Multi-Sensor Personal Navigator: System Design and Calibration

Global Positioning System (GPS) spoofing is a common mean to cheat an Unmanned Aerial Vehicle (UAV) which is guided by the INS/GPS integrated navigation system. Detecting the spurious GPS signal is the precondition of anti-spoofing for UAVs. A detection system of GPS spoofing is designed based on dynamics identification. The centroid motion model and attitude motion model were established. The measurement information from inertial measurement units (IMUs) and positioning information from GPS both are visualized responses to the dynamic laws of UAVs. These two types of measurement information were separately used to estimate the dynamics parameters of UAVs online using two Extended Kalman Filters (EKFs). The detection of spurious GPS signals was realized effectively based on the monitoring of relative estimation errors. Numerical simulations demonstrated that the dynamics parameters could be estimated online by the combined filters. And the precision and accuracy of filter #1, which was based on IMU information, were both higher than those of filter #2, which was based on GPS information. When the spurious GPS signal was just zero-order continuous with the true signal at the penetration point, a pulse relative error would occur at the penetration point; when the spurious GPS signal was first-order continuous with the true signal at the penetration point, a step relative error would occur. Furthermore, relative systematic errors matched with the period of spurious signals.

A real-time integrated navigation system with 2-cm position accuracy was developed using commercial off-the shelf (COTS) equipment including inertial sensors and differential Global Positioning System (GPS) receivers, and was demonstrated for transportation applications with an emphasis on lane departure detection. A 900-MHz radiofrequency communications link transmits GPS base station range and carrier phase measurements to a GPS receiver on the vehicle. The GPS receiver calculates the vehicle position relative to the surveyed base location. The loosely coupled eight-state extended Kalman filter with 10-Hz navigation aids provides smoothed position information between 2-Hz GPS updates and provides graceful degradation in the presence of GPS blockages or communication dropouts. Currently, the navigation aids include anti-lock braking system wheel turns, electronic compass heading and pitch, and map vertical height. Data acquisition and Kalman filtering systems were developed to handle real-time processing and recording of all instrument data for further post-test system evaluation to determine the optimum configuration. An accurate rapid survey and map representation approach for measuring lane boundaries was validated using a vehicle, demonstrating inherit repeatability by the GPS positioning system. Real-time lane departure monitoring was developed. A video camera was installed to assist in accurate surveying and real-time system validation. This GPS-based positioning system is suitable for highway speeds during all weather conditions. It can be extended to other collision avoidance, drowsy driver awareness, advanced curve or intersection warnings, SOS, tracking, automated control, fast precision road mapping, and traffic management applications.

Global positioning system (GPS) technology is well suited for attitude determination. However, in land vehicle application, low-cost single frequency GPS receivers which have low measurement quality are often used, and external factors such as multipath and low satellite visibility in the densely built-up urban environment further degrade the quality of the GPS measurements. Due to the low-quality receivers used and the challenging urban environment, the success rate of the single epoch ambiguity resolution for dynamic attitude determination is usually quite low. In this paper, a micro-electro-mechanical system (MEMS)—inertial navigation system (INS)-aided ambiguity resolution method is proposed to improve the GPS attitude determination performance, which is particularly suitable for land vehicle attitude determination. First, the INS calculated baseline vector is augmented with the GPS carrier phase and code measurements. This improves the ambiguity dilution of precision (ADOP), resulting in better quality of the unconstrained float solution. Second, the undesirable float solutions caused by large measurement errors are further filtered and replaced using the INS-aided ambiguity function method (AFM). The fixed solutions are then obtained by the constrained least squares ambiguity decorrelation (CLAMBDA) algorithm. Finally, the GPS/MEMS-INS integration is realized by the use of a Kalman filter. Theoretical analysis of the ADOP is given and experimental results demonstrate that our proposed method can significantly improve the quality of the float ambiguity solution, leading to high success rate and better accuracy of attitude determination.

This paper presents the integration of an Ultra Short Base Line (USBL) acoustic modem and positioning device in a two-parallel Extended Kalman Filter (EKF) multisensor navigation schema for an Autonomous Underwater Vehicle (AUV). The system consists of a first odometric EKF position estimator fed with the measurements provided by an Inertial Measurement Unit (IMU), a Doppler Velocity Log (DVL), a visual tracker and a pressure sensor, and a second corrective EKF that fuses the previous sensors with the Global Positioning System (GPS) and the corrected delayed-state Ultra Short Base Line (USBL) position data. The first filter is aimed to compute a highly reliable vehicle position estimation from the sensor suit that produces high frequency relative navigation data. This estimation is essential to correct the delay of the USBL position measurements. The objective of the second filter is to give the vehicle a global pose, eliminating the drift inherent to dead-reckoning sensors by integrating absolute positions (GPS and USBL) provided at low frequencies. The effects of the USBL integration in the global localization module are evaluated in a simulated environment, and compared to a ground truth trajectory with the pose estimates given by the first EKF, the USBL raw data and the output of the second EKF, showing an effective reduction of the trajectory error.

This paper presents an operational approach for determining satellite attitude from carrier phase GPS (Global Positioning System) measurements. A conventional recursive least square (RLS) estimator was implemented without requiring spacecraft dynamics. The proposed approach can be applicable for general space missions, particularly for real-time operation onboard-spacecraft. The attitude angles and electrical path difference (line bias) between antenna chains caused an offset error in GPS observables, can be estimated. The implemented estimator was tested with a simulated data, and the flight data collected onboard a mini-satellite orbiting in low Earth orbit.

The application of unmanned vehicles in civilian and military fields is increasingly widespread. Traditionally, unmanned vehicles primarily rely on Global Positioning Systems (GPSs) for positioning; however, GPS signals can be limited or completely lost in conditions such as building obstructions, indoor environments, or electronic interference. In addition, countries are actively developing GPS jamming and deception technologies for military applications, making precise positioning and navigation of unmanned vehicles in GPS-denied or constrained environments a critical issue that needs to be addressed. In this work, authors propose a method based on Visual–Inertial Odometry (VIO), integrating the extended Kalman filter (EKF), an Inertial Measurement Unit (IMU), optical flow, and feature matching to achieve drone localization in GPS-denied environments. The proposed method uses the heading angle and acceleration data obtained from the IMU as the state prediction for the EKF, and estimates relative displacement using optical flow. It further corrects the optical flow calculation errors through IMU rotation compensation, enhancing the robustness of visual odometry. Additionally, when re-selecting feature points for optical flow, it combines a KAZE feature matching technique for global position correction, reducing drift errors caused by long-duration flight. The authors also employ an adaptive noise adjustment strategy that dynamically adjusts the internal state and measurement noise matrices of the EKF based on the rate of change in heading angle and feature matching reliability, allowing the drone to maintain stable positioning in various flight conditions. According to the simulation results, the proposed method is able to effectively estimate the flight trajectory of drones without GPS. Compared to results that rely solely on optical flow or feature matching, it significantly reduces cumulative errors. This makes it suitable for urban environments, forest areas, and military applications where GPS signals are limited, providing a reliable solution for autonomous navigation and positioning of drones.

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.

Low-Cost Livestock Global Positioning System Collar from Commercial Off-the-Shelf Parts

This paper evaluates the performance of a semidistributed multinode time-of-arrival (TOA) anddirection-of-arrival (DOA) fusion localization technique in terms of localization circular error probability (CEP). The localization technique is applicable in mobile ad hoc networks (MANETs) when global positioning system (GPS) is not available (GPS denied environments). The localization CEP of the technique is derived theoretically and verified via simulations. In addition, we theoretically derive the localization CEP of GPS-aided TOA fusion and GPS-aided DOA fusion techniques, which are also applicable in MANETs. Finally, we compare these three localization techniques theoretically and via simulations. The comparison confirms that in moderate scale MANETs, the multinode TOA-DOA fusion localization technique achieves the best performance; while in large scale MANETs, GPS-aided TOA fusion leads to the best performance.

The Global Positioning System (GPS) provides satellite-based navigation signals, which are employed in many fields, including agriculture, transportation, aviation, and military/personal navigation. In an effort to minimize interference among GPS satellites and to enable GPS receivers to discern satellite identity, each satellite is assigned a specific pseudorandom noise (PRN) sequence that is used to modulate the phase of the corresponding signal. The codes that modulate the current GPS landscape are constructed in such a way that cross-correlation among codes is kept to a bounded minimum, which should significantly limit harmful signal interference. In this study, the efficacy of the current PRN-based modulation system is called into question as GPS signal amplitude and carrier phase data over the past decade show frequent interference between satellite signals.

Navigation Scheme for the LSTS SEACON Vehicles: Theory and Application

Multipath (MP) is the dominant error source in Global Positioning System (GPS) code-based position solutions requiring high accuracy. A technique is introduced here to mitigate error due to ground-reflected MP signals. The technique uses two orthogonal dipoles to capture the direct GPS signal and the ground-reflected GPS signal. Adjusting the amplitude and phase of the received voltage between the two dipoles can reduce the impact of MP error. Theoretical derivations of this technique are performed for a GPS signal upon reflection from dry soil, seawater, and fresh water. The theoretical results are verified with a real world experiment on the aforementioned surfaces. GPS pseudo-range (PR) and carrier-to-noise ratio (C/No) measurements for specific satellites are used to verify the predicted theoretical results.

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for low-cost navigation of ground vehicles. The ANFIS structure is proposed for fusing the measurements of the Global Positioning System (GPS) and inertial measurement unit (IMU). Low-cost GPS receivers provide the position and velocity of the vehicle at a lower rate than the IMU sampled vehicle dynamics. Additionally, the GPS measurements might be missed/delayed due to receiver's inability to lock on the signal or due to obstruction from neighboring vehicle's or infrastructures. Therefore, the variable delay between the IMU and GPS signals is selected as an additional input to the ANFIS architecture. The performance of the proposed ANFIS architecture is demonstrated through experimental results. The instrumentation system used in this experiment consists of a microcontroller system, a GPS receiver, and an IMU.

This paper presents an adaptive learning method for data fusion in autonomous\ndriving vehicles. The localization is based on the integration of Inertial\nMeasurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning\nSystem (GPS) units in an adaptive Kalman filter (KF). The observability\nanalysis reveals that $i$) integration of a single GPS with IMU does not\nconstitute an observable system; $ii$) integration of two GPS units with IMU\nresults in a locally observable system provided that the line connecting two\nGPS antennas is not parallel with the vector of the measured acceleration,\ni.e., the sum of inertial and gravitational accelerations. The latter case\nmakes it possible to compensate for the error in the estimated orientation due\nto gyro drift and its bias without needing additional instruments for absolute\norientation measurements, e.g., a magnetic compass. Moreover, in order to cope\nwith the fact that GPS systems sometimes lose their signal and receive\ninaccurate position data, the self-tuning filter estimates the covariance\nmatrix associated with the GPS measurement noise. This allows the KF to\nincorporate GPS measurements in the data fusion process heavily only when the\ninformation received by GPS becomes reliably available.\n

Many organizations would benefit from the ability to continuously locate the position of individuals as they move throughout a facility. Early tracking and direction finding was achieved using directional antennas or pseudo Doppler array techniques. It was developed for outdoor use over relatively long ranges. The reliability and accuracy fell short of current personnel tracking needs. Today, Global Positioning System (GPS) service is ubiquitous. Differential GPS provides sufficient accuracy for most personnel tracking applications. Recent cost reductions in OEM grade GPS receiver printed circuit assemblies have made GPS solutions feasible for personnel tracking. Bidirectional LEOs based satellite location systems are under development which can be applied to personnel location as well. The paper examines the practical and technical issues confronting personnel tracking systems in today's target markets. Potential approaches to personnel tracking are presented and compared. We then introduce an alternative RF location technique utilized by the PALS (Personal Alarm Location System) from Dominion Wireless, Inc. The theory of operation of PALS is presented. Early designs and their performance are summarized. Current implementations are discussed and the capabilities and limitations are reviewed as observed in actual deployed systems. Finally, the future plans for the refinement and advancement of PALS are presented and projections of enhanced performance supplied