanned system itself, which is the basic technical guarantee for realizing the intelligent independent control of unmanned systems. Among the many navigation technologies such as radio navigation, terrain matching navigation, inertial navigation, satellite navigation, magnetic navigation, visual navigation, etc., the inertial navigation technology that does not rely on external information is the most powerful technical means to realize the autonomous navigation of unmanned systems. MEMS inertial navigation technology based on micro-electro-mechanical system (MEMS) inertial sensors is an important branch of inertial navigation technology, which has the advantages of low cost, small size, low power consumption, and strong impact resistance. Therefore, the research of MEMS inertial sensor and its navigation technology is of great significance to support the rapid development of unmanned system autonomous navigation technology and to meet its growing application demand.
1. MEMS inertial sensor
1.1 Classification of MEMS inertial sensors
MEMS inertial sensors include MEMS gyroscopes and MEMS accelerometers, which can be categorized into consumer level (zero deviation >100°/h) and tactical level (zero deviation 0.10.1°/h ~ 10°/h) according to the accuracy from low to high.
MEMS gyroscopes can be categorized into vibrating arm, vibrating disk, and ring resonance. Vibrating arm MEMS gyroscope obtains angular velocity by measuring torsional vibration amplitude and torsional vibration phase, typically representing ENV-05A series tuning fork gyroscope. Vibrating disk MEMS gyro obtains angular velocity by measuring the change in capacitance between the element and the bottom, typical of the HG1940 inertial measurement unit. Ring resonant MEMS gyro obtains angular velocity by measuring the change of magnetic field, typical representative SiIMU02 gyro.
MEMS accelerometers can be categorized into displacement, resonant and electrostatic levitation. Displacement MEMS accelerometers measure acceleration by detecting changes in capacitance, typically representing Northrop Grum ** SiACTMn Inc. Resonant MEMS accelerometers measure acceleration by measuring the change of resonant frequency, with high accuracy, typical SiMMA. electrostatic levitation MEMS accelerometers measure the position of a disk or ball in the levitation state by measuring capacitance, with high theoretical accuracy. Typical representative is SuperSTAR accelerometer of French ONERA company.
According to the sensing principle, MEMS accelerometers can be categorized into piezoresistive, piezoelectric and capacitive. Piezoresistive accelerometers can convert the resistance on the corresponding cantilever beam into a voltage output, and have the advantages of small size, simple processing, high accuracy, fast response speed, and strong resistance to electromagnetic interference. Piezoelectric MEMS accelerometer calculates the external acceleration by measuring the relationship between the change of internal piezosensitive resistance value and the measured acceleration, which has the advantages of large measuring range, small weight, small volume, strong anti-interference ability, simple structure and high measuring accuracy. Capacitive MEMS accelerometer calculates the external acceleration by detecting the change of capacitance value, which has the advantages of high measurement accuracy, high sensitivity, good stability and low power consumption.
1.2 Overview of the development of MEMS inertial sensors
Since the successful development of MEMS gyroscopes and accelerometers, with the development of MEMS technology, the performance of MEMS gyroscope and accelerometer devices has been significantly improved.
1.2.1 Foreign MEMS gyroscope development history
In 1954, C.S Smith discovered the piezoresistive effect, which provided a theoretical basis for the development of micro-pressure sensors.In 1967, the surface sacrificial layer technology was proposed, and on this basis, the development of suspended beam technology with high resonant frequency.In 1989, Draper Laboratory developed the first vibrating MEMS gyroscope, which is a major change in the field of inertial technology; in 1993, the laboratory developed a tuning fork vibrating MEMS gyroscope, a major step forward.In 1997, the first surface micromechanical Z-axis gyroscope was designed at the University of California, Berkeley, with a resolution of 1°/s.In 1999, Yokohama Technology Center presented an uncoupled design of a MEMS gyroscope with a resolution of up to 1°/h.In 2001, Draper Laboratories designed a monocrystalline silicon tuning fork MEMS gyroscope with a temperature drift to 1°/(h/°C).In 2004, HSG Germany designed a surface micromechanical X-axis gyroscope with a sensitivity of 8mv/(°/s).In 2006, K. Maenska of Hyogo University, Japan, reported a novel piezoelectric vibrating solid-state micromechanical gyroscope consisting of only a lead zirconate titanate prism with electrodes.In 2013 , a 3D capacitive tuning fork gyroscope with a horizontal suspension design was designed at the Laboratoire de l'Electronique et de l'Informatique, France.
1.2.2 Development history of foreign MEMS accelerometers
In the late 1960s, the research and development of MEMS accelerometers began, and the main research and development units were Draper Laboratories, Stanford University and the University of California, Berkeley.In the 1970s, the synthesis of the MEMS process and piezoresistive effect, piezoresistive accelerometers appeared, and for the first time the commercialization of MEMS accelerometers was realized.At the end of the 1980s, with the surface MEMS technology and sensing technology, capacitive MEMS accelerometers developed rapidly and were first applied to the automotive industry. Since the 21st century, with the rapid development of integrated circuits and the computer industry, MEMS accelerometers are more used in automotive airbags, and play an increasingly important role in cell phones, computers and other consumer electronics industry. In the future, MEMS accelerometers will develop in the direction of lightweight, high precision and economization.
1.2.3 Development history of domestic MEMS inertial devices
The research of MEMS inertial devices in China began in the late 1990s. Since 1995, it has received strong support from the Ministry of Science and Technology, the Ministry of Education and the National Natural Science Foundation of China. The development of domestic MEMS gyroscopes has achieved remarkable results.In 1998, Tsinghua University developed China's first tuning fork MEMS gyroscope with a resolution of 3 °/s.In 2006, the 49th Institute of Electronics Group and the Russian Institute of Applied Physics cooperated in the development of a 70-resolution °/h gyroscope.In 2010, the State Key Laboratory of Sensor Technology of the Chinese Academy of Sciences (CAS) reported a highly symmetric structure micromechanical vibrating ring gyroscope.In 2012, Chun-Wei Tsai et al. from National Taiwan University fabricated a dual decoupled micromechanical gyroscope with a wide driving frequency. After more than 20 years of development, the existing technology in China has formed a series of systems from design to production, and the accuracy of many famous MEMS devices in China has been significantly improved.
2. Key technologies of MEMS inertial navigation
MEMS inertial navigation system software design is mainly navigation algorithms, including initial alignment, inertia resolution and error compensation; hardware design mainly includes circuit and structure design, selection of inertial navigation sensors (gyroscopes, accelerometers) and navigation computers. The system accuracy is not only related to hardware, but also to software. Under the premise of slow development of hardware processing technology, the error compensation algorithm is particularly important in the system. For applications requiring high navigation accuracy, the MEMS inertial navigation error is easily dispersed because the system is characterized by long-term navigation, and the inertial navigation system error dispersion is mostly suppressed by combined navigation. This section focuses on the error analysis and compensation of MEMS inertial sensors, and the design of MEMS combined navigation algorithms.
2.1 Error analysis and compensation of MEMS inertial sensor
The inertial sensor is the core component of the inertial navigation system, and its accuracy determines the accuracy of the inertial navigation system, so one of the main tasks of the inertial navigation system is to compensate the error of the inertial sensor. There are roughly two ways to improve the accuracy of the inertial navigation system. One is to improve the accuracy of the inertial sensor, but the method is technically difficult and requires high processing conditions and materials; the other is to use error compensation to compensate for the system error.
The error analysis and compensation methods of MEMS inertial sensors are roughly divided into three kinds: the first is the error compensation algorithm through algorithm fitting, the second is to rotate the IMU (Inertial Measurement Unit) and the rotating mechanism by rotating the rotating modulation technique to eliminate the constant value error (called rotating modulation); the third is to use Allan ANOVA to compensate for the random error of the system.
2.1.1 Inertial sensor temperature error compensation technology
The temperature-induced accuracy error of the inertial device mainly comes from the sensitivity of the inertial device itself to temperature and the influence of the temperature gradient or the cross product term of temperature and temperature gradient. With the change of temperature, due to thermal expansion and cold contraction, the structural material of the inertial device will form an interference torque, so it is necessary to study the temperature characteristics of the inertial device in order to obtain the law of temperature on the output performance of the inertial device, and the establishment of the static temperature model of the accelerometer to compensate for the error caused by the change of temperature, which is an effective means to improve its accuracy.
Generally, the least-squares method is used to fit the static temperature model of gyroscope and accelerometer to obtain the relationship between the coefficients of the mathematical model of gyroscope and accelerometer and the temperature, and to establish the static temperature error compensation model, so as to improve the accuracy of the device. Many domestic production units of gyroscopes and accelerometers have studied temperature error compensation to reduce the static error of the product to the order of magnitude before compensation.
2.1.2 Rotary modulation technology for inertial sensor constant value drift error
Static gyro systems initially used rotational modulation techniques to automatically compensate for drift error torque by case rotation. Since the emergence of laser gyro, the United States quickly carried out research on rotary inertial navigation systems.In 1968, some scholars first proposed to compensate the drift error of inertial sensors by rotating the IMU.In the 1970s, the rotary technology was used to develop the electrostatic gyro detector, so that the supporting ship system has long-term accuracy performance.In the 1980s, Sperry developed a single-axis rotary In the 1980s, Sperry developed a single-axis rotary inertial navigation system with a classic single-axis, four-position forward and reverse stopping scheme, which is still widely used today.In 1989, the MK49 dual-axis rotary laser gyro inertial navigation system was the standard inertial navigation system for NATO ships, and was fitted to submarines and surface ships. The application of rotary modulation technology on optical gyro was firstly started in National Defense University of Science and Technology in China. At present, the rotary modulation technology mainly adopts the single-axis rotation scheme on MEMS, and the dual-axis rotation scheme is less applied due to the complexity of the rotation mechanism.
Due to the need for rotation, the navigation system uses the Jetlink algorithm. In principle, the rotational modulation of MEMS inertial navigation system can effectively counteract the system constant value error. The system error transmission equation is as follows:
In Equation (1), the system errors caused by the measurement errors of the gyroscope and accelerometer themselves are σωbib and σfb, therefore, the two errors of CNB in style σωbib and Cnbσfb are introduced by the measurement errors, so the error compensation is mainly to compensate for these two errors. Since both of these items include CNB, these two errors can be eliminated by changing the CNB value periodically. Therefore, the rotary device is applied to the inertial navigation system to cancel the periodic errors by rotation. This is the principle of rotary modulation technique to improve the accuracy of inertial navigation systems.
The rotational modulation scheme requires the determination of parameters such as the number of rotational axes (single, dual, or multiple), rotation rate, rotational angular acceleration, stopping time, and number of stopping positions. Different rotational stopping schemes under static and dynamic bases affect the rotational modulation effect.
2.1.3 Alllan variance analysis of random errors in inertial sensors
Time series analysis, Allan variance and power spectral density analysis are currently commonly used methods for modeling random errors.
Since the error equation for inertial navigation is based on the error being white noise, in fact, various noises contained in the output data of MEMS inertial devices can interfere with the system and lead to random errors in the calculation results. The random noise in the gyro output value error needs to be modeled to compensate for it, and the Allan ANOVA method is currently one of the most widely used methods in random noise analysis.The random errors in MEMS devices are mainly divided into angular random wander, acceleration random wander, quantization noise, and zero-bias stability.
The Allan method was proposed by DavidAlan in 1966, which is mainly used to analyze the oscillator phase and evaluate the frequency stability.The Allan variance can reflect the ups and downs of the average frequency difference between two consecutive sampling ranges. The estimation formula of Allan variance based on phase data and frequency data is
2.2 MEMS Combined Navigation Algorithm
Inertial navigation system has the advantages of low cost, small size and low power consumption. However, due to the low accuracy of MEMS inertial devices, long-term use will lead to fast error dispersion, and can not be long-term navigation tasks, the general use of multi-sensor integrated navigation, that is, MEMS inertial navigation and other navigation integration, through other navigation systems navigation information to correct the inertial navigation system error, so as to improve the accuracy of the entire navigation system. If the data integration of multiple navigation systems is to be carried out, filtering and other methods should be used.
2.2.1 Kalman filtering algorithm
Kalman (Kal ** n) filter is a filtering algorithm that estimates state quantities by obtaining information from extracted observation signals.Kal ** n filter is a real-time recursive algorithm that deals with random objects. Depending on the system noise and observation noise, the output of the system observations is used as the filter input and the state quantity to be estimated is used as the output, i.e., the state quantity of the system at the next moment is estimated from the last-minute observations, which is essentially the best estimation method.
Conventional Kal ** n filtering is applicable to linear Gaussian models, whereas most inertial navigation systems are nonlinear, so conventional Kal ** n filters cannot meet the requirements and filtering algorithms applicable to nonlinear systems must be established. Therefore, the extension of Kal has been developed ** n filtering method to linearize the nonlinear function of nonlinear system by Taylor series and other methods, saving the advanced term and obtaining the linear system model.
Since Kal's extension ** n filtering linearizes the nonlinear function, it inevitably introduces a linear error, leading to the development of Kal ** n filtering without traces. This filtering method is similar to the probability density of a nonlinear function and uses identified samples to estimate the posterior probability density of the state without being similar to a nonlinear function. The statistics of the traceless Kal ** n filter are not only more accurate but also more stable than the expanded Kalman filter.
2.2.2 Complementary Filtering Algorithms
The traditional extended Kal ** n filter has a Jacobi matrix, there is a large amount of computation, and the white noise conditions can not guarantee that the moment is established and other shortcomings; however, the use of complementary filtering algorithms can reduce the amount of computation, improve the accuracy of the system measurements, and do not need to be in the white noise conditions can also be established. Using the complementary characteristics of gyroscope and accelerometer in the frequency domain can improve the data fusion accuracy of gyroscope and accelerometer, and realize high-precision fusion.
2.2.3 Neural networks
Machine neural networks are modeled after biological neural networks. Neural networks are a type of machine learning, where model parameters are trained through a network system.Neural networks are mainly composed of an input layer, an output layer and an implicit layer. From M-P neurons and Hebb learning rules in the 1940s, to Hodykin-Huxley equations, perceptron models and adaptive filters in the 1950s, to self-organizing mapping networks, neurocognitive machines, and adaptive resonance networks in the 1960s, numerous neural network computational models have evolved to become classical methods in computer vision, signal processing, and other fields, bringing about far-reaching impact.
There are two kinds of neural networks: forward neural networks and inverse neural networks. Neural networks are characterized by parallel processing, distributed storage, high redundancy, the ability to perform nonlinear operations, and good fault tolerance. With the development of neural network technology, its application field is also broadening, and now it plays a vital role in the field of inertial navigation, image processing and so on. Neural network algorithms have a wide range of theoretical foundations, including neural network structure model, network communication model, and memory model. Learning algorithms have shown that big data analysis based on neural network algorithms has good performance and application prospects, provides a decision-making basis in data fusion of sensors, and makes an important contribution to the autonomous navigation of unmanned systems. Fuzzy neural network has superior performance in data fusion and data mining, can make better use of language, and the form of knowledge expression is easy to understand, but there are shortcomings such as weak self-learning ability and difficult to utilize numerical information, so artificial neural network can be combined with fuzzy system.
3. Application of MEMS inertial navigation
MEMS inertial navigation technology with its small size, low power consumption, light weight and low cost and other characteristics in a variety of unmanned systems, such as unmanned aircraft, unmanned vehicles, unmanned ships and robots and other systems have been commonly used.
3.1 Unmanned Aircraft
In recent years, micro-small UAVs have been playing an increasingly important role in the military and civil fields, and in order to realize the positioning of the UAV itself and the positioning problem, the air attitude measurement and control system plays a crucial role. The air attitude measurement and control system is mainly composed of GPS antenna, GPS receiver board, Jetlink magnetic sensor, inertial measurement unit, altitude airspeed sensor and conditioning unit. The accuracy of the sensor directly determines the accuracy of the UAV's position, and the data collected by the sensor calculates the UAV's position and attitude information through the navigation algorithm. At present, the navigation of UAV mainly adopts the combination of MEMS inertial navigation system and GPS, which can not only improve the accuracy of the system, but also shorten the initial alignment time. Nowadays, the accuracy of the navigation system equipped on top of the UAV is consumer level, such as the accuracy of Invensense MP6500 is 2°/s. With the improvement of the accuracy of the MEMS device and the reduction of the cost, the navigation accuracy of the UAV will be improved in the future.
3.2 Unmanned Vehicles
Unmanned vehicles are used to perceive the external environment through on-board sensors, and obtain vehicle position, attitude information and obstacle information, so as to control the vehicle traveling speed, steering, and starting and stopping. At present, Google, are carrying out the development of unmanned vehicles, and has carried out road experiments. When the unmanned vehicle walks to the tall building, and the GPS is blocked and no ** normal work, the inertial navigation system equipped on the unmanned vehicle for a short period of time can meet the needs of the vehicle autonomy to move forward. The MEMS inertial navigation system on the unmanned vehicle generally requires high accuracy.
3.3 Unmanned ship field
Due to the border patrol, water quality exploration and other tasks to take ordinary ship equipment is more dangerous and high cost, resulting in unmanned ship technology is developing rapidly. Obtaining the position and attitude information of unmanned ship is an important prerequisite for unmanned ship to be able to carry out work autonomously. Nowadays, the sensors equipped on unmanned ships mainly include GPS, MEMS inertial navigation system and obstacle avoidance radar. With the improvement of the accuracy of MEMS inertial navigation system, the inertial navigation system plays a crucial role in the acquisition of position and attitude information of unmanned ship. For the MEMS inertial navigation system carried on unmanned ship, the general consumer-grade medium-low precision can meet the demand.
3.4 Robotics
A mobile robot is an automated device that can work autonomously in a fixed or time-varying environment. In recent years, it has been widely used in the service industry, household, industry and other fields. Wheeled robots are similar to unmanned vehicles in terms of application, and are navigated by collecting data from sensors such as vision cameras, MEMS inertial sensors, LiDAR and odometers. Domestic universities have also started research work on wheeled robots earlier. In the navigation process of wheeled robots adopting inertial sensors and odometers, MEMS inertial sensors provide accurate attitude angles, and due to wheel slippage and other impacts on inertial navigation as well as odometers, most of them are now navigated through a combination of visual odometers and MEMS inertial navigation, and data fusion is carried out through the extended Kal ** n filtering algorithm so as to improve the accuracy of the system.
3.4 Other Fields
In addition to the above fields, MEMS inertial sensors are also used in electronic devices such as cell phones, tablets, game consoles, cameras, VR glasses, and man-portable navigation for indoor positioning. At present, the personal safety of firefighters when fighting fires in high-rise buildings as well as the elderly with limited mobility at home is a common concern in the society. If the MEMS inertial navigation system is placed on the detector for navigation, real-time positional attitude information can be obtained, which can improve the safety factor of the monitored personnel. There are several ways to use MEMS inertial navigation system for indoor personnel localization: one is to use MEMS accelerometers to detect and identify the personnel's pace status, and then through the magnetometer to detect the direction of the personnel's movement, and thus carry out the directional positioning of indoor personnel. Another method is to use two or more MEMS inertial navigation systems, installed in the feet and waist position of the personnel, through multiple MEMS inertial navigation system correction method for positioning.
4 Outlook for the development of MEMS inertial navigation
4.1 MEMS inertial navigation devices
In recent years, MEMS inertial sensors have been developing rapidly, and their accuracy has been improving. Although there is still a big gap compared to fiber optic gyro and laser gyro, its low price, small size and light weight make MEMS inertial navigation system play an important role in inertial navigation system. In the future, with the continuous development of MEMS material process and manufacturing process, MEMS inertial navigation system accuracy will continue to improve, and its cost will continue to reduce, so the use of strategic high-precision MEMS gyroscope to replace the fiber optic gyroscope is an important development trend. With the continuous progress of micromachining process, MEMS inertial sensors will develop in the direction of lightweight and miniaturization.
4.2 MEMS combined navigation algorithm
Although the accuracy of MEMS inertial sensors is progressing, but the tactical level MEMS inertial navigation system error over time is still dispersed large, in many occasions can not meet the requirements of high precision, so the MEMS inertial navigation and GPS combination of navigation is still the main navigation mode. Therefore, it is an important development direction to study the algorithms with higher accuracy, efficiency and robustness, and to support the combined navigation system in software.
4.3 Application of MEMS Inertial Navigation
In the decades of MEMS technology development, MEMS inertial navigation technology has been widely used in the electronics field, automotive industry and home service industry. With the continuous improvement of MEMS inertial navigation accuracy and stability, the future MEMS inertial navigation technology will certainly play an important role in the field of unmanned systems, such as spacecraft, satellites, robots and other unmanned systems.
5 Conclusion
MEMS inertial navigation technology has the advantages of miniaturization, low cost, etc., and has been developed rapidly in the past decades, and has been applied more and more in the field of unmanned systems, which, as the main development direction of future inertial navigation, is showing a strong potential as well as a good application prospect. This paper reviews the development history of MEMS inertial navigation system, summarizes its key technologies, and looks forward to the application and development of MEMS inertial navigation technology to provide reference for the research of MEMS inertial navigation system.
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