New Algorithms for Autonomous Inertial Navigation Systems Correction with Precession Angle Sensors in Aircrafts

An autonomous navigation method based on the fusion of INS (inertial navigation system) measurements with the line-of-sight (LOS) observations of space targets is presented for unmanned aircrafts. INS/GNSS (global navigation satellite system) integration is the conventional approach to achieving the long-term and high-precision navigation of unmanned aircrafts. However, the performance of INS/GNSS integrated navigation may be degraded gradually in a GNSS-denied environment. INS/CNS (celestial navigation system) integrated navigation has been developed as a supplement to the GNSS. A limitation of traditional INS/CNS integrated navigation is that the CNS is not efficient in suppressing the position error of the INS. To solve the abovementioned problems, we studied a novel integrated navigation method, where the position, velocity and attitude errors of the INS were corrected using a star camera mounted on the aircraft in order to observe the space targets whose absolute positions were available. Additionally, a QLEKF (Q-learning extended Kalman filter) is designed for the performance enhancement of the integrated navigation system. The effectiveness of the presented autonomous navigation method based on the star camera and the IMU (inertial measurement unit) is demonstrated via CRLB (Cramer–Rao lower bounds) analysis and numerical simulations.

“…According to (11) and (18), for synchronously observed multiple stars, the measurement model is expressed as:…”

Section: Model For the Attitude Determinationmentioning

confidence: 99%

“…However, the performance of INS/GNSS integrated navigation is easily affected by the disturbance of the GNSS signals. The use of INS alone is limited, as the navigation error increases with time, and it is sensitive to the initial errors [10,11].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Navigation of Unmanned Aircraft Using Space Target LOS Measurements and QLEKF

Xiong¹,

Zhou²,

Wei³

2022

“…The common methods for achieving relative positioning in GNSS-denied environments can be classified into the following three categories: visual positioning [7][8][9][10][11][12][13][14][15][16][17], inertial navigation positioning [18][19][20][21][22], and radio positioning [23,24]. The development status of each category of positioning technology is systematically discussed below, and the advantages and limitations of each technology are analyzed, providing good inspiration for the study of relative positioning methods for MAVs in GNSS-denied environments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng

Ren

Deng

2023

Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

“…A variety of positioning systems exist which make use of different (i) technologies , (ii) signal properties , and (iii) positioning algorithms . Technologies include inertial navigation systems (INS) [ 3 , 4 ], sound waves [ 5 , 6 ], infrared [ 7 ], visible light [ 8 ], and radio frequency, including Ultra-Wide Band (UWB) [ 9 ], Bluetooth [ 10 ], Bluetooth Low Energy (BLE) [ 11 ], ZigBee, Wireless Local Area Network (WLAN) [ 12 , 13 ], and Wireless Underground Sensor Network [ 14 ]. Signal properties used for positioning include Angle of Arrival (AoA) [ 7 ], Time of Arrival (ToA) [ 14 ], Time Difference of Arrival (TDoA), Received Signal Strength Indication [ 15 ], Time of Flight (ToF), Return Time of Flight (RToF), and Phase of Arrival (PoA) [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Accuracy Improvement of UWB-TDoA-Based Indoor Positioning System

Grasso

Innocente

Tai

et al. 2022

Positioning systems are used in a wide range of applications which require determining the position of an object in space, such as locating and tracking assets, people and goods; assisting navigation systems; and mapping. Indoor Positioning Systems (IPSs) are used where satellite and other outdoor positioning technologies lack precision or fail. Ultra-WideBand (UWB) technology is especially suitable for an IPS, as it operates under high data transfer rates over short distances and at low power densities, although signals tend to be disrupted by various objects. This paper presents a comprehensive study of the precision, failure, and accuracy of 2D IPSs based on UWB technology and a pseudo-range multilateration algorithm using Time Difference of Arrival (TDoA) signals. As a case study, the positioning of a 4×4m2 area, four anchors (transceivers), and one tag (receiver) are considered using bitcraze’s Loco Positioning System. A Cramér–Rao Lower Bound analysis identifies the convex hull of the anchors as the region with highest precision, taking into account the anisotropic radiation pattern of the anchors’ antennas as opposed to ideal signal distributions, while bifurcation envelopes containing the anchors are defined to bound the regions in which the IPS is predicted to fail. This allows the formulation of a so-called flyable area, defined as the intersection between the convex hull and the region outside the bifurcation envelopes. Finally, the static bias is measured after applying a built-in Extended Kalman Filter (EKF) and mapped using a Radial Basis Function Network (RBFN). A debiasing filter is then developed to improve the accuracy. Findings and developments are experimentally validated, with the IPS observed to fail near the anchors, precision around ±3cm, and accuracy improved by about 15cm for static and 5cm for dynamic measurements, on average.