Analytical Assessment of the Propagation of Colored Sensor Noise in Strapdown Inertial Navigation

Blum, Christopher; Dambeck, Johann

doi:10.3390/s20236914

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Our analysis is carried out in the frequency domain and relies on the propagation of the power spectral density of the accelerometer's noise. A similar approach has been recently published for conventional strapdown inertial navigation [21]. However, to our knowledge, this analysis is lacking for gyro-free inertial navigation.…”

Section: Introductionmentioning

confidence: 98%

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers

Sanjuán

Sinyukov

Warrayat

et al. 2023

Sensors

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High-sensitivity uniaxial opto-mechanical accelerometers provide very accurate linear acceleration measurements. In addition, an array of at least six accelerometers allows the estimation of linear and angular accelerations and becomes a gyro-free inertial navigation system. In this paper, we analyze the performance of such systems considering opto-mechanical accelerometers with different sensitivities and bandwidths. In the six-accelerometer configuration adopted here, the angular acceleration is estimated using a linear combination of accelerometers’ read-outs. The linear acceleration is estimated similarly but requires a correcting term that includes angular velocities. Accelerometers’ colored noise from experimental data is used to derive, analytically and through simulations, the performance of the inertial sensor. Results for six accelerometers, separated by 0.5 m in a cube configuration show noise levels of 10−7 m s−2 and 10−5 m s−2 (in Allan deviation) for time scales of one second for the low-frequency (Hz) and high-frequency (kHz) opto-mechanical accelerometers, respectively. The Allan deviation for the angular velocity at one second is 10−5 rad s−1 and 5×10−4 rad s−1. Compared to other technologies such as MEMS-based inertial sensors and optical gyroscopes, the high-frequency opto-mechanical accelerometer exhibits better performance than tactical-grade MEMS for time scales shorter than 10 s. For angular velocity, it is only superior for time scales less than a few seconds. The linear acceleration of the low-frequency accelerometer outperforms the MEMS for time scales up to 300 s and for angular velocity only for a few seconds. Fiber optical gyroscopes are orders of magnitude better than the high- and low-frequency accelerometers in gyro-free configurations. However, when considering the theoretical thermal noise limit of the low-frequency opto-mechanical accelerometer, 5×10−11 m s−2, linear acceleration noise is orders of magnitude lower than MEMS navigation systems. Angular velocity precision is around 10−10 rad s−1 at one second and 5×10−7 rad s−1 at one hour, which is comparable to fiber optical gyroscopes. While experimental validation is yet not available, the results shown here indicate the potential of opto-mechanical accelerometers as gyro-free inertial navigation sensors, provided the fundamental noise limit of the accelerometer is reached, and technical limitations such as misalignments and initial conditions errors are well controlled.

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Section: Introductionmentioning

confidence: 98%

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers

Sanjuán

Sinyukov

Warrayat

et al. 2023

Sensors

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Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements

Gräfe,

Pettas,

Dimitrov

et al. 2024

Wind Energ. Sci.

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Abstract. Floating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.

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Analytical Assessment of the Propagation of Colored Sensor Noise in Strapdown Inertial Navigation

Cited by 2 publications

References 20 publications

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers

Gyro-Free Inertial Navigation Systems Based on Linear Opto-Mechanical Accelerometers

Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements

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