Necessity and Feasibility of Outdoor Sports Teaching for Teenagers Integrating the Wireless Communication Network

Abstract

With the rapid development of mobile communication technology, the research on wireless positioning technology is also deepening and improving. As one of the most popular services, positioning service has a very broad development prospect. With the popularity of outdoor sports among young people in recent years, colleges and universities have also paid more attention to outdoor sports teaching. But in outdoor sports, there will be many risks, so it is necessary to integrate positioning methods in wireless communication networks to ensure the safety of young people. The purpose of this paper is to study the necessity and feasibility of outdoor sports teaching for teenagers integrating wireless communication networks. This paper proposes a multireference point localization method, a signal fingerprint localization method, and a level-based localization method based on a converged wireless communication network. The experimental results of this paper show that 25 boys and 20 girls are very interested in outdoor sports, 20 boys and 13 girls are more interested, and 2 boys and 5 girls are not interested at all. Overall, far more people are interested in outdoor sports than those who are not. In terms of gender, boys are more interested in outdoor sports than girls, but the difference is not significant. It can be seen that today’s young people are still very interested in outdoor sports, and it is necessary to implement outdoor sports teaching.

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1. Introduction

With the development of society, people’s quality of life is getting better and better. Parents all want the best children, which leads to the fact that children today seem to be in good health, but in fact they have poor physical health due to lack of exercise. And traditional sports activities have been unable to attract the attention of young people. With the rise of outdoor sports, teenagers are very interested in this kind of physical exercise that can be close to nature. It can not only appreciate the natural scenery but also cultivate its own physical fitness and self-care ability.

The biggest difference between outdoor sports and traditional sports is that outdoor sports are a way of life that is closely related to outdoor sightseeing, interpersonal communication, culture, and adventure. It allows people to eliminate boring social life, feel the charm of nature in the most relaxed state, and return to social work with a more optimistic and positive attitude. Outdoor sports were introduced into China at the end of the century. With the development of people’s national outdoor sports, it has been supported by more and more people today.

The innovations of this paper are as follows: (1) introduce the relevant theoretical knowledge of youth outdoor sports teaching and wireless communication network. And based on the wireless communication network, the multireference point positioning method, the signal fingerprint positioning method, and the level-based positioning method are proposed, and the positioning accuracy of these three methods is analyzed. (2) The three positioning methods are experimentally compared. Through analysis, it can be known that the signal fingerprint positioning method is much more accurate than the multireference point positioning method and the level-based positioning method and is more suitable for application in the safety guarantee of outdoor sports teaching.

2. Related Works

Outdoor sports are fashionable. Its rise fully reflects the progress of people’s society and economy. It is the result of the progress of material civilization and spiritual civilization. Outdoor sports are widely carried out in Chinese schools. Yao et al. found that cognitive nodes (CNs) are clustered based on their received power levels to improve perception performance. In the CSS scheme, time resources are limited and shared by energy harvesting, spectrum sensing, and data transmission. By constructing a virtual cognitive node (FCN), the optimal local detection threshold is found [1]. Emery found that the public health impact of injuries in children and youth sports is enormous. Injury prevention in sports for children and adolescents is emerging as a major public health concern, as long-term health effects and social burdens are associated with early osteoarthritis following outdoor exercise [2]. The purpose of Kim’s study was to demonstrate the relationship between outdoor exercise participant perceptions and coaches’ safety awareness, safe practice actions, and exercise procedures. He sampled 266 people participating in outdoor sports through convenience sampling. The results showed that there was a significant relationship between the safety awareness of the coaches and the safety practice behaviors perceived by the adventure sports participants [3]. Frontini et al. found that in today’s society, the proportion of physical inactivity continues to rise, so it is necessary to understand the motivation for outdoor exercise and exercise. They surveyed 67 outdoor exercisers in gym, nature, and adventure sports modes. The results show that the motivation and effect of outdoor exercise are better than those of gym exercise [4]. Frühauf et al. found an inseparable relationship between risk-taking behavior and the factors that adolescents participate in high-risk sports. While most research on adolescent risk-taking has addressed negative attitudes toward risk-taking, they sought to highlight that risk-taking and high-risk sports participation have a huge impact on adolescents’ physical and mental health. Outdoor sports such as mountain biking, freestyle skiing, snowboarding, and rock climbing are very popular among adolescents, and these sports also have the potential for multiple psychological benefits [5]. The purpose of Maleta’s study is to provide an overview of the epidemiology of injury in extreme sports, and he discusses content related to adventure and extreme sports, including injury outcomes, risk factors, predisposing events, and prevention. In view of the fact that the injuries caused by outdoor sports may change people’s lives, we should actively prevent the risks that will occur in outdoor sports [6].

In the process of the continuous destruction of the natural environment, the relationship between man and nature is gradually estranged, and the physical activity and happiness experience of human beings are gradually reduced, which brings huge abnormal changes to human body and mind. As a result, teenagers are more and more interested in outdoor sports, and schools have begun to attach importance to the implementation of outdoor sports teaching. However, if people simply teach outdoor sports without any auxiliary means, students may encounter many unknown dangers. Scholars have only described the dangers that can occur in outdoor sports but have not suggested how to solve these difficulties. Only carrying out outdoor teaching can no longer guarantee the safety of students. Therefore, this paper combines the positioning function in the wireless communication network to assist the implementation of outdoor sports teaching for students, improve the efficiency of teaching, and ensure the safety of young people.

3. Positioning Method of the Integrated Wireless Communication Network

3.1. Necessity and Feasibility of Outdoor Sports Teaching for Teenagers

As an extension of outdoor sports courses in middle schools, outdoor sports should not only comply with the basic content of competitive outdoor sports such as walking, running, jumping, and throwing in the original outdoor sports courses but also extend to society and nature [7]. Outdoor sports are a group of sports events with adventure or expedition held in the natural environment. These include mountaineering, rock climbing, cliff descent, kayaking, diving, sailing, orienteering, and more. A unique outdoor sports program reformed on behalf of the outdoor sports class, so that students tirelessly adhere to long-term endurance sports. On a certain terrain, while exercising their physical and willpower, they go back and forth in natural scenery such as jungles, mountains, small rivers, lakes, and other fresh air. In judgment and route selection, they can happily accept the knowledge of nature and broaden their horizons [8]. Outdoor sports are shown in Figure 1.

[figure(s) omitted; refer to PDF]

As shown in Figure 1, the attitude of physical activity is the comprehensive performance of cognition, emotion, and action necessary to participate in outdoor sports. Interest is the subjective factor that motivates people to actively participate in an activity. In order to investigate the degree of interest of teenagers in outdoor sports, the interest of 68 boys and 61 girls in outdoor sports and the emotional experience of students when they usually participate in outdoor sports are shown in Tables 1 and 2.

Table 1

Adolescents’ interest in outdoor sports.

Object of investigation	Boys	Girl	Total
Very interested	25	20	45
More interested	20	19	39
General interest	16	13	29
Not interested	5	4	9
No interest	2	5	7

Table 2

Students’ emotional experience questionnaire when they usually participate in outdoor sports.

		Very happy	Joy	No feelings	Hate
Boys	Number of people	30	18	15	5
	Percentage	44.1%	26.5%	17.6%	11.8%
Girl	Number of people	20	14	12	15
	Percentage	32.6%	22.9%	19.7%	28%

As shown in Tables 1 and 2, interest has important significance in people’s practical activities, which can make people concentrate and produce a happy and tense psychological state. Interest is the conscious tendency of people to engage in specific activities. As the internal driving force of physical movement, it is closely related to the physical needs of students. Physical needs may be direct or indirect: direct physical needs emphasize the charm of physical movement itself, and indirect physical needs emphasize the meaning and effect of physical training [9].

Cultivation of survivability: in the school’s outdoor sports area, the teaching objectives of the new 21st century curriculum have been revised to focus only on outdoor sports skills. Starting from the concept of outdoor sports, outdoor sports are not only activities that can be performed in outdoor gyms. Living a prosperous life with a healthy body is the ultimate goal of outdoor sports, which can improve teenagers’ hands-on ability and ability to survive [10].

Cultivation of spiritual strength: today’s teenagers not only do not need to do hard work but also lack mental exercise. Due to the excessive protection of their parents, today’s teenagers’ psychology has also become abnormally fragile [11]. It is difficult for the children in the greenhouse to exert the original power of human nature. Because they are too protected, they cannot get the opportunity to think and judge for themselves in the complex social environment in the process of growth and development, while outdoor sports can cultivate their strong willpower.

3.2. Outdoor Sports Teaching for Teenagers Integrating the Wireless Communication Network

(1) Outdoor sports are adventurous and stimulating

Outdoor sports take place outdoors, nature is constantly changing, and there are many factors beyond your control. Sudden natural disasters, animal intrusions, and human responses in a variety of environments are unpredictable prior to activity. So outdoor sports are more risky and dangerous than other sports. Therefore, it is necessary to communicate with teenagers who are exercising outdoors at any time through an integrated wireless communication network.

(2) Outdoor sports have a certain crisis

People who are engaged in outdoor sports are far more capable of surviving in abnormal environments than ordinary people. In the wild, a variety of geographic difficulties must be overcome. Mountains, lakes, plateaus, rapids, etc. have high requirements for students. At the same time, in hot summer, the natural environment such as severe cold, wind, rain, and snow can improve the adaptability of students and also strengthen the immune system of adolescents to adapt to the environment. But because of this, young people are very likely to accidentally fall into danger or get lost, so it is necessary to integrate wireless communication networks for positioning.

(3) Outdoor sports are unpredictable

The venue for outdoor sports is natural, so the safety rate is an important factor to make this sport run smoothly. Although there are many factors that affect the safety of outdoor sports, they are unpredictable [12, 13]. In outdoor sports, there will be many personal safety issues. Therefore, it is necessary to combine modern technology-wireless communication network for monitoring and rescue.

3.3. Acquisition of Mobile User Behavior Data

Wireless communication refers to the long-distance transmission communication between multiple nodes without conductor or cable propagation, and wireless communication can be carried out by radio. The wireless communication network has experienced rapid development for more than 20 years, its network technology is becoming more and more mature, and the wireless communication network has been successfully commercialized. The current mobile communication network has various network heterogeneity coexistence, the need for network service innovation, and diversified characteristics and requirements. In recent years, the research on location services in wireless communication networks has been widely discussed. The so-called wireless positioning method is to use the wireless communication technology to know the position information of the mobile station, and its commercial value is one of the reasons for the vigorous development of this technology [14].

The collection of user behavior data is the basis of user behavior analysis. Due to the complex and diverse user behavior and the huge amount of data, the cloud computing network is used for collection and storage, which can reduce costs and improve efficiency [15]. According to the current network situation, user data does not come from a single data source, but from multiple data sources. Therefore, it is necessary to unify the multiple data sources to form the same platform for data collection. The user behavior data collection framework is shown in Figure 2.

[figure(s) omitted; refer to PDF]

As shown in Figure 2, the data preprocessing module is responsible for simple processing of the collected data, as well as data format conversion, to prepare for cloud computing and data storage modeling. The background data storage module is responsible for storing the preprocessed data in the specified location or database according to the specified strategy. Connecting, collecting, and storing the intelligent library mainly provide access and storage of intelligent analysis library resources. The intelligent analysis and decision-making module is responsible for calling other modules to realize the intelligent analysis and decision-making of the entire data collection and generate a task output. The management execution module is responsible for executing the output, configuring the corresponding parameters, and completing the data acquisition task [16].

3.4. Positioning Algorithm of Wireless Positioning Technology

User location and movement tracking are important aspects to reflect user behavior. Therefore, users need to be positioned. The terminal side can obtain location information through external devices such as GPS, but from the perspective of large-scale user data acquisition requirements, wireless positioning must be used [17].

(1) Multireference point positioning method

The algorithm of the multireference point positioning method is easy to understand, the calculation amount is small, and the positioning accuracy depends on the layout density of the beacons. In the multipoint positioning method based on the receiving level, the whole positioning accuracy is not high due to the influence of inaccurate model parameters. Therefore, this paper considers setting multiple reference points within a certain range, usually a fixed test mobile station. Since the position of the reference point is known, the estimated value for the model correction value in this range can be calculated, and the model parameters can be corrected in real time, thereby further improving the positioning accuracy [18].

The Hata model refers to a model that easily uses the parameters in the path loss calculation formula, such as operating frequency, antenna effective height, distance, and coverage area type. Using the joint calculation of multiple reference points, the model parameters can be further optimized, the form of the Hata model is retained, the parameters are undetermined, and the calculation formula is $$\begin{array}{}\text{(1)}& L_p=K_1+K_2\log f-K_3\log h_0+K_4-K_5\log h_b-A_{hm}.\end{array}$$

Through joint calculation of multiple reference points around, more realistic model parameters can be obtained, so as to achieve more accurate positioning services. The multireference point positioning method is shown in Figure 3.

[figure(s) omitted; refer to PDF]

As shown in Figure 3, through the precise position of the reference point and real-time measurement, more accurate model parameters in the area can be obtained. It is more accurate to use the calculated distance of the mobile station, and the positioning accuracy with an error of less than 150 meters can be achieved. Therefore, this method can obtain more accurate results [19, 20]. However, this method needs to lay out reference points equivalent to the number of BSs, and the real-time calculation amount of the model correction process is large.

(2) Signal fingerprint matching method

Location fingerprint matching is a commonly used method, which is based on wireless communication and network technology, and has many characteristics such as easy implementation, low cost, and low requirements for the time synchronization accuracy of access points. In order to overcome the problems of real-time calculation and layout of fixed reference points, the method of block fingerprint matching is adopted. The method of signal fingerprinting is to use the signal strength to estimate the user’s location. Due to the complex urban terrain, many buildings, and the obvious diversity of signal characteristics, this method can obtain better performance in dense cities [21]. In addition, more importantly, this method does not require any other additional facilities and does not need to change the existing network structure and signaling process, and the implementation cost is small. The signal fingerprint matching method is shown in Figure 4.

[figure(s) omitted; refer to PDF]

As shown in Figure 4, the implementation process of the signal fingerprinting method can be divided into two stages, namely, the offline stage and the online stage. In the offline phase, the information of the signal strength vector of each geographic block is collected first, and the signal strength vector includes signal reception strength, cell number, and so on. The signal strength vector and the location information of the geographic segments are stored in the database. Normalization is a way of simplifying the calculation; that is, the dimensional expression is transformed into a dimensionless expression and becomes a scalar. This method is often used in various calculations. In the real-time stage, the difference distance between the signal strength vector of the terminal and the normalization of each block in the database is calculated as $$\begin{array}{}\text{(2)}& L_p=\frac{1}{N}{\sum _{i=1}^N{s_i-s_i^m}^p}^{1/p}.\end{array}$$

Among them, $N$ is the number of BS signals received in the location block, that is, the number of all adjacent base stations that can be monitored, and $p$ is the scale factor. $s_i$ is the weight of the received strength of the $n$th signal, which is used to correct the signal strength of the $n$th base station.

RSS builds a technical platform for rapid information dissemination, making everyone a potential information provider. The training data set can get the location information and the MR mapping relationship, which is stored in the database. Since MR can record the RSS of the 6 adjacent base stations with the strongest signal strength, the 6 RSSs are used here as the main recording objects of the signal fingerprint, as shown in Figure 5.

[figure(s) omitted; refer to PDF]

As shown in Figure 5, the RSS of the neighboring base station and the corresponding BCCH frequency points are parsed in the MR under the record A point. The BCCH frequency is a “point-to-multipoint” unidirectional control channel, which is used by the base station to broadcast public information to the mobile station and transmit the public control information of the system. The recorded data are statistically processed here, because some BCCHs with weak RSS may appear alternately due to the random fluctuation of the signal. Figure 5 shows the MR neighbor base station information parsed from another location B close to A during the real-time computation phase. Different neighboring cells or neighboring base stations in MR and their signal fingerprints calculate the difference distance between different location points and collection points and then compare the minimum difference distance. Because this method does not use exponents but only uses addition and multiplication operations, the calculation amount is small, and the performance is high.

(3) Positioning method of the reception level

The positioning method of the receiving level obtains the bandwidth of the received signal to be measured. Find the reference voltage value corresponding to the bandwidth in the preset received signal bandwidth calibration table; perform digital-to-analog conversion on the reference voltage value. In the above method, the path loss from the base station to the mobile station is calculated by the adjacent cell reception level in the MR frame structure sent by the mobile station. Then, according to the radio wave propagation model, the distance from the base station to the mobile station is calculated, and the intersection of the three circular areas is the location of the mobile station, as shown in Figure 6.

[figure(s) omitted; refer to PDF]

As shown in Figure 6, the wireless propagation model is a model designed for better and more accurate research on wireless propagation. Communication channels are divided into wired channels and wireless channels according to the transmission medium. Only when the radio wave propagation model matches the actual geographical environment well can the positioning accuracy be high. However, because the receiving level is often time-varying, the positioning accuracy is also deteriorated by this factor.

Through the base station transmit power $P_0$ and the reception level $P_r$ contained in the MR frame structure, the radio wave propagation model is used as in $$\begin{array}{}\text{(3)}& P_r\text{dBm}=P_0\text{dBm}-10n\cdot \log \frac{d_1}{d_0}+A_{\sigma }.\end{array}$$

Obtain the required path loss, and then, according to the path loss model such as $$\begin{array}{}\text{(4)}& L_p=K_1+K_2\log f-K_3\log h_0-A_{hm},\end{array}$$

the distance $h_0$ from the BS to the MS is estimated. The coordinates of the base station $\text{BS}$ are defined as $a_i,b_i$, and the distance from the BS to the MS is $h_0$. The calculation formula is $$\begin{array}{}\text{(5)}& d_i=\sqrt{{a-a_i}^2+{b-b_i}^2}.\end{array}$$

3.5. Position Prediction Method Based on Group Movement

Information entropy is a rather abstract concept in mathematics. Here, information entropy may be understood as the probability of occurrence of certain information (the probability of occurrence of discrete random events). The more orderly a system, the lower the information entropy. In information theory, entropy is used to measure the uncertainty of random variables. The concept of information entropy is borrowed here to determine the uncertainty of the user’s trajectory. The smaller the movement trajectory entropy, the more stable the user’s mobility. The user’s motion entropy is defined here to describe the user’s motion stability.

Let $A_i$ be a random variable, representing the position of a certain user at a moment, and the motion entropy is defined as $$\begin{array}{}\text{(6)}& SA=-\sum _{a\in A}pa{\log }_{2}pa.\end{array}$$

In the above formula, $pa$ represents the probability that the user is at position $a$. For the stochastic process ${\log }_{2}pa$, the user’s motion entropy can be written as $$\begin{array}{}\text{(7)}& S=\lim \frac{1}{n}S{A}_1,A_2,\cdots ,A_n.\end{array}$$

Correspondingly, the kinematic entropy of a group can be similarly defined. For a system with users, the system entropy is $$\begin{array}{}\text{(8)}& S=\frac{1}{n}S{U}_1,U_2,\cdots ,U_n.\end{array}$$

In the formula, $S{U}_1,U_2,\cdots ,U_n$ above represents the joint entropy of all users. According to the property of joint entropy, it is $$\begin{array}{}\text{(9)}& S{U}_1,U_2,\cdots ,U_n\le \sum _i^{N}S{U}_i.\end{array}$$

The above formula holds if and only if all users are independent of each other. According to the previous analysis, the movements of users are not independent of each other, which means that the amount of location information describing a group is less than that describing all users in the group separately. That is, the motion entropy of the group is smaller than the sum of the motion entropy of all users in the group.

Ideally, they have exactly the same trajectory; then, their conditional entropy is 0, so the position of each person can be inferred, as long as it is enough to know any one of them. If the motion entropy of user $i$ is represented by $S{U}_i=S_0$, then only $S_0$ bits are needed to describe the motion trajectory of this group, instead of ${NS}_0$ bits. In this way, the lower bound of the group motion entropy is obtained, which indicates that the amount of information required to describe a group can be greatly reduced.

A method that exploits the information margin has been proposed to predict the user’s location. According to the above-mentioned content, groups exist widely in daily user behavior, but it is impossible to determine how many groups there are and how many users there are in the group based on only one user. For $N$ users, if they belong to a group, then they should exercise like the same person as much as possible. Motion entropy is used to measure the dispersion of these users to determine whether they should belong to the same group. Referring to the definition of entropy, the dispersion degree of $N$ users is defined as $$\begin{array}{}\text{(10)}& DN=Sa=-\sum _{i}p{a}_i{\log }_{2}p{a}_i.\end{array}$$

Among them, $p{a}_i$ is $$\begin{array}{}\text{(11)}& p{a}_i=\frac{N_i}{N}.\end{array}$$

In the above formula, $a_i$ represents location $i$ and $N_i$ is the number of users at location $a_i$. Obviously, if all users are in the same location, then the dispersion of these users is 0 and the smallest. If no two users are in the same location, then these users have the largest dispersion which is equal to ${\log }_{2}N$.

Considering that the user’s location changes over time, the sum of the user’s dispersion is $$\begin{array}{}\text{(12)}& \text{sum}D{N}_i=\sum _{t=t_i}^{t_j}D{N}_t.\end{array}$$

With the above definitions, two guidelines are given:

Given $t_i$ and $t_j$, the $N$ determination algorithm should find $N$ users such that formula (13) holds: $$\begin{array}{}\text{(13)}& \text{sum}D{N}_i=\min \sum _{t=t_i}^{t_j}D{N}_t.\end{array}$$

The location of the group should be determined by the common location of most users, which is $$\begin{array}{}\text{(14)}& A_i=\arg \max p{A}_i.\end{array}$$

According to the actual situation, the following thresholds are defined to determine group users. If a user belongs to a certain group, it should satisfy $$\begin{array}{}\text{(15)}& PA=A_{i}T=t_i=\frac{\text{count}A=A_i,T=t}{\text{count}T=t_i}\succ P_{\text{threshold}}.\end{array}$$

In the above formula, the conditional probability $PA=A_{i}T=t_i$ represents the probability that the user is at the position $A_i$ at time $t_i$. $A_i$ belongs to the location area set covered by the base station, and $\text{count}T=t_i$ is the number of all calls made by the user at the moment.

The user’s position is determined according to the inference of criterion 2: the users in the group basically move according to the movement trajectories of most users in the group. To test this inference, the maximum $P{a}_i$ for each group is calculated as $\max p_i$ over time, as shown in Figure 7.

[figure(s) omitted; refer to PDF]

As shown in Figure 7, in practical applications, this method can be used to predict the location of a user according to the location of the group where the user is located and provide users with more location information services.

In the positioning system, at least three base stations must be used to estimate the position of the mobile station, so one can find the distance between the three base stations and the mobile station by $$\begin{array}{}\text{(16)}& \begin{array}{c}\sqrt{{A-A_X}^2+{B-B_X}^2}=d_1,\hfill \\ \sqrt{{A-A_Y}^2+{B-B_Y}^2}=d_2,\hfill \\ \sqrt{{A-A_C}^2+{B-B_C}^2}=d_3.\hfill \end{array}\end{array}$$

$d_1$, $d_2$, and $d_3$ are the distances between the mobile station and the three base stations, $A,B$ is the position of the unknown MS, and $B-B_X$ is the position of the known base station.

The TOA positioning system utilizes the current RSS-based fingerprint positioning system, takes the measured distance value obtained by TOA as a reference signal, and improves the positioning accuracy by using the continuity of the wireless signal. The experimental data show that the system achieves a certain accuracy in a complex environment and has certain practical significance. In the TOA positioning system, by multiplying the measured time by the electromagnetic wave propagation speed, the distance measured at the $m$th base station time $t$ can be obtained as $$\begin{array}{}\text{(17)}& r_{m}t=d_{m}t+m_{m}t+m{\text{los}}_{m}t.\end{array}$$

Among them, $d_{m}t$ is the actual distance between the $n$th base station and the mobile station and $m{\text{los}}_{m}t$ is the error of base station $m$ at time $t$.

In the AOA positioning system, the measured incident angle from the $M$th base station is $\theta$, which can be expressed as $$\begin{array}{}\text{(18)}& {\theta }_{m}t={\vartheta }_{m}t+\theta n_{m}t+\theta {\text{los}}_{m}t.\end{array}$$

${\vartheta }_{m}t$ is the actual incident angle, and $\theta n_{m}t$ is the measurement error.

The root mean square error is usually used to evaluate the accuracy of the positioning technology, and the formula for the root mean square error is $$\begin{array}{}\text{(19)}& \text{RMSE}=\sqrt{E{a-\stackrel{\wedge }{a}}^2+{b-\stackrel{\wedge }{b}}^2.}\end{array}$$

Among them, $a,b$ is the actual position of the signal source, and coordinate $\stackrel{\wedge }{a},\stackrel{\wedge }{b}$ is the estimated position of the signal source.

4. Experiment of the Positioning Method Based on the Wireless Communication Network

4.1. Comparison of Positioning Accuracy of Three Positioning Methods

General experiments are carried out on the receiving level positioning method, the positioning method of multiple reference points, and the signal fingerprint positioning method in the field mobile network. In the test, 3 test mobile stations, 10 test points, and multiple test periods are used. The fingerprint features have good geographical discrimination, and the test results of different periods have little effect on the discrimination of fingerprints. The test compares the probability that the multireference point method, the reference point correction method, and the signal fingerprint matching method achieve the standard accuracy, respectively. The positioning accuracy comparison of the three positioning methods is shown in Table 3.

Table 3

Comparison of positioning accuracy of the three positioning methods.

Distance	Receiving level positioning	Multiple reference point positioning	Fingerprint matching and positioning
50 m	6%	45%	44%
100 m	10%	58%	60%
150 m	12%	70%	69%
200 m	17%	86%	88%
250 m	25%	92%	92%
300 m	35%	99%	99%

As shown in Table 3, the results show that the reference point correction method and the fingerprint matching method have similar positioning accuracy due to the similar implementation principles. While the basic positioning method of the receiving level has a large error, the accuracy can only reach 70% within the accuracy range of 150 m, and the accuracy is only 35% within the accuracy range of 300 m. In terms of the complexity of the scheme, the multireference point positioning method mainly has the higher cost of laying reference point facilities.

This paper also compares the dimension selection of fingerprint features, as shown in Table 4.

Table 4

Dimension selection of fingerprint features.

Distance	3	4	5	6
50 m	23%	39%	39%	43%
100 m	26%	46%	57%	59%
150 m	35%	54%	66%	68%
200 m	55%	76%	85%	77%
250 m	77%	85%	89%	91%
300 m	83%	95%	98%	98%

As shown in Table 4, the increase in dimension can improve the positioning accuracy, but it also increases the pressure and calculation amount of the database. After comprehensively considering the amount of calculation and the positioning accuracy, the signal fingerprint positioning method is selected as the dimension, which has good satisfaction. Selecting more level reference values has higher accuracy, thereby improving the positioning accuracy. Therefore, the experiment shows the high accuracy and feasibility of the signal fingerprint localization method.

In this paper, the comparative experiment of the system on the accuracy of the signal fingerprint positioning algorithm and the other two positioning methods is tested, as shown in Figure 8.

[figure(s) omitted; refer to PDF]

As shown in Figure 8, it shows the accuracy of the localization method based on signal fingerprints. As can be seen from the figure, the probability of positioning accuracy of 40 meters is about 90%. This result can meet the needs of the system. Compared with other algorithms, it is found that the accuracy of other algorithms is lower than that of the fingerprint positioning algorithm. It can be seen that the signal fingerprint positioning accuracy is higher than that of other positioning methods.

4.2. Simulation of the Three Positioning Methods in Different Environments

The positioning method of the receiving level, the signal fingerprint matching method, and the multireference point positioning method are, respectively, carried out for the positioning simulation of the same position. In different cases, the number of base stations and the measurement error are, respectively, increased, and the distribution function of the root mean square error is observed through multiple simulations.

Simulations are performed in two environments, namely, (1) an environment without NLOS error and (2) an environment with NLOS error—adding the NLOS error, the angle error is a normal distribution distributed in the interval $‐\pi ,\pi$, and the measurement error is a Gaussian distribution. In these two environments, the simulation results of the three positioning algorithms are shown in Figure 9.

[figure(s) omitted; refer to PDF]

As shown in Figure 9, when the number of base stations used is more, the estimated accuracy is relatively higher. It can be seen from the simulation results that when the received signal is a direct wave LOS, the estimated error is small. When the received signal is non-line-of-sight NLOS, it can be seen from the simulation in environment 2 that the error is large. In particular, the receiving level positioning method, the multireference point positioning method, and the signal fingerprint matching method positioning technology have better accuracy.

It can be seen from the simulation diagram that the signal fingerprint matching method algorithm can effectively improve the positioning accuracy. Overall, this algorithm can suppress the impact of base stations, especially when the number of base stations participating in the simulation increases. However, the number of base stations participating in the simulation cannot be increased arbitrarily, and the time used for positioning also increases with the increase in the number of base stations. Therefore, a balance must be found between the number of base stations and the positioning time, in order to maximize the utilization of the positioning algorithm.

5. Conclusions

With the implementation of school outdoor sports reform and the continuous improvement of the national economy, people’s health awareness and sports methods have undergone great changes. The unique charm of outdoor sports can make up and improve the shortcomings of indoor sports. Students’ enthusiasm for outdoor sports enriches their spare time life and cultivates young people’s ability to adapt to society. However, in outdoor sports, there will inevitably be many accidents, and teachers are also worried about students getting lost. Therefore, in the outdoor sports teaching, the wireless communication network should be integrated to contact the teenagers in time and locate them. In this paper, several positioning methods in wireless communication networks are introduced in detail, and the necessity and feasibility of outdoor sports teaching for young people integrated with wireless communication networks are also described in detail. In order to choose a positioning method with higher positioning accuracy, the three positioning methods proposed in this paper are tested and analyzed in the experimental part. Finally, it is found that the accuracy of the signal fingerprint positioning method proposed in this paper is higher than that of the multireference point positioning method and the level-based positioning method. The authors’ knowledge is limited, so there will be some errors in the experimental part, but the authors will definitely keep improving in the next work.

Acknowledgments

This work is supported by the open fund project of Hubei Leisure Sports Development Research Center (no. 2020Y018).

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