Research on algorithms for optimizing absolute positioning accuracy evaluation indicators

Research on Algorithm Optimization Based on Absolute Positioning Accuracy Evaluation Index

Abstract: This article aims at the absolute positioning accuracy evaluation index in the positioning system, and improves positioning through algorithm optimization. System accuracy and stability. First, the absolute positioning accuracy evaluation index is introduced and analyzed in detail. Then, in view of the shortcomings of the evaluation indicators, a targeted algorithm optimization method is proposed, and the effectiveness of the algorithm optimization is proved through experiments. Finally, specific code examples are given to help readers better understand the implementation process of the algorithm.

Keywords: absolute positioning, accuracy evaluation, algorithm optimization

1. Introduction

With the development of mobile Internet, the application of positioning technology is becoming more and more widespread. In many application scenarios, such as navigation systems, logistics tracking, etc., the requirements for positioning accuracy are very high. Therefore, how to improve the accuracy and stability of the positioning system has become an important research direction.

The absolute positioning accuracy evaluation index in the positioning system is an important standard for measuring positioning accuracy. The absolute positioning accuracy evaluation index usually includes two aspects: error distance and error angle. Among them, the error distance represents the error of the target in the geographical location, and the error angle represents the error of the target in the azimuth angle. By measuring and analyzing these two indicators, the accuracy of the positioning system can be evaluated.

2. Analysis of absolute positioning accuracy evaluation indicators

The absolute positioning accuracy evaluation indicators mainly include the following aspects.

1. Distance Error
   Distance error is one of the commonly used evaluation indicators in absolute positioning systems. It represents the geographical deviation of the target. Distance error is usually measured in meters and can be calculated by taking the Euclidean distance between the target's actual position and its positioning result.
2. Angle error
   Angle error is another important evaluation index in the absolute positioning system. It represents the deviation of the target in the azimuth angle. Angular error is usually measured in degrees and can be found by calculating the difference between the target's actual azimuth and its positioning result.
3. Positioning accuracy
   Positioning accuracy refers to the ability of the positioning system to achieve accurate positioning of the target within a certain error range. Positioning accuracy is usually expressed as a percentage and can be calculated by counting the proportion of target positioning results within the error range.

3. Algorithm optimization method

Based on the definition and analysis of the above absolute positioning accuracy evaluation indicators, we can see that in the actual positioning system, due to the influence of various factors , the accuracy evaluation index may have certain errors. In order to improve the accuracy and stability of the positioning system, we can use the following algorithm optimization method.

1. Sensor fusion
   Sensor fusion refers to fusing the positioning results of multiple sensors to improve positioning accuracy and stability. Common sensors include GPS, IMU, geomagnetic sensors, etc. By comprehensively utilizing the data from these sensors, the error in position estimation can be effectively reduced.
2. Multipath Suppression
   In complex scenarios such as indoor environments or urban canyons, multipath effects will lead to increased positioning errors. Therefore, adopting multipath suppression algorithm is an important means to improve the accuracy of positioning system. Common multipath suppression algorithms include least squares method, Kalman filter, etc.
3. Data correction
   Data correction in the positioning system refers to reducing the positioning error by correcting the positioning results. Data correction can be achieved through outlier detection, outlier removal and other methods. For example, when the positioning result is too different from the actual position, the positioning result can be excluded from statistics, thereby improving positioning accuracy.

4. Experimental verification

In order to verify the effectiveness of algorithm optimization, we conducted a series of experiments. In the experiment, we used a set of real positioning data and compared the original positioning results with the positioning results optimized by the algorithm.

The experimental results show that through the algorithm optimization method, the absolute positioning accuracy evaluation index has been significantly improved. Both distance error and angle error have been effectively controlled, and the positioning accuracy has been significantly improved.

5. Code examples

In order to help readers better understand the implementation process of the algorithm, we provide the following code examples.

import numpy as np
import math

def calculate_distance(point1, point2):
    return math.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)

def calculate_angle(point1, point2):
    return math.atan2(point2[1] - point1[1], point2[0] - point1[0]) * 180 / math.pi

def optimize_algorithm(data):
    optimized_data = []
    for i in range(len(data)):
        if i == 0:
            optimized_data.append(data[i])
        else:
            last_point = optimized_data[-1]
            distance = calculate_distance(last_point, data[i])
            angle = calculate_angle(last_point, data[i])
            if distance < 1 or angle < 5:
                optimized_data.append(data[i])
    return optimized_data

# 测试代码
data = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8)]
optimized_data = optimize_algorithm(data)
print(optimized_data)

The above code is a simple implementation that optimizes the positioning results by calculating the distance and angle between points, and outputs the optimized positioning data.

6. Conclusion

Through algorithm optimization, we can effectively improve the accuracy and stability of the positioning system. This article introduces the analysis method of absolute positioning accuracy evaluation index and gives the specific implementation process of algorithm optimization. Finally, the effectiveness of the algorithm optimization is proved through experiments. It is believed that these works can further promote the research and application of positioning systems.

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