Speed and acceleration calculation method and measurement device based on regularization algorithm

Abstract

The invention relates to a speed and acceleration calculation method and measurement device based on a regularization algorithm. The speed and acceleration calculation method comprises the following steps: 1) obtaining observation position data or displacement data; and 2) calculating the speed and the acceleration by using the position data or the displacement data and adopting a regularization method. According to the invention, the technical problem that the existing speed and acceleration measuring equipment greatly amplifies noise under the condition of high sampling rate is solved; and the problem of calculating the speed and the acceleration based on position data or displacement data is converted into a typical Volterra first-type integral equation, and then a regularization method is utilized, so that noise amplification is inhibited, and speed and acceleration signal values are accurately extracted.

Description

technical field [0001] The invention relates to a velocity and acceleration calculation method and a measuring device based on a regularization algorithm, belonging to the field of velocity and acceleration calculations. Background technique [0002] Currently, there are various velocity and acceleration hardware devices on the market, such as piezoelectric, piezoresistive, and capacitive acceleration measurement devices, as well as GNSS-based velocity and acceleration hardware devices. The working process of these types of measuring equipment is as follows: First, the original measurement data is obtained according to a certain sampling frequency and converted into equivalent position data or displacement data; then, the velocity and acceleration can be calculated and output by using the difference method. For acceleration hardware devices, the displacement of the proof mass (proof mass) under the hardware mechanical framework is usually used to transform and calculate the ...

AI Technical Summary

Problems solved by technology

[0004] First, the problem of calculating velocity and acceleration based on position data or displacement data is transformed into Volterra’s first type integral equation, which is a typical ill-posed problem. As the sampling rate increases, noise will be greatly amplified, resulting in velocity and acceleration signals All submerged in noise, even the output velocity and acceleration seem to be all noise, it is difficult to extract the correct velocity and acceleration signals from the position data or displacement data, or even no correct velocity and acceleration signals at all, refer to figure 1 speed graph for and figure 2 The accelerogram; use the position data obtained by GNSS to calculate the velocity and acceleration signals of the moving body. It can be seen from the figure that almost all of them are noise, and the signals are completely submerged in the noise. The speed and acceleration of the moving body show instantaneous Random height changes, without any regularity of speed and acceleration signals

[0005] Secondly, if the sampling rate is reduced, the noise can be controlled. However, the low sampling rate means interval averaging, resulting in distortion of the velocity and acceleration signals. Refer to image 3 speed graph for and Figure 4 the acceleration graph;

[0006] Finally, the low sampling rate also means that the user cannot get the velocity and acceleration signals between two samples, especially the instantaneous velocity and acceleration signal values

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