A wireless signal propagation model correction method and system supporting segmented calculation

Abstract

The present invention discloses a wireless signal propagation model correction method and system supporting segmented calculation, and relates to the technical field of wireless signal propagation. Firstly, a segmented propagation model is constructed according to a signal measurement value and a signal loss value. Then, according to the actually measured signal value, the distance between a measurement point and a wireless access point, the power of the wireless access point, the signal frequency of the wireless access point, obstacles and other key factors on the signal propagation are comprehensively considered to dynamically adjust the segmented propagation model parameters. By considering the distance between the measurement point and the wireless access point in the process of correcting the segmented propagation model, segmented signal value measurement is performed. The segmented propagation model can support faster signal attenuation in a closer distance and slower signal attenuation in a farther distance. The calculation accuracy of the adjusted model is better than that of the original parameters. The model can be applied to an obstacle-free environment, an environment with a small number of obstacles and an environment with all obstacles, and has better environmental adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless signal propagation, and in particular to a wireless signal propagation model correction method and system supporting segmented calculation. Background Art

[0002] To ensure the best quality and reliability of wireless network services, it is necessary to accurately understand the indoor coverage of WiFi signals and the signal strength at different locations. In indoor environments, some factors that may affect wireless network signals will cause inaccurate RSS measurements. Factors such as the material and distribution of obstacles, the movement of people, and the mutual interference of access point devices will have a significant impact on the stability of indoor signals. Even if the indoor environment does not change, the signal at a specific location will fluctuate, attenuate, and become unstable, which poses a huge challenge to the measurement of wireless signals.

[0003] The signal segment propagation model is very important for calculating RSS. In the field of wireless communications, the measurement and calculation of RSS is usually used for AP deployment planning, estimating the distance between wireless devices, evaluating the quality of wireless channels, and performing services such as wireless positioning. However, due to the influence of various factors in the actual environment, including multipath effects, signal attenuation, and hardware limitations, the original signal propagation formula often cannot provide accurate RSS values. This affects the performance of the wireless network and the accuracy of the application. In order to solve this problem, it is very important to calibrate the RSS signal segment propagation model. The calibration process includes a series of corrections, including environmental adaptation, multipath effects, hardware deviation, and dynamic correction. In the above correction method, the parameter adjustment of the RSS segment propagation model according to the dynamic changes of the specific environment and environmental conditions is the main means of correction. This method can effectively improve the adaptability of the model and the accuracy of the measurement. The work of correcting the RSS signal propagation formula includes a series of processes such as field measurement, selection of a suitable signal segment propagation model, parameter estimation, model correction, and verification and optimization of related models. The calibration of the RSS calculation formula requires adjusting and optimizing the formula parameters according to the specific propagation environment and hardware conditions. The correct setting and calibration of parameters are crucial to improving ranging accuracy and system performance.

[0004] Although some progress has been made in the technology used to correct the signal propagation formula for RSS in complex indoor environments, there are still some limitations:

[0005] (1) Existing technologies often ignore various complex factors in indoor environments, such as distance, obstacles, antenna gain, multipath effects, transmission power, noise and interference, and weather conditions. This makes the signal propagation loss model based on experience and fixed parameters poorly adaptable to the environment, resulting in large deviations in the generated correction model in practical applications;

[0006] (2) Existing RSS correction models are often optimized for specific environments or devices, which limits their versatility and generalization capabilities, thereby limiting their applicability in different environments;

[0007] (3) Ensuring the robustness and stability of the RSS correction method in complex indoor environments with multiple users and multiple devices is a challenging task. Summary of the invention

[0008] The technical problem to be solved by the present invention is that the propagation loss of different indoor propagation distances is not fully considered during the signal measurement process, and the measurement result is inaccurate. The purpose is to provide a wireless signal propagation model correction method and system supporting segmented calculation. First, a segmented propagation model is constructed according to the signal measurement value and the signal loss value. Then, according to the actual measured signal value, the distance between the measurement point and the wireless access point, the power of the wireless access point, the signal frequency of the wireless access point and the obstacles on the signal propagation are comprehensively considered to dynamically adjust the segmented propagation model parameters. By considering the distance between the measurement point and the wireless access point in the process of correcting the segmented propagation model, the segmented signal value measurement is performed. The segmented propagation model can support faster signal attenuation in a closer distance and slower signal attenuation in a longer distance. The calculation accuracy of the adjusted model is better than the original parameters. It can be applied to an obstacle-free environment, an environment with a small number of obstacles and an environment with all obstacles, and has better environmental adaptability.

[0009] The present invention is achieved through the following technical solutions:

[0010] A first aspect of the present invention provides a wireless signal propagation model correction method supporting segmented calculation, comprising the following specific steps:

[0011] Obtain signal measurement values ​​and signal loss values ​​and construct a segmented propagation model;

[0012] Select multiple measurement points, and perform segmented measurement on the measurement points based on the distances between the measurement points and the wireless access points to obtain a segmented signal measurement value set;

[0013] Obtain a wall signal loss value between the measurement point and the wireless access point, and obtain a corrected distance coefficient according to the signal loss value and a set of signal measurement values;

[0014] The segmented propagation model is corrected according to the corrected distance coefficient.

[0015] Furthermore, the constructing of the segmented propagation model includes:

[0016] Obtain the coordinates of the measuring point and the wireless access point, and determine the distance between the measuring point and the wireless access point;

[0017] According to the distance between the measurement point and the wireless access point, the signal loss between different measurement points and the wireless access point is obtained;

[0018] According to the power and signal loss of the wireless access point, the measurement point signal is obtained and a segmented propagation model is constructed.

[0019] Furthermore, the steps of calculating the signal loss between the measurement point and the wireless access point include:

[0020] ;

[0021] in, loss x,y represents the signal loss value, F h and F l Indicates the different operating frequency bands of wireless access points. F h Indicates high frequency, F l represents low frequency, A represents the close distance coefficient, B represents the long distance coefficient, wall It represents the sum of the signal losses through all walls between the measurement point and the wireless access point. d represents the distance between the measurement point and the wireless access point, and T represents the threshold distance.

[0022] Furthermore, the selecting of multiple measurement points includes:

[0023] Select p The measurement points whose distance is less than or equal to T are regarded as the short-distance measurement point set;

[0024] Select q measurement points with a distance greater than T meters as the long-distance measurement point set;

[0025] Among them, T meters is the dividing line between short distance and long distance.

[0026] Further, obtaining a set of segmented signal measurement values ​​includes:

[0027] Perform multiple signal measurements on each point in the short-distance measurement point set and the long-distance measurement point set to obtain a short-distance signal measurement value set and a long-distance signal measurement value set;

[0028] Obtain a first average signal value of a short-distance signal measurement value set, and obtain a second average signal value of a long-distance signal measurement value set;

[0029] A short-range signal measurement set is obtained based on the first average signal value, and a long-range signal measurement set is obtained based on the second average signal value.

[0030] Furthermore, before obtaining the short-distance signal measurement set and the long-distance signal measurement set, the method further includes:

[0031] Obtaining a first average value and a first signal standard deviation of a short-range signal measurement value set;

[0032] Obtain a second average value and a second signal standard deviation of the long-distance signal measurement value set;

[0033] Determine whether there are abnormal values ​​in the short-distance signal measurement value set and the long-distance signal measurement value set;

[0034] If any, outliers are removed.

[0035] Further, the obtaining of the wall signal loss value between the measuring point and the wireless access point includes:

[0036] Acquire the coordinates of the measuring point and the coordinates of the wireless access point, and construct a first line segment according to the coordinates of the measuring point and the coordinates of the wireless access point;

[0037] Get the second line segment formed by the wall;

[0038] Determine whether the first line segment and the second line segment intersect;

[0039] If so, obtaining a signal loss value of the corresponding wall wireless access point transmitting the signal to the measurement point;

[0040] All second line segments intersecting with the first line segment are polled in sequence to obtain the wall signal loss value between the measuring point and the wireless access point.

[0041] Further, obtaining a corrected distance coefficient according to the signal loss value and the signal measurement value set includes:

[0042] Based on the wall signal loss value, the corrected short-distance coefficient is calculated in combination with the short-distance signal measurement set;

[0043] Based on the wall signal loss value, the corrected long-distance coefficient is calculated in combination with the long-distance signal measurement set.

[0044] Furthermore, the specific calculation steps of the corrected short-distance coefficient and the corrected long-distance coefficient include:

[0045] ;

[0046] ;

[0047] ;

[0048] in, loss i Indicates iThe wall signal loss value at each measurement point is: power Indicates the transmit power of the wireless access point. t i Indicates i The received power at the measurement point, A′ represents the corrected close-range coefficient, B′ represents the corrected long-range coefficient, A represents the close-range coefficient, p represents the number of measurement points used to calculate A′, and the distances between these measurement points d are all less than or equal to the threshold distance T, q′ represents the number of measurement points used to calculate B′, and the distances between these measurement points d are all greater than the threshold distance T, F h and F l Indicates the different operating frequency bands of wireless access points. F h Indicates high frequency, F l Indicates low frequency, d i Indicates i The distance from the measurement point to the wireless access point, wall i Indicates the i The wall signal loss value at each measurement point.

[0049] A second aspect of the present invention provides a wireless signal propagation model correction system supporting segmented calculation, the system being used to execute a wireless signal propagation model correction method supporting segmented calculation, comprising:

[0050] A data acquisition module, used to obtain a signal measurement value and a signal loss value, and to obtain a wall signal loss value between a measurement point and a wireless access point;

[0051] A model building module, used for building a segmented propagation model according to signal measurement values ​​and signal loss values;

[0052] The data processing module is used to select multiple measurement points, perform segmented measurement on the measurement points based on the distance between the measurement points and the wireless access points, and obtain a segmented signal measurement value set; and is also used to obtain a corrected distance coefficient based on the signal loss value and the signal measurement value set;

[0053] The model optimization module is used to calibrate the segmented propagation model according to the calibrated distance coefficient obtained by the data processing module.

[0054] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0055] According to the signal measurement value and signal loss value, a segmented propagation model is constructed. Then, according to the actual measured signal value, the distance between the measurement point and the wireless access point, the power of the wireless access point, the signal frequency of the wireless access point, and the influence of obstacles on signal propagation are comprehensively considered to dynamically adjust the segmented propagation model parameters. By considering the distance between the measurement point and the wireless access point in the process of correcting the segmented propagation model, segmented signal value measurement is performed. The segmented propagation model can support faster signal attenuation in a closer distance and slower signal attenuation in a longer distance. The calculation accuracy of the adjusted model is better than the original parameters, and it can be applied to obstacle-free environments, environments with a small number of obstacles, and environments with all obstacles, and has better environmental adaptability.

[0056] The corrected RSS signal propagation formula has good performance optimization significance for applications such as improving positioning accuracy, optimizing network resource allocation, and enhancing network quality reliability in wireless networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0058] Figure 1 A specific flow chart of a wireless signal propagation model correction method supporting segmented calculation in an embodiment of the present invention;

[0059] Figure 2 is a flow chart of the propagation model correction steps in an embodiment of the present invention;

[0060] Figure 3 is a system structure diagram in an embodiment of the present invention;

[0061] Figure 4 This is the experimental result of the non-interference scenario in the embodiment of the present invention;

[0062] Figure 5 These are experimental results of a scenario with a small amount of interference in an embodiment of the present invention;

[0063] Figure 6 This is the experimental result of a scene with a small number of obstacles in an embodiment of the present invention;

[0064] Figure 7 This is the experimental result of the full obstacle scene in the embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0066] The propagation of RSS signals in indoor environments is affected by many factors, including the distance between the transmitter and the receiver, the presence of obstacles, the gain of the antenna used, multipath effects, the transmission power level, the noise and interference levels, and the weather conditions at the time. Antenna gain and transmission power are inherent to the equipment used for signal transmission and reception. In indoor environments, weather conditions are not an important factor. The focus of the present invention is to design a wireless signal propagation model correction method and system that supports segmented RSS at different distances, correctly reflecting the comprehensive impact of distance, obstacles, noise and interference on the calibration of the signal propagation model, and providing a more accurate signal propagation formula to adapt to complex scenarios. The purposes of the present invention are as follows:

[0067] (1) A wireless signal propagation model correction method that supports segmented calculation is proposed. The propagation parameters can be adjusted according to the actual measurement data of different indoor scenes to support the correct calculation of indoor signals.

[0068] (2) The proposed signal correction method can be used for segmented correction of signal values. The signal attenuates faster at close distances, while the signal attenuates slower at long distances.

[0069] (3) The performance of the modified algorithm has been verified under various conditions. Experimental results show that the algorithm proposed in this invention can achieve the best performance.

[0070] Here, the present invention proposes the following specific embodiments to achieve the above content.

[0071] As a possible implementation, Figure 1As shown, this embodiment provides a wireless signal propagation model correction method supporting segmented calculation, obtaining signal measurement values ​​and signal loss values, and constructing a segmented propagation model; selecting multiple measurement points, and based on the distance between the measurement points and the wireless access points, performing segmented measurement on the measurement points to obtain a segmented signal measurement value set; obtaining the wall signal loss value between the measurement point and the wireless access point, and obtaining a corrected distance coefficient according to the signal loss value and the signal measurement value set; and correcting the segmented propagation model according to the corrected distance coefficient. First, a segmented propagation model is constructed according to the signal measurement value and the signal loss value. Then, according to the actual measured signal value, the distance between the measurement point and the wireless access point, the power of the wireless access point, the signal frequency of the wireless access point, and obstacles that affect the signal propagation are comprehensively considered to dynamically adjust the segmented propagation model parameters. By considering the distance between the measurement point and the wireless access point in the process of correcting the segmented propagation model, segmented signal value measurement is performed. The segmented propagation model can support faster signal attenuation in a closer distance and slower signal attenuation in a longer distance. The calculation accuracy of the adjusted model is better than the original parameters, and it can be applied to obstacle-free environments, environments with a few obstacles, and environments with all obstacles, and has better environmental adaptability.

[0072] like Figure 2 As shown, segmented signal propagation model, segmented signal measurement and propagation model correction are defined, and the specific implementation content includes:

[0073] S1: Define the segmented signal propagation model

[0074] S101: Define a wall set. Mark the signal loss values ​​of the walls in the map in straight lines. w For all walls w i gather, ( s x , s y , e x , e y ) represents the starting point coordinate and the end point coordinate of the wall, recorded as:

[0075] ;

[0076] ;

[0077] S102: Define measurement points and wireless access point locations. x,y ) represents the coordinates of the measuring point, ( a x , a y) represents the coordinates of the wireless access point AP. The distance between the measuring point and the wireless access point is determined based on the coordinates of the measuring point and the AP coordinates.

[0078] S103: Define the segment signal loss formula. According to the distance between the measurement point and the wireless access point, obtain the signal loss between different measurement points and the wireless access point; according to the power and signal loss of the wireless access point, obtain the measurement point signal. We set the working frequency band of the AP to 5.2GHz. d Indicates the distance between the measurement point and the AP. loss Indicates measurement ( x,y ) to the AP, wall is the sum of the signal losses through all walls between the measurement point and the AP. d When it is less than or equal to T meters, define the close-range signal loss formula and calculate according to the first formula in this step; when d When it is greater than T meters, we define the long-distance signal loss formula and calculate it according to the second formula in this step. A is the short-distance coefficient and B is the long-distance coefficient;

[0079] ;

[0080] S104: Calculate the signal value. power is the power of the AP, then when measuring ( x,y ) rss The record is:

[0081] ;

[0082] In S1, loss x,y represents the signal loss value, F h and F l Indicates the different operating frequency bands of wireless access points. F h Indicates high frequency, which can be 5.2GHz, F l Indicates low frequency, which can be 2.4GHz, A indicates the close distance coefficient, and B indicates the long distance coefficient. wall It represents the sum of the signal losses through all walls between the measurement point and the wireless access point. d represents the distance between the measurement point and the wireless access point, T represents the threshold distance, power Indicates the transmit power of the wireless access point.

[0083] S2: Segmented signal measurement. Perform multiple signal measurements on each point in the short-distance measurement point set and the long-distance measurement point set to obtain a short-distance signal measurement value set and a long-distance signal measurement value set; obtain a first average signal value of the short-distance signal measurement value set, and obtain a second average signal value of the long-distance signal measurement value set; obtain the short-distance signal measurement set based on the first average signal value, and obtain the long-distance signal measurement set based on the second average signal value;

[0084] S201: Select measurement points. When selecting multiple measurement points, select m points from the grid points for measurement, and measure each point n times. In order to make the measured RSS more accurate, some outliers need to be moved. The measured RSS average value is taken to calculate the parameters A and B in the signal propagation formula. The present invention has good performance in an obstacle-free environment, a small number of obstacle environments, and a full obstacle environment, including: randomly selecting p The set of measurements with distances less than or equal to T meters loc lt (a collection of close-range measurement points), q The signal distance is greater than T meters. loc gt (a set of long-distance measurement points), denoted as:

[0085] ;

[0086] ;

[0087] S202: Segment signal measurement. loc lt and loc gt Each point in the signal is measured n times, r lt is the short-distance signal measurement value, r gt is the measured value of the long distance signal, Indicates j The measurement point i The signal measurement value is recorded as:

[0088] ;

[0089] ;

[0090] S203: Calculate standard deviation. Since there are too many factors that affect the propagation of wireless network signals, the measurement results of signals at the same location will not be the same each time and will be unstable. Therefore, the RSS value of a single measurement cannot reflect the actual signal strength. The traditional method is usually to perform multiple measurements at the same location and obtain the average result. However, if there are outliers, it may cause a certain error in the calculation result. Therefore, we remove the outliers according to three standard deviations and take the average of the RSS values. The average value of each measurement point Sum signal standard deviation s j The calculation formula is as follows:

[0091] ;

[0092] ;

[0093] S204: Remove outliers. If , then Remove from list;

[0094] S205: Get the average signal value. After removing the outliers, each measurement point loc j There will be n′ valid signal values, and the short-distance signal measurement set is T lt and long distance signal measurement collection T gt for:

[0095] ;

[0096] ;

[0097] ;

[0098] in, t i Indicates i The received power at each measurement point is Indicates j The measurement point i Signal measurement value, n means n signal measurement, n′ means n′ signal measurement, d i Indicates i The distance between the measurement point and the wireless access point, T represents the threshold distance.

[0099] S3: Propagation model correction. From S103, it can be seen that within a distance of T meters, if a distance value and an A value are determined, a loss value can be obtained; conversely, if a distance value and a corresponding loss value are determined, an A value can be obtained. Similarly, outside a distance of T meters, if A is fixed, a distance value and a B value can be obtained to obtain a loss value; conversely, if a distance value and a corresponding loss value are known, a B value can be obtained. A and B have original default values, and the method in this paper obtains the corrected A′ and B′ based on the actual signal measurement values;

[0100] S301: Calculate the wall loss between the measurement point and the AP. Set the starting point as the measurement point ( x,y ), the end point is the AP location ( a x , a y ) and the line segment starting at ( s x , s y ), the end point is ( e x , e y ) to determine whether they intersect. If they do, add the corresponding wall signal loss value. After polling all walls in turn, get the i The wall signal loss value between the measurement point and the AP wall i；

[0101] S302: Calculate the value of A'. We can T lt Calculate the short-range corrected coefficient A′ as follows:

[0102] ;

[0103] ;

[0104] S303: Calculate the B' value. We can T gt Calculate the long-distance corrected coefficient B′ as follows:

[0105] ;

[0106] ;

[0107] S304: Finally, the corrected signal propagation model loss calculation formula is obtained:

[0108] ;

[0109] S305: Calculate the corrected signal using the formulas in steps S104 and S304;

[0110] S3, where loss i Indicates i The wall signal loss value at each measurement point is: power Indicates the transmit power of the wireless access point. t i Indicates i The received power at the measurement point, A′ represents the corrected close-range coefficient, B′ represents the corrected long-range coefficient, A represents the close-range coefficient, p represents the number of measurement points used to calculate A′, and the distances between these measurement points d are all less than or equal to the threshold distance T, q′ represents the number of measurement points used to calculate B′, and the distances between these measurement points d are all greater than the threshold distance T, F h and F l Indicates the different operating frequency bands of wireless access points. F h Indicates high frequency, F l Indicates low frequency, d i Indicates i The distance from the measurement point to the wireless access point, wall i Indicates the i The wall signal loss value at each measurement point, loss Indicates the wall signal loss value at the measurement point, wall Indicates the wall signal loss value.

[0111] As a possible implementation, Figure 3 As shown, this embodiment provides a wireless signal propagation model correction system supporting segmented calculation, including:

[0112] A data acquisition module, used to obtain a signal measurement value and a signal loss value, and to obtain a wall signal loss value between a measurement point and a wireless access point;

[0113] A model building module, used for building a segmented propagation model according to signal measurement values ​​and signal loss values;

[0114] The data processing module is used to select multiple measurement points, perform segmented measurement on the measurement points based on the distance between the measurement points and the wireless access points, and obtain a segmented signal measurement value set; and is also used to obtain a corrected distance coefficient based on the signal loss value and the signal measurement value set;

[0115] The model optimization module is used to calibrate the segmented propagation model according to the calibrated distance coefficient obtained by the data processing module.

[0116] As a possible implementation, this embodiment provides a test comparison example. In this embodiment, 5 to 7 points are selected in an indoor environment to measure the RSS value of the AP signal received by the mobile phone. At each measurement point, one RSS value is taken every 1 second, and the measurement is continued for 1 minute to obtain 60 RSS values. The outliers in these 60 RSS values ​​are removed by removing outliers by 3 standard deviations, and the average value is taken as the RSS value of the point.

[0117] In this embodiment, the RSS measurement values ​​of 5 to 7 points are plotted as a "Measured data" group curve. According to the distance of 5 to 7 points from the AP, the power of the AP, and the attenuation value of obstacles, the original calculated RSS values ​​of 5 to 7 points are obtained according to the formula and plotted as an "Original model" curve. According to the propagation model correction method mentioned in this article, the "Corrected model" curve is obtained.

[0118] This embodiment performs a total of 4 groups of experiments for four different indoor environment scenarios, namely, a scenario with no obstacles between the test point and the AP, a scenario with a small amount of interference, a scenario with a small amount of obstacles, and a scenario where all measurement points pass through obstacles.

[0119] Experiments with non-interference scenarios: Figure 4 As shown, Figure 4 The experiment process is described in an indoor environment with no obstacles between the measurement point and the AP. To verify the RSS of the same measurement point at different measurement times, we performed two sets of tests at exactly the same 7 measurement points.

[0120] Figure 4 The line graphs marked as (a) and (b) are the measured RSS data of 7 points measured simultaneously by mobile phone 1 and mobile phone 2 for the first time (time1), the curves of RSS calculated by the original model and RSS calculated by the correction model. The curves reflect the trend of RSS within 10 meters from the AP. The experimental results show that, firstly, the RSS measured by different hardware devices at the same point is different, and secondly, the RSS curve calculated by the correction model we proposed has a better fit with the measured RSS curve. This reflects that the use of the correction model to calculate RSS has a better fit to the RSS propagation environment of the indoor environment corresponding to the experiment.

[0121] The results of the second measurement (time2) using mobile phone 1 and mobile phone 2 are shown in Figure 4 Compared with the line graphs marked as (a) and (b), the line graphs marked as (c) and (d) in the figure show that the RSS measured by the same device at the same point at different times is different. In addition, it can be seen that the correction model we proposed can dynamically fit the curve of the RSS measured in the actual indoor environment.

[0122] Experiments with a small amount of interference scenes: Figure 5 As shown, Figure 5 The indoor environment scenario with a small amount of interference between the measurement point and the AP is explained. In this set of experimental scenarios, there are interferences such as electronic devices and people walking around, which may affect the RSS measurement value. However, the experimental results still show that the correction model we proposed can better fit the curve of the measured RSS.

[0123] Experiments on scenes with a small number of obstacles: Figure 6 As shown, Figure 6 This paper illustrates an indoor scenario where there are a few obstacles (walls) between the measurement point and the AP. Among the seven measurement points, the 5th, 6th, and 7th points pass through the wall, and the attenuation value of the wall is 8dB. The correction model we proposed takes the influence of the wall into consideration and still fits the measured RSS curve well.

[0124] Experiments with full obstacle scenarios: Figure 7 As shown, Figure 7 This paper explains the indoor scenario where there are obstacles (walls) between all measurement points and the AP. Figure 7 The distances from the measurement points to the AP in the line graphs marked as (a) and (b) are both within 10 meters, while the distances from the measurement points to the AP in the line graphs marked as (c) and (d) are mostly over 10 meters. It can be seen that the RSS measurement curves within 10 meters from the AP and the RSS measurement curves over 10 meters have different trends. The distance-based segmented RSS correction model we proposed can fit the measured RSS curve well.

[0125] Based on the above experimental results, the comparison is shown in Table 1, which introduces the performance of the technical model. To avoid repeated description, we choose the third measurement point to evaluate the performance. NI represents the experiment without interference. LI represents the experiment with less interference. LO represents the experiment with a small number of obstacles. α and β represent the correction parameters.

[0126] Table 1 Performance analysis of the third measurement point

[0127]

[0128] We observed the following from the results:

[0129] 1) The RSS calculated by the modified model is closer to the actual measured value than the original calculation result;

[0130] 2) The changes of α and β under different conditions can determine the accuracy of the proposed method;

[0131] 3) Our correction model can support segmented correction and better adapt to distance changes;

[0132] 4) Different mobile phones were used in the experiment and the results showed that the revised model is applicable to different mobile terminals.

[0133] This embodiment proves that the wireless signal propagation model correction method and system supporting segmented calculation provided by the present invention solves the problem of signal calculation accuracy for adapting to unstable RSS. The corrected model can adjust the model parameters according to the actual measurement data, so the accuracy is better than the original parameters. On the other hand, the segmented calculation model can support the situation where the close-range signal attenuates faster and the long-range signal attenuates slower.

[0134] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A wireless signal propagation model correction method supporting segmented calculation, characterized in that: The specific steps include: Acquire signal measurement values ​​and signal loss values, and construct a segmented propagation model. The segmented propagation model construction includes: acquiring measurement point coordinates and wireless access point coordinates, determining the distance between the measurement point and the wireless access point; acquiring signal losses between different measurement points and the wireless access point according to the distance between the measurement point and the wireless access point; obtaining measurement point signals according to the power and signal loss of the wireless access point, and constructing a segmented propagation model. Specifically: S101: Define a wall set and mark the signal loss values ​​of the walls in the map in straight lines. w For all walls w i gather,( s x , s y , e x , e y ) represents the starting point coordinate and the end point coordinate of the wall, recorded as: ； ； S102: Define measurement points and wireless access point locations, where ( x,y ) represents the coordinates of the measuring point, ( a x , a y ) represents the coordinates of the wireless access point, and the distance between the measuring point and the wireless access point is determined according to the coordinates of the measuring point and the coordinates of the wireless access point; S103: defining a segmented signal loss formula, obtaining the signal loss between different measurement points and the wireless access point according to the distance between the measurement point and the wireless access point; obtaining the measurement point signal according to the power and signal loss of the wireless access point, including: ； S104: Calculate the signal value, where: power Represents the transmit power of the wireless access point. x,y ) rss Denoted as: ； in, loss x,y represents the signal loss value, F h and F l Indicates the different operating frequency bands of wireless access points. F h Indicates high frequency, which can be 5.2GHz, F l Indicates low frequency, which can be 2.4GHz, A indicates the close distance coefficient, and B indicates the long distance coefficient. wall It represents the sum of the signal losses through all walls between the measurement point and the wireless access point. d represents the distance between the measurement point and the wireless access point, and T represents the threshold distance; Select multiple measurement points, and perform segmented measurement on the measurement points based on the distances between the measurement points and the wireless access points to obtain a segmented signal measurement value set; Obtain a wall signal loss value between the measurement point and the wireless access point, and obtain a corrected distance coefficient according to the signal loss value and a set of signal measurement values; The segmented propagation model is corrected according to the corrected distance coefficient.

2. The wireless signal propagation model correction method supporting segmented calculation according to claim 1, characterized in that: The selecting of multiple measuring points comprises: Select p The measurement points whose distance is less than or equal to T are regarded as the short-distance measurement point set; Select q measurement points whose distance is greater than T as the long-distance measurement point set; Among them, T is the dividing value between short distance and long distance.

3. The wireless signal propagation model correction method supporting segmented calculation according to claim 2, characterized in that: The obtaining of the segmented signal measurement value set comprises: Perform multiple signal measurements on each point in the short-distance measurement point set and the long-distance measurement point set to obtain a short-distance signal measurement value set and a long-distance signal measurement value set; Obtain a first average signal value of a short-distance signal measurement value set, and obtain a second average signal value of a long-distance signal measurement value set; A short-range signal measurement set is obtained based on the first average signal value, and a long-range signal measurement set is obtained based on the second average signal value.

4. The wireless signal propagation model correction method supporting segmented calculation according to claim 3, characterized in that: Before obtaining the short-distance signal measurement set and the long-distance signal measurement set, the method further includes: Obtaining a first average value and a first signal standard deviation of a short-range signal measurement value set; Obtain a second average value and a second signal standard deviation of the long-distance signal measurement value set; Determine whether there are abnormal values ​​in the short-distance signal measurement value set and the long-distance signal measurement value set; If any, outliers are removed.

5. The wireless signal propagation model correction method supporting segmented calculation according to claim 1, characterized in that: The obtaining of the wall signal loss value between the measuring point and the wireless access point includes: Acquire the coordinates of the measuring point and the coordinates of the wireless access point, and construct a first line segment according to the coordinates of the measuring point and the coordinates of the wireless access point; Get the second line segment formed by the wall; Determine whether the first line segment and the second line segment intersect; If so, obtaining a signal loss value of the corresponding wall wireless access point transmitting the signal to the measurement point; All second line segments intersecting with the first line segment are polled in sequence to obtain the wall signal loss value between the measuring point and the wireless access point.

6. The wireless signal propagation model correction method supporting segmented calculation according to claim 1, characterized in that: The step of obtaining a corrected distance coefficient according to the signal loss value and the signal measurement value set includes: Based on the wall signal loss value, the corrected short-distance coefficient is calculated in combination with the short-distance signal measurement set; Based on the wall signal loss value, the corrected long-distance coefficient is calculated in combination with the long-distance signal measurement set.

7. The wireless signal propagation model correction method supporting segmented calculation according to claim 6, characterized in that: The specific calculation steps of the corrected short-distance coefficient and the corrected long-distance coefficient include: ； ； ； in, loss i Indicates i The wall signal loss value at each measurement point is: power Indicates the transmit power of the wireless access point. t i Indicates i The received power at the measurement point, A′ represents the corrected close-range coefficient, B′ represents the corrected long-range coefficient, A represents the close-range coefficient, p represents the number of measurement points used to calculate A′, and the distances between these measurement points d are all less than or equal to the threshold distance T, q′ represents the number of measurement points used to calculate B′, and the distances between these measurement points d are all greater than the threshold distance T, F h and F l Indicates the different operating frequency bands of wireless access points. F h Indicates high frequency, F l Indicates low frequency, d i Indicates i The distance from the measurement point to the wireless access point, wall i Indicates the i The wall signal loss value at each measurement point.

8. A wireless signal propagation model correction system supporting segmented calculation, applied to the wireless signal propagation model correction method supporting segmented calculation as claimed in any one of claims 1 to 7, characterized in that: include: A data acquisition module, used to obtain a signal measurement value and a signal loss value, and to obtain a wall signal loss value between a measurement point and a wireless access point; A model building module, used for building a segmented propagation model according to signal measurement values ​​and signal loss values; The data processing module is used to select multiple measurement points, perform segmented measurement on the measurement points based on the distance between the measurement points and the wireless access points, and obtain a segmented signal measurement value set; and is also used to obtain a corrected distance coefficient based on the signal loss value and the signal measurement value set; The model optimization module is used to calibrate the segmented propagation model according to the calibrated distance coefficient obtained by the data processing module.

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