Method for decomposing laser radar waveform data based on particle swarm optimization

Abstract

The invention provides a three-dimensional laser etch decomposing algorithm based on the combination of particle swarm optimization and LM algorithm (Levenberg-Marquardt Algorithm). The method comprises the steps of smoothly denoising; detecting the peak value; decomposing the waveform; fitting. According to the method, the threshold is set and the threshold is detected, so as to determine the quantity of smoothly denoised wave with good signal-to-noise ratio; the particle swarm optimization is performed to acquire the rough strength parameter value and the rough wide parameter value of single waveform to be used as the initial values of the LM algorithm to improve the decomposing precision as well as reducing the error influence of the initial value.

Description

technical field [0001] The invention belongs to the technical field of laser data processing, and specifically refers to a method for decomposing laser radar waveform data based on a particle swarm algorithm. Background technique [0002] The laser echo waveform contains a large amount of surface information inside the laser spot, and the fine features of the surface target can be extracted by analyzing the echo waveform. Therefore, finding an effective and accurate echo decomposition algorithm is a topic worthy of research. [0003] Background noise will cause random changes in waveform amplitude, and too many burrs may cause detection errors, so smoothing filtering has a greater impact on parameter fitting. However, some filtering algorithms will lead to amplitude distortion and excessive smoothing, resulting in loss of details or loss of peak points, so it is necessary to compare and choose a better algorithm. [0004] In the existing algorithms, the accuracy of the LM ...

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Problems solved by technology

[0003] Background noise will cause random changes in waveform amplitude, and too many burrs may cause detection errors, so smoothing filtering has a greater impact on parameter fitting

However, some filtering algorithms will lead to amplitude distortion and excessive smoothing, resulting in loss of details or loss of peak points, so it is necessary to compare and choose a better algorithm

[0004] In the existing algorithms, the accuracy of the LM algorithm depends on the initial value. If the deviation of the initial value is large, it is difficult to obtain an accurate fitting effect.

In the previous algorithms, the pulse width parameters were often determined by the detection of the zero-crossing inflection point, but the actual laser model is not a standard Gaussian model and is not symmetrical. Therefore, the echo parameters obtained by this method are Not accurate as an initial value

[0005] To sum up, the current laser echo decomposition algorithm is not ideal in the case of many parameters and superimposed broadening, so the vondrak smoothing algorithm with better effect is more suitable for echo decomposition, and the improved particle swarm optimization algorithm is used to obtain its The parameter value as the initial value of the LM algorithm is a way to optimize its results

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