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A Feasibility Study of Machine Learning Based Coarse Alignment^{ †}

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## Abstract

**:**

## 1. Introduction

## 2. Traditional Coarse Alignment

## 3. Machine Learning Methodology

#### 3.1. Overall Approach

#### 3.2. Features Description

- Basic statistical features: Mean, Standard deviation, Variance, Minimum value, Absolute of the minimum value, Maximum value, Absolute of the maximum value.
- Advanced statistical features:
- Entropy: the amount of regularity and the unpredictability.
- Skewness: the asymmetry of the probability distribution.
- Kurtosis: the “tailedness” of the probability distribution.
- Energy: the sum of squares of values.
- Amplitude: the difference between the minimum and the maximum value.

- Time-Domain Features:
- Number of peaks: the number of peaks with defined minimum peak height and the minimum distance between peaks.
- Mean spectral energy: the mean spectral energy computation using one-dimensional discrete Fourier Transform.
- Mean crossing rate: the number of mean crossings.
- Zero-crossing rate: the number of sign changes.

## 4. Results and Discussion

## 5. Conclusions

## Conflicts of Interest

## References

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Parameters | Classical Method | RF |
---|---|---|

Convergence time | 2 s | 1 s |

MAE (deg) | 0.005 (±0.029) | 0.0039 (±0.0031) |

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**MDPI and ACS Style**

Zak, I.; Klein, I.; Katz, R.
A Feasibility Study of Machine Learning Based Coarse Alignment. *Proceedings* **2019**, *4*, 50.
https://doi.org/10.3390/ecsa-5-05735

**AMA Style**

Zak I, Klein I, Katz R.
A Feasibility Study of Machine Learning Based Coarse Alignment. *Proceedings*. 2019; 4(1):50.
https://doi.org/10.3390/ecsa-5-05735

**Chicago/Turabian Style**

Zak, Idan, Itzik Klein, and Reuven Katz.
2019. "A Feasibility Study of Machine Learning Based Coarse Alignment" *Proceedings* 4, no. 1: 50.
https://doi.org/10.3390/ecsa-5-05735