Investigation of UWB RF signal technology for solving indoor positioning problem

The article considers the process of developing a local positioning system using an ultra-wideband radio signal system and its integration with a strapdown inertial navigation system (SINS). A system based on Ultra-Wide Band (UWB) technology is used as a radio navigation system. An overview of the developed experimental integrated navigation system model is presented. Algorithms for calculating the position using the propagation time of the radio signal are used to obtain a navigation solution. An analysis of the accuracy of Single-Sided Two-Way Ranging and Double-Sided Two-Way Ranging algorithms using a UWB radio module is presented. The modeling errors of the inertial navigation system were performed. The maximum permissible parameters of the sensitive element errors were obtained for integration with the radio navigation system. The scheme of integration of the navigation solution of the UWB and SINS systems is determined.

A Gyroless Algorithm with Multi-Hypothesis Initialization for Projectile Navigation

Projectiles are subjected to a high acceleration shock at launch (20,000 g and higher) and can spin very fast. Thus, the components of onboard navigation units must therefore withstand such constraints in addition to being inexpensive. This makes only a few inertial sensors suitable for projectiles navigation. Particularly, rate gyroscopes which are gun-hardened and have an appropriate operating range are not widely available. On the other hand, magneto-resistive sensors are inexpensive and can satisfy both gun-hardening and operating range requirements, making them an alternative for angular estimation in guided projectiles. This paper presents a gyroless navigation algorithm for projectiles. The lack of gyroscope is handled by the usage of attitude kinematics computed over past attitude estimates of the filter, coupled with a measurement model based on magnetometer and GPS observations of the attitude. The observability of the attitude when considering non-calibrated magnetometers and its dependency on the initialization is addressed. Then, to cope with the initialization dependency of the filter, we proposed a multi-hypothesis initialization algorithm. In terms of performance, the algorithm is shown to provide a high-rate navigation solution with an interesting performance.

Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach.

A bathymetric mapping and SLAM dataset with high-precision ground truth for marine robotics

In recent years, sonar systems for surface and underwater vehicles have increased in resolution and become significantly less expensive. As such, these systems are viable at a wide range of price points and are appropriate for a broad set of applications on surface and underwater vehicles. However, to take full advantage of these high-resolution sensors for seafloor mapping tasks an adequate navigation solution is also required. In GPS-denied environments this usually necessitates a simultaneous localization and mapping (SLAM) technique to maintain good accuracy with minimal error accumulation. Acoustic positioning systems such as ultra short baseline (USBL) and long baseline (LBL) are sometimes deployed to provide additional bounds on the navigation solution, but the positional uncertainty of these systems is often much greater than the resolution of modern multibeam or interferometric side scan sonars. As such, subsurface vehicles often lack the means to adequately ground-truth navigation solutions and the resulting bathymetic maps. In this article, we present a dataset with four separate surveys designed to test bathymetric SLAM algorithms using two modern sonars, typical underwater vehicle navigation sensors, and high-precision (2 cm horizontal, 10 cm vertical) real-time kinematic (RTK) GPS ground truth. In addition, these data can be used to refine and improve other aspects of multibeam sonar mapping such as ray-tracing, gridding techniques, and time-varying attitude corrections.

Implementation of Parallel Cascade Identification at Various Phases for Integrated Navigation System

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution.

S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

GNSS/IMU integration algorithm experimental evaluation

To improve the reliability and accuracy of navigation solution, the integration of the satellite navigation receiver with the inertial navigation system is used. These systems have complementary characteristics. An important part of the combined systems is the integration algorithm, which largely determines the final characteristics. The synthesis of such an algorithm for velocity, attitude and the errors of the inertial measuring unit estimation has been carried out. The algorithm is implemented in the software of the prototype of the inertial-satellite navigation system. The results of the experimental evaluation of algorithm’s characteristics for automotive dynamics are shown.

MLCA—A Machine Learning Framework for INS Coarse Alignment

Inertial navigation systems provides the platform’s position, velocity, and attitude during its operation. As a dead-reckoning system, it requires initial conditions to calculate the navigation solution. While initial position and velocity vectors are provided by external means, the initial attitude can be determined using the system’s inertial sensors in a process known as coarse alignment. When considering low-cost inertial sensors, only the initial roll and pitch angles can be determined using the accelerometers measurements. The accuracy, as well as time required for the for the coarse alignment process are critical for the navigation solution accuracy, particularly for pure-inertial scenarios, because of the navigation solution drift. In this paper, a machine learning framework for the stationary coarse alignment stage is proposed. To that end, classical machine learning approaches are used in a two-stage approach to regress the roll and pitch angles. Alignment results obtained both in simulations and field experiments, using a smartphone, shows the benefits of using the proposed approach instead of the commonly used analytical coarse alignment procedure.