Discuss the future development trend of absolute positioning accuracy evaluation indicators

Discussion on the future development trend of absolute positioning accuracy evaluation indicators

Abstract: With the continuous development of precise positioning technology, the evaluation indicators of absolute positioning accuracy have gradually received attention and research. This article summarizes the current status of absolute positioning accuracy evaluation indicators and discusses its future development trends, including specific code examples.

Keywords: absolute positioning; accuracy evaluation index; development trend

1. Introduction

Absolute positioning refers to the ability to Accurately determine the location of a target object on Earth. In many fields, such as navigation, surveying, autonomous driving, etc., absolute positioning accuracy is an important consideration. However, due to the existence of various errors and interference factors, absolute positioning is often difficult to achieve accurately. Therefore, it is very important to accurately evaluate the absolute positioning accuracy.

2. Current status of absolute positioning accuracy evaluation indicators

At present, the indicators for evaluating absolute positioning accuracy mainly include positioning error, positioning error distribution, and the difference between positioning error and reference positioning. Among them, positioning error is the most basic evaluation index, which refers to the error between the positioning result and the real position. Positioning error distribution refers to the spatial distribution of positioning errors, which can be expressed in the form of error ellipses, error circles, etc. The difference between the positioning error and the reference positioning is the difference measured at different times in multiple positioning results.

3. Future development trends of absolute positioning accuracy evaluation indicators

1. Use more reference data

In order to improve the accuracy of absolute positioning accuracy evaluation , more reference data can be used for evaluation. For example, in GPS positioning, compensation data can be used to correct errors, thereby reducing positioning errors.

2. Combined with deep learning technology

Deep learning technology has been widely used in image recognition, speech recognition and other fields. In the field of absolute positioning, deep learning technology can be used to model and predict positioning errors, thereby improving the accuracy of accuracy evaluation.

3. Combined with statistical analysis methods

Statistical analysis methods can process and analyze a large amount of positioning data, providing a basis for subsequent accuracy evaluation. For example, statistical methods can be used to analyze the distribution of data and determine appropriate evaluation indicators.

4. Develop a more powerful evaluation index library

Currently, the library of absolute positioning accuracy evaluation indicators is still relatively small, and may not be comprehensive enough for accuracy evaluation in different situations. Therefore, the future development trend is to develop more and more powerful evaluation indicators to adapt to the needs of different fields.

4. Code Example

The following is a sample code that demonstrates how to use Python to evaluate absolute positioning accuracy:

import numpy as np

# 定义真实位置
true_position = np.array([100, 200])

# 定义测量结果
measurement = np.array([105, 210])

# 计算定位误差
error = np.linalg.norm(true_position - measurement)

# 打印结果
print("定位误差为：", error)

Through the above code, the measurement results can be calculated positioning error from the true position.

Conclusion

With the continuous development of absolute positioning technology, the research on absolute positioning accuracy evaluation indicators is also deepening. In the future, with the application of more new technologies, the absolute positioning accuracy evaluation indicators will be more accurate and comprehensive. At the same time, through the application of big data and artificial intelligence, the accuracy of precision evaluation can be further improved.

References:

[1] Feng M, Guo J, Zhang Z, et al. An Evaluation Framework for GNSS Positioning Accuracy in Vehicular Networks[J]. Sensors, 2018, 18( 10): 3483.

[2] Li H, Yuan Y, Sun S, et al. A Continuous Accurate Positioning Algorithm for Large Tunnel Construction Based on Multipath[J]. ISPRS International Journal of Geo-Information, 2019, 8(2): 93.

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