In-motion initial alignment and positioning with INS/CNS/ODO integrated navigation system for lunar rovers

Lu, Jiazhen; Lei, Chaohua; Yang, Yanqiang; Liu, Ming

doi:10.1016/j.asr.2017.03.011

Cited by 10 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SINS performance depends on the accuracy and speed of initial alignment process, which is one of the key technologies in SINS. The Kalman filer (KF) is widely used in the initial alignment [13] with the information from an external sensor device such as GNSS [14], odometer [15] and Doppler velocity log (DVL) [16]. Optimisation-based initial alignment is also suggested as obtaining a roughly known initial estimate required for KF is hard for an inmotion vehicle [17], [18].…”

Section: A Problem Formulation and Main Ideamentioning

confidence: 99%

Phased Array Radio Navigation System on UAVs: GNSS-based Calibration in the Field

Okuhara

Bryne

Gryte

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

With global coverage, high accuracy, and lightweight receivers, global navigation satellite system (GNSS) has been the major positioning solution for unmanned aerial vehicles (UAV). However, GNSS is prone to electromagnetic interference and malicious attacks such as jamming or spoofing due to its low signal-to-noise ratio (SNR). To ensure the continuity and safety of UAV operation, the use of redundant navigation systems is crucial. Phased array radio system (PARS) has proven its potential as a local navigation solution in the last few years. PARS is robust against malicious attacks due to a significantly higher SNR than GNSS together with directional and encrypted transmission. One of the challenges of the PARS-based navigation is the radio antenna at ground station, as its orientation needs to be determined precisely to obtain accurate navigation solution for unmanned vehicles. This paper presents an automatic calibration algorithm for the ground radio antenna orientation using a multiplicative extended Kalman filter (MEKF) based on GNSS and PARS measurements. The calibration algorithm was tested with data obtained from a field test using a fixed wing UAV and validated by a residual analysis comparing the PARS-and GNSS-based positioning.

show abstract

Section: A Problem Formulation and Main Ideamentioning

confidence: 99%

Phased Array Radio Navigation System on UAVs: GNSS-based Calibration in the Field

Okuhara

Bryne

Gryte

et al. 2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

show abstract

“…where u = C b i 0 0 1 T is the unit direction vector of the star sensor's optical axis in the earth centred inertial frame (i-frame). C b i is the transformation matrix from the star sensor frame (b-frame) to the i-frame, which can be determined by the attitude of the star sensor in the i-frame [24]. θ FOV represents the size of the star sensor's field and u L is the unit direction vector of a captured star in the i-frame, which is related to the known stellar right ascension…”

Section: Star Image Simulationmentioning

confidence: 99%

Automatic centroid extraction method for noisy star image

Gou

Chen

2018

IET Image Processing

View full text Add to dashboard Cite

“…Currently, the INS/CNS/SMN integrated navigation system is extensively utilized [18][19][20][21][22][23]. This system leverages measurements provided by CNS and SMN to mitigate the accumulation of errors in INS over time.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

Tian,

Cheng,

Yao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

show abstract

In-motion initial alignment and positioning with INS/CNS/ODO integrated navigation system for lunar rovers

Cited by 10 publications

References 10 publications

Phased Array Radio Navigation System on UAVs: GNSS-based Calibration in the Field

Phased Array Radio Navigation System on UAVs: GNSS-based Calibration in the Field

Automatic centroid extraction method for noisy star image

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

Contact Info

Product

Resources

About