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The distribution of overall tomography inversion error is further shown in Fig. 6a.
Figure 14 shows that the inversion error decreases slowly when the number of sampling points is more than 30, and the inversion error may also fluctuate with the increase of the number of sampling points.
It is clear that output ENOB levels off when the quantization noise is low and the matrix inversion error dominates.
The temperature control accuracy of the FBG spectroscopy system is up to 0.03 °C, the corresponding potential atmospheric temperature inversion error is 0.04 K based on the numerical analysis of all-fiber Raman temperature lidar.
The optimization design of the tuning method of an all-fiber rotational Raman spectroscopy system is analyzed and tested for estimating the potential temperature inversion error caused by the instability of FBG.
The inversion error is calculated with respect to the control law and two matrices (inertia and dynamic damping matrices) which express the deviation of the estimated matrices relative to the calculated ones (the matrices from the nonlinear dynamics of MAV׳s) in conditions of absolute stability in closed loop system by using the Lyapunov theory.
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Moreover, a sliding-mode compensator is designed for possible dynamics inversion errors during the feedback linearization.
The inversion errors are shown in Tables 4, 5, and 6.
In the decoding side, the major error contributors are the basis approximation error and the numerical computation errors, including the matrix inversion errors and the matrix boundary problems.
Among these methods are LP, lagrange interpolation [55], time varying method [56], spline interpolation [57], matrix inversion [58], error locator polynomial (ELP) [59], iterative techniques [52, 60 65], and IMAT [25, 31, 66, 67].
Uncertainties in thickness were taken as the inversion formal errors, σ z, and had an average value of ~5 m (14%% of average thickness).
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