Parametric study of loosely coupled INS/GNSS integrity performance

With the increasing demand for indoor navigation applications, indoor navigation has become a research hotspot in many technical fields. High-precision sensors are expensive, and they are often used in industrial and aerospace applications. Low-cost sensors can cause severe drift and noise. This paper uses a simple, low-cost Inertial measurement unit (IMU) and effectively evaluates the precise position of pedestrians. This paper designs a pedestrian position estimation based on low-cost inertial sensors. To solve the problem of inertial sensing noise and accumulated errors during the movement, based on the EKF algorithm, a multi-condition threshold detection method is proposed, and then a zero-speed update, an angular speed update, and a yaw angle update are designed. In the measurement state, the proposed measurement error vector effectively eliminates the cumulative error during walking. Finally, using this algorithm to perform straight and rectangular walking routes, corresponding experiments were performed on the effectiveness of the method used. The proposed method for estimating the inertial navigation of pedestrians has an error within 3.8%. This basic research is of great significance in rehabilitation training and somatosensory play.

In an earlier chapter, we explored how the Kalman filter does an excellent job of taking the radionavigation system geometry into account when forming its estimates but does not do well in handling nonlinear system dynamics. In the previous chapter, we introduced the concept of the complementary filter and applied it to a simple one-dimensional aided-inertial navigation problem. Since the filter in the aided case is estimating inertial errors, instead of the total aircraft state, the non-linearities arising from a dynamic trajectory were not a problem. An aided-inertial architecture is founded on the principle that the inertial navigation system does an excellent job of determining the position, velocity and attitude of the vehicle in the short term and thus the Kalman filter is not needed for that task. The purpose of the aiding is to prevent the inertially determined vehicle state from drifting off over the long term.In this chapter, we extend the inertial aiding concept to two dimensions. We will use a two-dimensional radionavigation system (DME-DME) to aid a simplified inertial navigation system. We will be estimating inertial errors and one way to view this is that the stand-alone inertial position solution provides the "nominal" trajectory used to linearize the system and measurement equations to make them suitable for the Kalman filter. This implies that the inertial errors may be modeled linearly. In order to introduce some key concepts in this chapter, we will use a very simple inertial error model. In the next chapter, we will develop a significantly more detailed inertial error model but the fact remains that the model must be linear in order to be suitable for the Kalman filter. We will discover in a later chapter that once the inertial error grows sufficiently large the linearity assumption breaks down. We will also discover that the typical solution to this problem is to feedback the estimated errors as corrections to the inertial system thus keeping the inertial errors small.

Prior experiments have confirmed that specific terrain-based localization algorithms, designed to work in GPS-free or degraded-GPS environments, achieve vehicle tracking with tactical-grade inertial sensors. However, the vehicle tracking performance of these algorithms using low-cost inertial sensors with inferior specifications has not been verified. The included work identifies, through simulations, the effect of inertial sensor characteristics on vehicle tracking accuracy when using a specific terrain-based tracking algorithm based on Unscented Kalman Filters. Results indicate that vehicle tracking is achievable even when low-cost inertial sensors with inferior specifications are used. However, the precision of vehicle tracking decreases approximately linearly as bias instability and angle random walk coefficients increase. The results also indicate that as sensor cost increases, the variance in vehicle tracking error asymptotically tends to zero. Put simply, as desired precision increases, increasingly larger and quantifiable investment is required to attain an improvement in vehicle tracking precision.

Recent developments in sports technology have enabled sports engineers to utilise the availability of inertial measurement units (IMUs) to develop a customised system for data collection during sports performances. Ensuring the reliability of such a system is essential for objective data collection and transmission of information that coaches could rely upon for improving athletes’ techniques and overall performance. The aim of this study was to investigate the reliability of a custom-made IMU system (embedded with Arduino Pro Mini) in the evaluation of skateboarding related manoeuvres. A skilful male and experienced amateur skateboarder (23 years, with 5 years skateboarding experience) performed five skateboarding tricks (Ollie, Nollie, Pop Shuvit, Kickflip and Frontside 180) using a customised ORY skateboard (IMUs fused). The skateboarder was permitted to execute two separate tricks (Tests A and B); thereafter, the reliability of the IMUs in recognising the similarity of the tricks was evaluated using a test-retest approach. Six time-domain signals obtained from the IMU system of each trick’s execution were extracted. Statistical analyses, including the Kolmogorov-Smirnov test, intraclass correlation coefficients (ICC), Cronbach alpha and correlation coefficients, were utilised to measure the scale’s reliability of the system. The results revealed no significant difference between Tests A and B of each trick p > 0.05, ICC > 0.80, Cronbach alpha > 0.80 and r > 0.80; p -value < 0.001. A relatively lower root means square error and mean absolute error were obtained, further suggesting the effectiveness of the system in detecting the similarities in the movement patterns of the skateboarder during the trick execution. The IMU system appeared to be reliable in measuring skateboarding tricks performances. The present findings could serve as guidelines for testing future, custom-made, IMU devices before the commencement of data collection.

Inertial navigation system (INS) pivotal error sources are related to the deterministic errors and random errors of the inertial measurement unit (IMU). Low-cost inertial sensors are distinguished by high noise and significant performance uncertainties. Consequently, errors related to low-cost INS states (position, velocity, and attitudes) are rising rapidly in stand-alone mode. If accurate performance can be achieved with low-cost IMU, the cost of real applications can be reduced and the development of new applications may be made feasible. Errors in gyroscopes (gyro) which are the main building block of IMU play a significant role, while the errors in the accelerometers can be calibrated and compensated with reasonable precision by comparison with the gravity of the earth. So precise modeling of gyro for navigation simulation for missiles and unmanned vehicles is essential. To achieve this goal, firstly an error estimation algorithm is implemented. This algorithm firstly is tested with a simulated signal with a known noise parameter injected in it. Moreover, the calibration of deterministic errors is performed. Furthermore, a study of the autocorrelation function of the sensor outputs is presented, and a method for calculating the frequency response to determine potential stability problems is provided by calculating the cutoff frequency. The typical analysis methods discussed throughout the paper are intended to assist designers to develop or choose gyro that is uniquely adapted to the design application requirements. The development technique is used to characterize a tactical grade Micro-Electro-Mechanical Systems (MEMSIC) IMU, and the test results prove its accuracy comparing to the manufacturing calibration sheet.

To enhance the inertial sensors performance, accurate characterization as well as modeling of the corresponding stochastic errors are required. In addition, accurate position and orientation information can be achieved when integrating inertial sensors with other position fix systems, such as Global Navigation Satellite Systems (GNSS). This paper investigates the Generalized Method of Wavelet Moments (GMWM), as a Wavelet Variance (WV)-based approach, for the stochastic characterization and model identification of the stochastic errors related to low-cost MEMS-based inertial sensor errors. Raw inertial sensor measurements were collected using MTi-G-710 MEMS Inertial Measurement Unit (IMU) where the GMWM was utilized for estimating the coefficients of different random processes associated with inertial sensor residuals. Results demonstrated that highly complicated error model structures are identified for the tested IMU, which contradicts the common assumption of modeling such errors as a single 1st order Gauss-Markov (GM) random process. In addition, the results showed that the GMWM is an efficient framework that could be used for estimating the parameters associated with complex structured random processes with the advantage of correlated residual characterization and identification.

Abstract. In airborne laser scanning a high-frequency trajectory solution is typically determined from inertial sensors and employed to directly geo-reference the acquired laser points. When low-cost MEMS inertial sensors are used, such as in lightweight unmanned aerial vehicles, non-negligible errors in the estimated trajectory project to the final point-cloud, resulting in unsatisfactory accuracy on the ground. There are different multi-sensor fusion approaches to correct the point-cloud errors caused by an imperfect trajectory determination. Mismatches between different optical observations and/or in the overlapping regions of the point-cloud can allow the correction of the final point-cloud, either directly, by means of rigid transformations, or indirectly, via improving the scanner trajectory estimation. In this work we propose to fuse lidar and cameras in a single adjustment based on dynamic networks, considering 2D tie-points from the imagery and 3D tie-points from overlapping point-cloud sections. On a challenging corridor mapping scenario, we show that considering either 2D or 3D tie-points, along with inertial and GNSS observations, results in a remarkably accurate point-cloud, even when low-cost inertial sensors are employed and in presence of challenging surface textures, such as high vegetation. Furthermore, since the distribution of the 2D and 3D tie-points is complementary, considering them together further increases the robustness of the adjustment due to higher redundancy. By employing the proposed approach within this controlled example, we were able to improve the final point-cloud accuracy by more than three times with respect to conventional geo-referencing methodology and to reduce the magnitude of the errors.

Low cost Micro Electrical Mechanical Systems (MEMS) inertial sensors are well known for their degraded performance if used in a self-contained navigation system. Due to large number of errors that affect the sensor's output. In this paper, an evaluation of performance improvement technique through experimental work for such sensors is introduced; the metric used in this work to evaluate the performance is the position error calculated using sensor's output through inertial navigation mechanization. For such evaluation, it is required to calculate the deterministic errors for gyroscopes and accelerometers that comprise the IMU, and to estimate the random errors using Allan variance technique for different bandwidths. To achieve this goal, a precise deterministic calibration process and random noise model of a chosen MEMS IMU is applied. The calibrated data then is used with the inertial navigation mechanization model, the test results show that the error in position is reduced dramatically when the calibration procedure is applied correctly to remove different types of errors. The estimated noise errors are compensated using Kalman Filter (KF) which led to improve the positioning accuracy.

With the quick development of smart tiny sensors, gait analysis (GA) methods based on wearable devices have become more popular recently. However, most existing wearable GA methods focus on data analysis from inertial sensors. This paper firstly proposes a novel system based on low-cost, wearable and multimodal sensors for GA, which combines inertial sensor and microphone sensor. The system is introduced by including the wearable multimodal prototype device, related software program, data collection procedure, etc. Secondly, based on the system, a freely available database (ICT_Gait) is published by including the details of setup method and spatial-temporal data label method. This database provides both data from inertial sensor and microphone sensor on two feet from 15 healthy subjects. There are totally 240 data sequences and 1732 steps from three various collection strategies. And this is the first database for multimodal and wearable GA as we know. Finally, based on the system and database, a preliminary research is conducted for footstep detection — the basic and key process for GA. And a coarse-to-fine multimodal fusion algorithm is presented for footstep detection. Experimental results show it achieves average 99.72% for precision rate and 97.83% for recall rate using multimodal fusion algorithm, which presents the effectiveness of multimodal fusion for wearable GA.

The demand for a reliable and accurate navigation system that can replace Global Positioning System (GPS) in GPS-denied environment has become increasingly imperative. For indoor environment where GPS is almost unavailable or unreliable, the utilization of other sensors such as inertial sensors becomes necessary. However, inertial sensors alone cannot sustain reliable long-term accuracy due to errors accumulation without external periodic corrections. Thus this paper proposes the utilization of Light Detection and Ranging (LiDAR) as an alternative system to provide periodic corrections. In this paper, a tightly-coupled integrated navigation system that integrates LiDAR, a single-axis gyroscope and wheel encoder is introduced. Straight lines detection and extraction algorithm is utilized to estimate the changes in orientation and range from LiDAR to the extracted line. LiDAR-estimated orientation change and range change to the extracted line feature between two consecutive LiDAR scans are first filtered out through a high rate extended Kalman Filter (EKF) to remove the effect of short-term noise associated with LiDAR scans. Then the smoothed orientation and range changes are fused by a low rate EKF with those predicted by gyroscope and wheel encoder. The proposed system is verified through real experiment on a wirelessly controlled Unmanned Ground Vehicle (UGV). Experimental results indicate that navigation accuracy has been improved to sub-meter and gyroscope bias is precisely estimated.

Achieving an innovative and intelligent low cost sensor system for an autonomous mobile robot to navigate and map the unstructured environment is the main aim of this paper. The environment or the map is unstructured in the sense that, the obstacles in the environment are unknown. There are various approaches to this problem, but the focused approach here can give robust performance of low cost sensor based localisation and mapping. The robust performance is quantified by explicit bounds of the position estimate of a mobile robot and to make it to build the environmental map of its surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), accelerometers and gyroscopes, to measure the acceleration and the angular rate, while the obstacle detection and map-making is done with the low cost sensor such as time-of-flight ultrasonic sensors. The localisation task is achieved by combining the different source of sensor measurements which are complementary and therefore compensate for each other's limitations, so that the resulting performance of the sensor system is better than of its individual components which provides high richness in the mapping task.

A trajectory reference system based on the Global Positioning System (GPS) can be used instead of conventional radars during missile flight tests. The high quality of the GPS-based trajectory reference makes it useful for evaluating the performance of the missiles' Inertial Measurement Units (IMUs). Such a system was installed and flight tested on two recently launched ballistic missiles. The GPS hardware configuration used on these flights is described. A Kalman filter approach is used to estimate individual IMU errors based on the GPS range and delta range data. The ability of the GPS-based system to identify IMU errors is compared to that of radar; GPS is found to provide superior estimates of the IMU errors.

This work presents an efficient initial calibration and alignment algorithm for a 6-degrees of freedom inertial measurement unit (IMU) to be used in land vehicle applications. Error models for the gyros and accelerometers are presented with a study of their perturbation in trajectory prediction. A full inertial error model is also presented to determine the sensors needed for full observability of the different perturbation parameters. Finally, dead-reckoning experimental results are presented based on the initial alignment and calibration parameters obtained with the algorithms presented. The results show that the proposed algorithms provide accurate position and velocity information for an extended period of time using nonaided IMU. ©1999 John Wiley & Sons, Inc.

Purpose – The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach – In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings – Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications – The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications – Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value – Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.

Accurate estimation of the attitude of unmanned aerial vehicles (UAVs) is crucial for their control and displacement. Errors in the attitude estimate may misuse the limited battery energy of UAVs or even cause an accident. For attitude estimation, proprioceptive sensors such as inertial measurement units (IMUs) are widely applied, but they are susceptible to inertial guidance error. With antenna arrays currently being installed in UAVs for communication with ground base stations, we can take advantage of the array structure in order to improve the estimates of IMUs. In the state-of-the-art attitude estimation algorithm, the first crucial step is phase delay estimation, which directly affects the attitude estimation performance. In this paper, by exploiting the parallelogram shape of the crossshaped antenna array, a 2-D unitary ESPRIT is used for phase delay estimation to achieve high attitude accuracy as well as computational efficiency. Moreover, we relax the known yaw to unknown case by adding an additional pair of antennas along the normal direction of the body-wing plane. The extension from one frequency bin to the case of multiple frequency bins is also included. Simulations show the feasibility of our proposed solution for different SNR levels as well as multipath scenarios.