Method for comparing, screening and positioning candidate points by using monitoring data

Abstract

The invention discloses a method for comparing, screening and positioning candidate points by using monitoring data, and relates to the field of radio orientation, and the method comprises the following steps: S1, obtaining a candidate position set; s2, performing spatial expansion on the candidate position set to generate an expanded candidate position set; s3, processing the received signal strength data to obtain combined monitoring data; s4, calculating simulation signal intensity; s5, comparing the simulation data with the monitoring data, calculating a standard deviation of a root mean square error and a difference data value, and calculating an error evaluation index in a combined manner; and S6, selecting the candidate position point with the minimum error evaluation index as a final positioning result. On the basis of a preliminary positioning result, a systematic and high-resolution refined evaluation and screening mechanism is introduced, and a comparison mechanism is based on a physical propagation model, so that the electromagnetic relationship between a signal source and a receiving point can be reflected more truly, and the positioning result is closer to the real position of the signal source.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radio orientation technology, and in particular to a method for screening and positioning candidate points using monitoring data. BACKGROUND

[0002] Under the background of the rapid development of modern wireless communication and Internet of Things technology, the demand for high-precision positioning of signal sources in indoor space is increasingly urgent, and its application scenarios widely cover intelligent buildings, industrial automation, personnel and asset tracking, indoor navigation, and security monitoring. Unlike open outdoor environments, the propagation characteristics of radio waves in indoor environments are extremely complex. Signals will encounter reflections, refractions, diffractions, and absorptions of obstacles such as walls, furniture, doors, and windows during transmission, resulting in significant multipath effects, shadow fading, and signal fast fading, causing received signal strength (RSSI) and other parameters to exhibit dramatic and nonlinear changes in space. The complex propagation environment makes it difficult for traditional positioning methods based on received signal characteristics, such as trilateration or fingerprint positioning, to meet the requirements of many high-value applications in terms of positioning accuracy.

[0003] To address this challenge, positioning technology based on electromagnetic situation awareness has emerged. This technology processes and models the signal data collected by discrete deployed receiving devices to generate a situation map that reflects the overall two-dimensional spatial region electromagnetic signal strength distribution overview, and then infers the most likely location of the signal source from the situation map, thereby transforming local, discrete measurement information into a macroscopic grasp of the global electromagnetic environment.

[0004] A transform domain electromagnetic situation awareness and radiation source positioning method is disclosed in Chinese patent CN111929643A. The core of this method is to process the original received signal data through transform domain processing, mapping it from the time or spatial domain to the transform domain. In the transform domain, the characteristics of the signal are more prominent, while noise and interference are suppressed, allowing more effective extraction of feature information related to the signal source location and generation of a clearer and more accurate electromagnetic situation distribution map. Once a high-quality situation map is generated, existing positioning algorithms can directly extract the signal source positioning point from the map by finding signal strength peaks, calculating centroids, or performing pattern matching. This method fully utilizes the global view provided by the situation map and can achieve rapid preliminary positioning of the signal source, improving the performance of positioning using raw data in complex environments to some extent.

[0005] However, the prior art solution generates a continuous spatial electromagnetic situation map from limited and discrete actual measurement point data, which is essentially a mathematical speculation process. Regardless of the advanced transform domain processing technology adopted, it is impossible to completely eliminate the errors introduced by sparse measurement points, inaccurate environmental model and uncertainty of unsampled areas. Therefore, the generated situation map can only be an approximate reflection of the real electromagnetic environment, which inevitably contains ambiguity and inaccuracy caused by speculation. More critically, the prior art directly extracts the signal source position based on the situation map with speculation, lacking a mechanism for backtracking comparison with the original high-resolution measured data and local fine verification. Furthermore, this one-step positioning method makes the final result very sensitive to the cumulative errors in the situation generation process. In a complex indoor multipath environment, the real signal strength distribution may present multiple similar peaks, or the real source position may not be located at the most obvious peak top of the situation map, and direct extraction is prone to slight deviation. SUMMARY

[0006] The present application overcomes the shortcomings of the prior art and provides a method for screening positioning candidate points using monitoring data.

[0007] To achieve the above object, the present application adopts the technical scheme of a method for screening positioning candidate points using monitoring data, comprising the following steps: S1, obtaining an initial signal source candidate position set; S2, performing spatial expansion on each initial candidate position point in the initial signal source candidate position set to generate an expanded candidate position set containing the initial point and multiple potential position points in the vicinity of the initial point; S3, performing spatial filtering and smoothing processing on the originally collected received signal strength data to obtain merged monitoring data; S4, for each candidate position point in the expanded candidate position set, assuming that the signal transmitting source is located at the point, calculating the simulated signal strength at each receiving device location according to the signal transmitting source operating parameters and receiving device location information through an electromagnetic wave propagation model; S5, comparing the simulated signal strength corresponding to each candidate position point with the merged monitoring data, calculating the root mean square error and the standard deviation of the difference data value, and combining to calculate the error evaluation index; S6, selecting the candidate position point with the minimum error evaluation index as the final positioning result of the signal transmitting source.

[0008] In a preferred embodiment of the present application, in the step of S1, the set is obtained by constructing electromagnetic situation information from the received signal data collected in the space and preliminarily positioning one or more potential position coordinate points of the signal transmitting source based on the electromagnetic situation information. In a preferred embodiment of the present application, in the step S2, the spatial expansion manner includes offsetting in the preset expansion distance in the eight directions of up, down, left, right, left up, left down, right up, and right down of the initial candidate position point, to generate nine candidate position points including the initial point.

[0009] In a preferred embodiment of the present application, the expansion distance is configured as 0.2m or 0.5m according to the preliminary positioning error range and the environmental complexity.

[0010] In a preferred embodiment of the present application, in the step S3, the spatial filtering and smoothing processing includes: identifying all other measurement data points with a spatial geometric distance within a preset threshold distance for each received signal strength at a receiving device position; performing an arithmetic average operation on the received signal strength values of the data points, and taking the average value as the updated received signal strength value of the position point.

[0011] In a preferred embodiment of the present application, in the step S4, the electromagnetic wave propagation model is a modified free space propagation model, and the path loss calculation formula is: ; wherein, f is the frequency of the received signal; d is the distance between the transceiver devices; a is a distance impact weight coefficient, and the value is 1.5-4.

[0012] In a preferred embodiment of the present application, in the step S5, the calculation formula of the error evaluation index is: EE= 0.95* RMSE +0.05* ESD ; wherein, RMSE is the root mean square error, ESD is the standard deviation of the difference data value.

[0013] In a preferred embodiment of the present application, the calculation formula of the root mean square error is: ; wherein, n is the number of measurement data; is the difference value between the simulated path loss and the measured path loss, , PL i-p is the simulated path loss; PL i-m is the measured path loss value.

[0014] In a preferred embodiment of the present application, the calculation formula of the standard deviation of the difference data value is: ; wherein, is the average value.

[0015] In a preferred embodiment of the present application, in the step of S6, the final positioning result is output in the form of two-dimensional coordinates.

[0016] The present application solves the defects in the background art and has the following beneficial effects: (1) The present application provides a method for screening and positioning candidate points using monitoring data comparison. By introducing a systematic and high-resolution fine evaluation and screening mechanism based on the preliminary positioning result, the inherent limitations of the prior art of relying solely on the electromagnetic situation data generated by speculation for final positioning are effectively overcome. Instead of directly extracting positioning points from the situation map with uncertainty, the present application performs direct, quantitative and fine comparison analysis between the simulation propagation characteristics of the preliminary candidate position points and the actual collected monitoring data after smoothing. The comparison mechanism is based on a physical propagation model and can more realistically reflect the electromagnetic relationship between the signal source and the receiving point, thereby making the positioning result closer to the true position of the signal source.

[0017] (2) In the present application, the initial candidate position points are expanded in space, and independent simulation and comparison are performed on each candidate position point after expansion, which enables the positioning result to be fine-tuned at a microscopic scale. The method is particularly suitable for solving the problem that the existing situation awareness method cannot effectively distinguish and select when multiple potential signal source regions are generated due to multipath effects or other interference in a complex indoor environment. By combining the evaluation of the root mean square error and the standard deviation of the difference data value, the present application can effectively distinguish the candidate point that best matches the actual signal propagation trend, thereby accurately selecting the most likely true position of the signal source from multiple preliminary positioning candidate points. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative labor; Figure 1 is a flowchart of a method for screening and positioning candidate points using monitoring data according to a preferred embodiment of the present application; Figure 2 is an actual position map of a signal source according to Embodiment 1 of the present application; Figure 3 This is a diagram showing the location result of the signal source in Embodiment 1 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0021] It should be noted that the method of this invention is deployed in an indoor positioning system, which includes multiple distributed signal receiving devices. These receiving devices can be wireless LAN access points, Bluetooth Low Energy gateways, or ultra-wideband receiver modules; the signal transmitting source can be any device that emits radio frequency signals, such as a Wi-Fi module, a BLE beacon, or a UWB tag. This embodiment assumes that the signal receiving devices have undergone precise spatial calibration beforehand, and their geographic coordinate information has been accurately entered into the system.

[0022] like Figure 1 As shown, a method for screening candidate locations using monitoring data comparison includes the following steps: S1. Obtain the initial set of candidate signal source locations; the set is obtained by constructing electromagnetic situation information from the received signal data collected in space and performing preliminary positioning based on the electromagnetic situation information to obtain the potential location coordinates of one or more signal sources. S2. Spatial expansion is performed on each initial candidate location point in the initial candidate location set of the signal source to generate an expanded candidate location set containing the initial point and multiple potential location points in its neighboring region. S3. Spatial filtering and smoothing are performed on the original acquired received signal strength data to obtain the merged monitoring data; S4. For each candidate location point in the expanded candidate location set, assuming that the signal source is located at that point, calculate the simulated signal strength at each receiving device location using the electromagnetic wave propagation model based on the signal source operating parameters and the receiving device location information. S5. Compare the simulated signal strength corresponding to each candidate location point with the merged monitoring data, calculate the root mean square error and the standard deviation of the difference data values, and combine them to calculate the error evaluation index. S6, selecting the candidate position point with the minimum error evaluation index as the final positioning result of the signal emission source.

[0023] In some specific embodiments, in the step of S1, the purpose is to preliminarily identify the potential location area of the signal emission source by macroscopically analyzing the received signal data collected by the signal receiving devices widely deployed in the target space.

[0024] Specifically, the system continuously monitors the real-time received signal strength indication (RSSI) data reported by each signal receiving device, and these original RSSI data carry preliminary information about signal propagation path loss, environmental attenuation, and potential multipath effects.

[0025] In this embodiment, electromagnetic situation information refers to the signal strength distribution map or model detected by all receiving devices in the target area, and its construction process can adopt various existing technical solutions, such as a method based on a signal strength fingerprint map, which measures the signal strength at different positions in the environment in advance and establishes a database, and matches the real-time measured RSSI with the database during positioning; or a method based on a propagation model, such as a trilateration method or a multilateration method, which inversely calculates the distance of the signal source by combining the known transmission power and reception power with the electromagnetic wave propagation model, and then determines the position of the signal source.

[0026] Further, in order to optimize the efficiency and quality of the situation map generation, this embodiment can incorporate transform domain processing, such as performing fast Fourier transform or wavelet transform on the original time domain received signal data to convert the signal into frequency domain or time-frequency domain representation. In the transform domain, background noise, harmonic interference can be more effectively identified and filtered out, or specific frequency domain features related to the position of the signal source can be extracted.

[0027] In actual operation, if the method based on the RSSI fingerprint map is adopted, the real-time RSSI is matched with the pre-constructed fingerprint library, such as using a K-nearest neighbor algorithm or a Gaussian process regression machine learning algorithm, to predict the preliminary position of the signal source. If the method based on the propagation model is adopted, a set of nonlinear equations is solved to calculate the preliminary coordinates of the signal source according to the distance relationship between each receiving device and the signal source.

[0028] It should be noted that regardless of the method, the output of this step is one or more potential signal emission source position coordinate points, which constitute an initial signal source candidate position set. The initial candidate points reflect the rough estimation of the signal source position by the preliminary positioning algorithm, and its accuracy is limited by the quality of the original data, model errors, and environmental complexity.

[0029] For example, in a 20m×20m indoor environment, the initial positioning algorithm may infer the location of one or two signal sources with high probability based on RSSI data fed back by eight evenly distributed receivers, with an estimation accuracy between 1m and 3m.

[0030] In some specific implementations, step S2 aims to perform a localized fine-grained search on the initial localization results obtained in S1 to compensate for any minor offsets that may exist in the initial localization and to provide denser candidate sample points for subsequent high-resolution comparisons; for each initial candidate location point in the initial set of candidate signal source locations ( x 0 , y 0 In this embodiment, the point is taken as the center and a systematic offset is made in its two-dimensional space to generate an expanded candidate location set containing the initial candidate location point and multiple potential location points in its neighboring area.

[0031] Specifically, the offset operation covers eight directions: up, down, left, right, upper left, lower left, upper right, and lower right. This means that for each initial candidate point, eight new candidate position points will be generated. Including the initial candidate point itself, a total of nine candidate position points are obtained.

[0032] The displacement of the offset operation is controlled by a pre-configured extension distance parameter, Dex. This extension distance is a key system parameter, and its configuration value needs to be set comprehensively based on the specific positioning accuracy requirements, the complexity of the signal propagation environment, and the expected error range of the preliminary positioning algorithm in the first step.

[0033] For example, in scenarios requiring high positioning accuracy and with small initial positioning errors, the extended distance Dex is configured to 0.2m; this means that around the initial candidate point, the new 8 points will be located at ( x 0 ±0.2, y 0 ),( x 0 , y 0 ±0.2); its smaller spread distance helps to achieve a denser distribution of candidate points, thus enabling a finer search within local areas to capture the true location offset caused by small model inaccuracies or data discreteness.

[0034] On the other hand, in scenarios where the initial localization results fluctuate significantly or the environment changes rapidly, to ensure coverage of the potential locations of the actual signal source, the extended distance Dex is configured to 0.5m; in this case, the expanded candidate points will form a larger search area, namely ( x0 ±0.5, y 0 ),( x 0 , y 0 ±0.5)。

[0035] It should be noted that the larger expansion distance increases the complexity of subsequent simulation calculation, but can provide a wider distribution of candidate points and increase the possibility of finding a global optimal solution; all generated 9 candidate position points will be added to the final expanded candidate position set as an independent subset, and if the initial signal source candidate position set contains multiple points, the above expansion operation will be performed independently for each initial candidate point. For example, if the initial set contains two candidate points PAP A and PBP B , this step will generate 9 points centered on PAP A and 9 points centered on PBP B , a total of 18 candidate position points, and form an expanded candidate position set. By creating a dense candidate grid in the neighborhood of the preliminary positioning result, sufficient resolution is provided for subsequent refinement comparison and verification to improve the accuracy of the final positioning.

[0036] In some specific embodiments, in the step of S3, the purpose is to perform spatial filtering and smoothing processing on the original collected received signal strength measurement data to eliminate or significantly weaken the influence of signal collection instantaneous fluctuations, local interference or sensor self-noise on the measurement data; the unprocessed original RSSI data often presents high volatility, which will interfere with the subsequent accurate comparison based on the propagation model; and then through spatial merging processing, each data point will better represent the average electromagnetic environment intensity of its local area, making the overall data trend more gentle and stable.

[0037] Specifically, for each signal receiving device, the received signal strength measurement data collected at the respective preset position x j , y j in a specific time window P rx,meas,j , this step systematically traverses all other collected data points, and for each measurement data point to be processed, for example P rx,meas,k and its corresponding position x k , y k), first identify all other measurement data points whose spatial geometric distance is within a pre-set threshold distance D th The geometric distance is the Euclidean distance.

[0038] Exemplarily, if the receiving device A is located at ( x A , y A ) and receives P rx,meas,A , the distance between the location of all other receiving devices B , C ,… and ( x B , y B ), ( x C , y C ), … and ( x A , y A ) will be calculated; if , then P rx,meas,B will be included in the smoothing processing set of P rx,meas,A .

[0039] After identifying all data points meeting the distance condition, an arithmetic average operation is performed on the received signal strength values of the identified data points, and the result P rx,meas,k will replace the corresponding received signal strength value of the original measurement data point P rx,meas,k as the updated received signal strength measurement data of this location point; after the merging processing, the number of original collected data points, i.e. the number of signal receiving devices, remains unchanged, however, the received signal strength value carried by each data point is no longer a single instantaneous measurement value, but represents the average electromagnetic environment intensity in its local area.

[0040] The threshold distance D th is a configurable system parameter, and its configuration value needs to be adjusted according to the complexity of the signal propagation environment, the fluctuation characteristics of the data and the desired smoothing granularity; for example, in an indoor environment, the signal is easily affected by multipath effect, shielding and absorption, resulting in rapid spatial changes, in order to effectively suppress these local fluctuations, the threshold distance D thThe threshold distance can be set to 2m, meaning that within a 2m radius, the RSSI values of all measurement points will be averaged, thus generating a smoother electromagnetic environment map; if the threshold distance is set too small, the smoothing effect will not be obvious; if it is set too large, it may be over-smoothed, losing the local details of signal strength changes. In actual deployment, the values can be determined by empirical testing or simulation analysis to determine the most suitable values for specific scenarios. D th The values can be determined by empirical testing or simulation analysis to determine the most suitable values for specific scenarios.

[0041] In some specific embodiments, in the step S4, the purpose is to generate a corresponding theoretical electromagnetic propagation characteristic model for each candidate position point in the expanded candidate position set. This process is achieved by assuming that the signal transmission source is located at a certain expanded candidate position point, and using the known electromagnetic wave propagation model and system parameters to calculate the theoretical received signal strength that the hypothetical signal source can produce at all signal receiving device locations. This set of theoretical values will constitute an electromagnetic fingerprint for the candidate point, which will be used for subsequent comparison with actual monitoring data.

[0042] Specifically, for each candidate position point in the expanded candidate position set, x s , y s , it is regarded as a hypothetical signal transmission source. Subsequently, according to the assumed signal transmission source operating parameter characteristics and the location information of each signal receiving device, the electromagnetic wave propagation model is used to calculate the received signal strength that the hypothetical signal transmission source can produce at each signal receiving device location x j , y j ; wherein the signal receiving device location information includes the geographic coordinates P rx,sim,j of each signal receiving device x j , y j and the gain of the receiving antenna G r .

[0043] Further, the electromagnetic wave propagation model uses a modified free space propagation model suitable for indoor space for simulation calculation; the path loss calculation formula of this model is as follows: ; wherein, f is the frequency of the received signal; d is the distance between the transmitting and receiving devices; a is the distance impact weight coefficient, taking a value of 1.5-4.

[0044] Further, the calculation formula of the received power is as follows: ; wherein, P r is the power of the received signal; P t is the power of the transmitted signal; G t is the gain of the transmitting antenna; L tt is the total other loss of the transmitting device; PL is the loss on the propagation path; G r is the gain of the receiving antenna; L rt is the total other loss of the receiving device; It can be understood that the simulation calculation needs to use the information of the signal source transmission power, the antenna directivity gain and the signal frequency in the working parameters; the frequency information of the signal is known, and the transmitting antenna and the receiving antenna can be considered as omnidirectional antennas in the whole processing process, i.e., the difference of the antenna directivity gain does not need to be considered; and the transmission power of the signal source is unknown, which is a problem to be solved in the simulation process.

[0045] Since the comparison between the simulation data and the measured data shows that if there is greater consistency in the trend, it can be considered that the signal source is more likely to be in the candidate position; since using a fixed transmission power does not affect the data trend of the simulation result, in the simulation process, a larger weight is configured to the matching of the data trend, and a smaller weight is configured to the absolute error of the data, so that the influence caused by the unknown transmission power of the signal source can be reduced, and in this case, the transmission signal source power uses the fixed transmission power described above.

[0046] Through the above calculation processing, for each candidate position point in the expanded candidate position set, a corresponding set of received signal strength data obtained by simulation and prediction at all signal receiving device position points is obtained. For example, if there are 9 points in the expanded candidate set, and there are 8 receiving devices, the simulation P r values will be generated in this step, which will be organized into 9 groups of 8-element data sets, and each data set represents the simulation electromagnetic fingerprint of a candidate point.

[0047] In some embodiments, in the step of S5, the purpose is to quantify the difference between the simulation data corresponding to each candidate position point in the expanded candidate position set and the measured data obtained by the combined processing of the monitoring data, by introducing two complementary statistical indicators, root mean square error (RMSE) and standard deviation of difference data value (ESD), so as to comprehensively evaluate the matching degree of the simulation data and the measured data in terms of absolute value and relative trend.

[0048] Specifically, the simulation signal strength data and the measured data at each candidate point are compared and analyzed, and the root mean square error of the two groups of data is calculated RMSE and the standard deviation of the difference data value ESD .

[0049] Further, the root mean square error RMSE is used to quantify the absolute error between the simulation data and the measured data, reflecting the average prediction deviation of the simulation model to the actual electromagnetic environment; the calculation formula is: ; wherein, n is the number of measurement data; is the difference between the simulation path loss and the measured path loss, , PL i-p is the path loss predicted by the electromagnetic wave propagation model simulation; PL i-m is the path loss value obtained by inversely calculating the measurement data and the transceiver device model parameters according to the received power formula.

[0050] Further, the standard deviation of the difference data value ESD is used to quantify the dispersion degree of the trend difference between the simulation data and the measured data, which RMSE focuses on the absolute deviation, ESD and is more focused on evaluating the consistency of the distribution trend of the simulation and measured data on different receiving devices; the calculation formula is: ; wherein, is the average value of the difference between the simulation path loss and the measured path loss.

[0051] In this embodiment, the calculated RMSE and ESD are used to generate a single error evaluation index EE that can comprehensively reflect the conformity between the simulation data and the measured data, wherein, RMSE the weight of 5%, ESD the weight of 95%, to obtain the comprehensive EE between the simulation data and the measured data: EE= 0.95*RMSE +0.05* ESD ; Understandably, the setting of weighting coefficients reflects a high degree of emphasis on the consistency of trends between simulated and measured data, while exhibiting a relatively low tolerance for absolute numerical differences. This strategy stems from the fact that in complex indoor environments, the actual transmit power of a signal source is often unknown and potentially fluctuating, or environmental differences may lead to systematic biases in the absolute power prediction of the corrected free-space propagation model. Over-reliance on... ESD Absolute error metrics, such as those for signal attenuation, can significantly influence positioning results due to these uncertainties. In contrast, the signal attenuation trend in space (i.e., relative intensity distribution) is more stable and predictable in most cases, and less affected by the absolute value of the transmitted power. RMSE Higher weights effectively mitigate the potential impact of unknown or fluctuating signal source transmission power and absolute model bias on the final positioning results, making the positioning process more focused on matching the spatial distribution trend of the electromagnetic environment. For most typical indoor positioning applications, especially when the signal source power is unknown, the above weight allocation of 0.95 and 0.05 can provide more robust and accurate positioning results.

[0052] In some specific implementations, step S6 involves evaluating and sorting all candidate location points in the expanded candidate location set; the evaluation criterion is the magnitude of the error evaluation index value, since... EE The value quantifies the degree of difference or mismatch between the simulated propagation characteristics and the actual monitored electromagnetic environment, therefore EE The smaller the value, the more realistic the assumption that the candidate point is a signal source is. This implementation selects... EE The candidate location with the smallest value is taken as the final signal source location result.

[0053] For example, if the expanded candidate location set contains N Candidate points P 1 , P 2 ,…, P N And their corresponding error evaluation indicators are respectively EE 1 , EE 2 ,…, EE N This step will iterate through all of them. EE Find the minimum value among the values. EE min .and EE min Corresponding candidate location pointsP optimal i.e. the best estimate of the signal source location; the candidate location point with the minimum error evaluation indicator, representing that its assumed electromagnetic propagation characteristics (including the absolute value of path loss and the relative variation trend) match the actual monitored electromagnetic environment best among all the expanded candidate location points; this matching degree, after the smoothing processing of the monitoring data, the simulation calculation based on the physical model, and the combined preference of the trend consistency in the previous steps, makes the finally selected location point have a high degree of confidence, and finally, the output will be the two-dimensional coordinates of the optimal signal location candidate point, such as (8.0, 0.5), representing the accurate positioning result of the indoor signal source. X optimal , Y optimal .

[0054] Embodiment 1: In this embodiment, an indoor laboratory environment with a size of 10m x 3m is selected as the test area, and the internal structure of the laboratory is relatively complex, including multiple partitions, metal shelves and office desks and chairs, simulating a typical indoor multipath propagation environment.

[0055] The signal receiving device is: 8 BLE receiving gateways are deployed, which are uniformly distributed on the walls of the laboratory, 1.5m high from the ground, and the accurate position coordinates of the receiving gateways have been determined in advance by a laser range finder and a building CAD drawing, and are recorded into the system.

[0056] The signal transmitting source is: a BLE beacon is used as the signal transmitting source, the actual position of the signal source is as shown in Figure 2 , and the known true position is P GT (8.3, 0.4) meters, and the transmission frequency of the beacon is F MHz 2402 MHz; the positioning result and error of the embodiment are shown in Table 1.

[0057] Table 1: Positioning result and error of embodiment 1

[0058] As shown in Table 1, the method proposed by the present application can effectively correct the preliminary positioning result by expanding the candidate points, merging the monitoring data, fine simulation and combined evaluation of RMSE and ESD , as shown in Figure 3 , the final signal source position is determined as (8.0, 0.5); the error in the X direction with the true position is only 0.3m, and the error in the Y direction is only 0.1m; it shows that the method proposed by the present application can effectively correct the preliminary positioning result.

[0059] The above is based on the ideal embodiment of the present application, through the above description, for those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0060] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that those skilled in the art can understand.

Claims

1. A method for screening and locating candidate points using monitoring data comparison, characterized in that, Includes the following steps: S1. Obtain the initial set of candidate locations for the signal source; S2. Spatial expansion is performed on each initial candidate location point in the initial candidate location set of the signal source to generate an expanded candidate location set containing the initial candidate location point and multiple potential location points in its neighboring region. S3. Spatial filtering and smoothing are performed on the original acquired received signal strength data to obtain the merged monitoring data; S4. For each candidate location point in the expanded candidate location set, assuming that the signal source is located at that point, calculate the simulated signal strength at each receiving device location using the electromagnetic wave propagation model based on the signal source operating parameters and the receiving device location information. S5. Compare the simulated signal strength corresponding to each candidate location point with the merged monitoring data, calculate the root mean square error and the standard deviation of the difference data values, and combine them to calculate the error evaluation index. S6. Select the candidate location point with the smallest error evaluation index as the final positioning result of the signal transmission source.

2. The method for screening and locating candidate points using monitoring data comparison according to claim 1, characterized in that: In step S1, the set is obtained by constructing electromagnetic situation information from the received signal data collected in space and performing preliminary positioning based on the electromagnetic situation information to obtain the potential location coordinates of one or more signal transmitting sources.

3. The method for screening and locating candidate points using monitoring data comparison according to claim 1, characterized in that: In step S2, the spatial expansion method includes offsetting the initial candidate position point by a preset expansion distance in eight directions: up, down, left, right, upper left, lower left, upper right, and lower right, to generate nine candidate position points, including the initial point.

4. The method for screening and locating candidate points using monitoring data comparison according to claim 3, characterized in that: The extended distance is configured to be 0.2m or 0.5m depending on the initial positioning error range and environmental complexity.

5. The method for screening and locating candidate points using monitoring data comparison according to claim 1, characterized in that: In step S3, the spatial filtering and smoothing process includes: For the received signal strength at each receiving device location, identify all other measurement data points whose spatial geometric distance is within a preset threshold distance; An arithmetic mean operation is performed on the received signal strength values ​​of the data points, and the average value is used as the updated received signal strength value for that location point.

6. The method for screening and locating candidate points using monitoring data comparison according to claim 1, characterized in that: In step S4, the electromagnetic wave propagation model is a modified free-space propagation model, and its path loss calculation formula is as follows: ;in, f It is the frequency of the received signal; d It is the distance between the transmitting and receiving devices; a This is the distance influence weighting coefficient, with a value ranging from 1.5 to 4.

7. The method for screening and locating candidate points using monitoring data comparison according to claim 1, characterized in that: In step S5, the formula for calculating the error evaluation index is as follows: EE= 0.95* RMSE +0.05* ESD ;in, RMSE It is the root mean square error. ESD It is the standard deviation of the difference data values.

8. The method for screening and locating candidate points using monitoring data comparison according to claim 7, characterized in that: The formula for calculating the root mean square error is: ;in, n It is the number of measurement data; It is the difference between the simulated path loss and the measured path loss. , PL i-p This is the simulated path loss; PL i-m This is the measured path loss value.

9. A method for screening and locating candidate points using monitoring data comparison according to claim 8, characterized in that: The formula for calculating the standard deviation of the difference data values ​​is: ;in, yes The average value.

10. A method for screening and locating candidate points using monitoring data comparison according to claim 1, characterized in that: In step S6, the final positioning result is output in the form of two-dimensional coordinates.

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