A method for contrast screening and positioning candidate points using monitoring data

Abstract

The application discloses a kind of methods for screening and positioning candidate points using monitoring data contrast, it is related to the field of radio orientation, the method comprises the following steps: S1, obtains candidate position set;S2, the spatial extension is carried out to candidate position set, generates augmented candidate position set;S3, processing received signal strength data, obtain the monitoring data after merging;S4, calculate simulation signal strength;S5, contrast simulation and monitoring data, calculate root mean square error and the standard deviation of difference data value, and combined error evaluation index is calculated;S6, select the candidate position point of minimum error evaluation index as final positioning result.Based on the preliminary positioning result, introduce systematic, high-resolution fine evaluation and screening mechanism, its comparison mechanism is based on physical propagation model, can more truly reflect the electromagnetic relationship between signal source and receiving point, so that the positioning result is closer to the real position of 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 above prior art scheme generates a continuous spatial electromagnetic situation map from limited and discrete actual measurement point data, which is essentially a mathematical speculation process. No matter what advanced transform domain processing technology is used, 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 importantly, the existing technology directly extracts the signal source position based on the situation map itself with speculative nature, lacking a mechanism for backtracking comparison and local fine verification with the original high-resolution measured data; 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-mentioned purpose, the technical scheme adopted by the present application is as follows: a method for screening positioning candidate points using monitoring data, comprising the following steps:

[0008] S1, obtaining an initial signal source candidate position set;

[0009] 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 its adjacent area;

[0010] S3, performing spatial filtering and smoothing processing on the originally collected received signal strength data to obtain merged monitoring data;

[0011] 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;

[0012] 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;

[0013] S6, selecting the candidate position point with the smallest error evaluation index as the final positioning result of the signal transmitting source.

[0014] In a preferred embodiment of the present application, in the step S1, the set is obtained by constructing electromagnetic situation information from the received signal data collected in the space and performing preliminary positioning based on the electromagnetic situation information to obtain one or more potential position coordinate points of the signal emission source;

[0015] In a preferred embodiment of the present application, in the step S2, the space expansion manner includes offsetting in the preset expansion distance in the eight directions of up, down, left, right, upper left, lower left, upper right, and lower right of the initial candidate position point to generate nine candidate position points including the initial point.

[0016] In a preferred embodiment of the present application, the expansion distance is configured as 0.2 m or 0.5 m according to the preliminary positioning error range and the environmental complexity.

[0017] In a preferred embodiment of the present application, in the step S3, the space filtering and smoothing processing includes:

[0018] For the received signal strength at each receiving device position, all other measurement data points with a spatial geometric distance within a preset threshold distance are identified;

[0019] An arithmetic average operation is performed on the received signal strength values of the data points, and the average value is taken as the updated received signal strength value of the position point.

[0020] 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:

[0021] ; 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.

[0022] In a preferred embodiment of the present application, in the step S5, the calculation formula of the error evaluation index is:

[0023] 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.

[0024] In a preferred embodiment of the present application, the calculation formula of the root mean square error is:

[0025] ; wherein, n is the number of measurement data; is a difference between the simulated path loss and the measured path loss, , PL i-p is a simulated path loss; PL i-m is a measured path loss value.

[0026] In a preferred embodiment of the present application, the formula for calculating the standard deviation of the difference data value is:

[0027] ; wherein, is an average value.

[0028] 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.

[0029] The present application solves the defects in the background art and has the following beneficial effects:

[0030] (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 on the basis of 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 the positioning point from the situation map with uncertainty, the present application performs a direct, quantitative and fine comparison analysis between the simulation propagation characteristics of the preliminary candidate position point 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.

[0031] (2) In the present application, the initial candidate position point is 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 optimize when multiple potential signal source regions are generated due to multipath effect 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

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0033] 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;

[0034] Figure 2 is a map of the actual positions of signal sources according to Embodiment 1 of the present application;

[0035] Figure 3 is a map of the positioning results of signal sources according to Embodiment 1 of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0037] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details that are set forth in the following description, and therefore, the scope of the present application is not limited by the following disclosed embodiments.

[0038] It should be noted that the method of the present application is deployed in an indoor positioning system, which includes a plurality of distributed signal receiving devices, which can be wireless local area network access points, Bluetooth low power consumption gateways, or ultra-wideband receiving modules; and signal transmitting sources can be any device that emits radio frequency signals, such as Wi-Fi modules, BLE beacons or UWB tags. The present embodiment assumes that the signal receiving devices have been pre-calibrated accurately in space, and their geographic coordinate information has been accurately recorded into the system.

[0039] As shown in Figure 1 , a method for screening and positioning candidate points using monitoring data, comprising the following steps:

[0040] S1, obtaining an initial signal source candidate position set; 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 one or more potential position coordinate points of the signal transmitting source;

[0041] S2, spatially expand 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;

[0042] S3, perform spatial filtering and smoothing processing on the originally collected received signal strength data to obtain merged monitoring data;

[0043] S4, for each candidate position point in the expanded candidate position set, assume that the signal transmitting source is located at the point, calculate the simulated signal strength at each receiving device location according to the signal transmitting source operating parameters and receiving device location information through the electromagnetic wave propagation model;

[0044] S5, compare the simulated signal strength corresponding to each candidate position point with the merged monitoring data, calculate the root mean square error and the standard deviation of the difference data value, and combine to calculate the error evaluation index;

[0045] S6, select the candidate position point with the smallest error evaluation index as the final positioning result of the signal transmitting source.

[0046] In some specific embodiments, in the step of S1, the purpose is to preliminarily identify the potential position area of the signal transmitting source by macroscopically analyzing the received signal data collected by the signal receiving devices widely deployed in the target space.

[0047] 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.

[0048] 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 the method based on 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 use the method based on propagation model, such as trilateration and multilateration, which reverses the signal source distance by combining the known transmitting power and receiving power with the electromagnetic wave propagation model, and then determines its position.

[0049] 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 signal source position can be extracted.

[0050] In actual operation, if the method based on RSSI fingerprint map is adopted, the real-time RSSI is matched with the pre-constructed fingerprint library, such as K-nearest neighbor algorithm or 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, the preliminary coordinates of the signal source are calculated according to the distance relationship between each receiving device and the signal source by solving a set of nonlinear equations.

[0051] It should be noted that no matter what method is used, the output of this step is one or more potential signal 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 the accuracy is limited by the original data quality, model error and environmental complexity.

[0052] Exemplarily, in a 20m*20m indoor environment, the initial positioning algorithm can estimate one or two signal source positions with high probability according to the RSSI data fed back by 8 uniformly distributed receivers, and the estimation accuracy is between 1m and 3m.

[0053] In some specific embodiments, in the step of S2, the purpose of implementation is to perform a fine search for the localization of the initial positioning result obtained in S1, to make up for the possible slight deviation of the preliminary positioning, and to provide more dense candidate sample points for subsequent high-resolution comparison; for each initial candidate position point in the initial signal source candidate position set, x 0 , y 0 , the embodiment performs systematic deviation in the two-dimensional space centered on the point to generate an expanded candidate position set containing the initial candidate position point and multiple potential position points in the adjacent area of the initial candidate position point.

[0054] Specifically, the deviation operation covers the up, down, left, right, left up, left down, right up and right down directions, which means that for each initial candidate point, 8 new candidate position points will be generated in addition to the initial candidate point itself, totaling 9 candidate position points.

[0055] Wherein, the displacement of the deviation operation is controlled by a pre-configured expansion distance parameter Dext, and the expansion distance is a key system parameter, and its configuration value needs to be set comprehensively according to the specific positioning accuracy requirement, the complexity of the signal propagation environment and the expected error range of the preliminary positioning algorithm in the first step.

[0056] Exemplarily, in the scenario where the positioning accuracy requirement is high and the preliminary positioning error is small, the expansion distance Dext is configured as 0.2m; which means that around the initial candidate point, the new 8 points will be located at x0 ±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.

[0057] 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 ( x 0 ±0.5, y 0 ),( x 0 , y 0 ±0.5).

[0058] It should be noted that while a larger expansion distance increases the complexity of subsequent simulation calculations, it provides a broader distribution of candidate points, increasing the likelihood of finding the global optimum. All nine generated candidate locations will be added as an independent subset to the final expanded candidate location set. If the initial candidate location set contains multiple points, the expansion operation will be performed independently on each initial candidate point. For example, if the initial set contains two candidate points... PAP A and PBP B This step will generate a... PAP A Nine points centered on and with PBP B Nine points are centered on the initial location, forming a total of 18 candidate locations. These are then combined to form an expanded candidate location set. By creating a dense candidate grid within the neighborhood of the initial location results, sufficient resolution is provided for subsequent fine-grained comparisons and verifications, thereby improving the accuracy of the final location.

[0059] In some embodiments, the step of S3 is to perform spatial filtering and smoothing on the raw collected RSSI data to eliminate or significantly weaken the impact of instantaneous fluctuations in signal collection, local interference or sensor self-noise on the measurement data; the unprocessed raw 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.

[0060] Specifically, for each signal receiving device at its respective preset position x j , y j collects the received signal strength measurement data P rx,meas,j , the step systematically traverses all other collected data points, for each to-be-processed measurement data point, such as 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 preset threshold distance D th ; the geometric distance is the Euclidean distance.

[0061] Exemplarily, if the receiving device A is located at x A , y A and receives P rx,meas,A , the distances between the positions of all other receiving devices B , C , … and x B , y B , x C , y C , x A , y A will be calculated; if , P rx,meas,B will be included in the smoothing processing set of P rx,meas,A .

[0062] After all the data points that meet the distance condition are identified, an arithmetic average operation is performed on the received signal strength values of the identified data points, and the result of the arithmetic average operation is taken as the updated received signal strength measurement data of the position point P rx,meas,k The original measurement data point will be replaced P rx,meas,k The corresponding received signal strength value is taken as the updated received signal strength measurement data of the position point. After the merging process, the number of original collected data points, i.e., the number of signal receiving devices, remains unchanged, but 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.

[0063] wherein 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 effects, shielding, and absorption, resulting in rapid spatial changes, in order to effectively suppress these local fluctuations, the threshold distance D th may be set to 2m, meaning that within a 2m radius, the RSSI values of all measurement points will be averaged, thereby generating a more smoothed 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 local details of signal strength changes. In actual deployment, different D th values can be empirically tested or simulated to determine the most suitable value for a specific scenario.

[0064] In some specific embodiments, in the step S4, the purpose is to generate the corresponding theoretical electromagnetic propagation characteristic model for each candidate position point in the expanded candidate position set, and this process is achieved by assuming that the signal transmitting 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 assumed signal source can produce at all signal receiving device positions. This set of theoretical values will constitute an electromagnetic fingerprint of the candidate point, which will be used for subsequent comparison with actual monitoring data.

[0065] Specifically, for each candidate position point in the expanded candidate position set x s , y s , it is regarded as an assumed signal transmitting source, and then, according to the assumed signal transmitting source operating parameter characteristics and the position information of each signal receiving device, the electromagnetic wave propagation model is used to calculate the theoretical received signal strength of the assumed signal transmitting source at each signal receiving device positionx j , y j ) on the receiving signal strength that can be generated P rx,sim,j ; wherein the signal receiving device location information includes the geographic coordinates of each signal receiving device x j , y j ) and the gain of the receiving antenna G r .

[0066] 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 the model is as follows:

[0067] ; wherein, f is the frequency of the received signal; d is the distance between the transceiver devices; a is the distance impact weight coefficient, taking a value of 1.5-4.

[0068] Further, the calculation formula of the received power is as follows:

[0069] ; 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;

[0070] It can be understood that in the working parameters of the simulation calculation, the signal source transmission power information, antenna directivity gain and signal frequency information need to be used; the frequency information of the signal is known, and in the entire processing process, the transmitting antenna and the receiving antenna can be approximately considered as omnidirectional antennas, i.e., the difference in antenna directivity gain does not need to be considered; and the transmission power of the signal source is unknown, and this parameter is a problem to be solved in the simulation process.

[0071] If the simulation data and the measured data are more consistent in trend, it can be considered that the signal source is more likely to be at the candidate position. Since the use of a fixed transmission power does not affect the data trend of the simulation results, 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 as to reduce the influence caused by the unknown transmission power of the signal source. In this case, the transmission power of the signal source is the fixed transmission power described above.

[0072] Through the above calculation process, for each candidate position point in the expanded candidate position set, a corresponding set of received signal strength data predicted by simulation at all signal receiving device position points is obtained. For example, if the expanded candidate set has 9 points and there are 8 receiving devices, this step will generate 9x89x8 simulation values, which will be organized into 9 sets of 8-element data sets, and each data set represents the simulation electromagnetic fingerprint of a candidate point. P r

[0073] In some specific embodiments, in the step of S5, the purpose of implementation 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, the root mean square error (RMSE) and the standard deviation of the difference data value (ESD), it can comprehensively evaluate the matching degree of the simulation data and the measured data in terms of absolute value and relative trend.

[0074] Specifically, the simulation signal strength data and the measured data at each candidate point are compared and analyzed, and the root mean square error RMSE and the standard deviation of the difference data value ESD of the two sets of data are calculated.

[0075] 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; its calculation formula is:

[0076] ; 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 using the received power formula to inversely calculate the measured data and the transceiver device model parameters. ​

[0077] Further, the standard deviation of the difference data values ESD is used to quantify the degree of dispersion of the trend difference between the simulation data and the measured data, and RMSE instead of focusing on the absolute deviation, ESD more emphasis is placed on assessing the consistency of the distribution trend of the simulation and measured data on different receiving devices; the calculation formula is:

[0078] ; wherein, is the average value of the difference between the simulation path loss and the measured path loss.

[0079] In the embodiment, the calculated RMSE and ESD are used to generate a single error evaluation index that can comprehensively reflect the degree of conformity between the simulation data and the measured data EE , wherein, RMSE the weight accounts for 5%, ESD the weight accounts for 95%, to obtain the comprehensive EE between the simulation data and the measured data:

[0080] EE= 0.95* RMSE +0.05* ESD ;

[0081] It can be understood that the setting of the weight coefficient reflects the high emphasis on the trend consistency between the simulation data and the measured data, and the relatively low tolerance for the absolute value difference, and the strategy is that in the complex indoor environment, the real transmission power of the signal transmission source is often unknown and may fluctuate, or there is a systematic deviation in the absolute power prediction of the corrected free space propagation model due to environmental differences; if too much reliance is placed on ESD and other absolute error indicators, the positioning result may be greatly affected by these uncertain factors; in contrast, the signal attenuation trend in space (i.e., the relative intensity distribution) is more stable and predictable in most cases, and is less affected by the absolute value of the transmission power. By giving RMSE a higher weight, the potential impact of the unknown transmission power of the signal source or its volatility, as well as the absolute deviation of the model on the final positioning result is effectively reduced, making the positioning process focus more on matching the spatial distribution trend of the electromagnetic environment; for most typical indoor positioning applications, especially in the case of unknown signal source power, the above weight distribution of 0.95 and 0.05 can provide more robust and accurate positioning results.

[0082] In some specific embodiments, in the step S6, all candidate position points in the expanded candidate position set are evaluated and sorted; the evaluation standard is the size of the error evaluation index value, sinceEE 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.

[0083] 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 points P optimal This means that the optimal estimated location of the signal source is determined; the candidate location with the minimum error evaluation index represents the location among all expanded candidate locations whose assumed electromagnetic propagation characteristics (including the absolute value and relative trend of path loss) best match the actual monitored electromagnetic environment; this degree of matching, through the smoothing of monitoring data in the previous steps, simulation calculations based on the physical model, and a combined preference for trend consistency, makes the finally selected location point have a high degree of confidence. Finally, the output will be the two-dimensional coordinates of the optimal signal location candidate point, for example ( X optimal , Y optimal This represents the precise location result of the indoor signal source.

[0084] Example 1: In this example, an indoor laboratory environment with a size of 10m×3m was selected as the test area. The laboratory has a relatively complex internal structure, including multiple partitions, metal shelves and desks and chairs, simulating a typical indoor multipath propagation environment.

[0085] The signal receiving equipment consists of eight BLE receiving gateways, evenly distributed on the walls of the laboratory, 1.5m above the ground. The precise coordinates of the receiving gateways have been determined in advance using a laser rangefinder and architectural CAD drawings and entered into the system.

[0086] The signal emission source is a BLE beacon, the actual position of the signal source is as shown in Figure 2 P GT (8.3,0.4) meters, the emission frequency of the beacon is F MHz 2402 MHz; the positioning results and errors of the embodiments are shown in Table 1.

[0087] Table 1: Positioning results and errors of Example 1

[0088]

[0089] As shown in Table 1, the method proposed by the application can effectively correct the preliminary positioning results by expanding the candidate points, monitoring data merging, fine simulation and RMSE and ESD combination evaluation, as shown in Figure 3 , the final signal source position is (8.0, 0.5); the error in the X direction is only 0.3 m, and the error in the Y direction is only 0.1 m; it shows that the method proposed by the application can effectively correct the preliminary positioning results.

[0090] The above is based on the ideal embodiment of the application, through the above description, for those skilled in the art, obviously, the application is not limited to the details of the above exemplary embodiments, and can be realized in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the 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 application. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0091] 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 manner of the specification is only for the sake of clarity, those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can be combined to form other embodiments that those skilled in the art can understand.​

Claims

1. A method for contrast screening of positioning candidate points using monitoring data, characterized by, The method comprises 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 candidate position point and a plurality of potential position points in the vicinity of the initial candidate position point; wherein the spatial expansion includes offsetting in the up, down, left, right, upper left, lower left, upper right, and lower right directions of the initial candidate position point by a preset expansion distance to generate nine candidate position points including 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 emission source is located at the point, calculating the simulated signal strength at each receiving device location according to the signal emission source operating parameters and the 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 emission source.

2. The method of claim 1, wherein the method further comprises: determining a plurality of candidate points based on the monitoring data; and selecting a candidate point from the plurality of candidate points based on the comparison. In the step S1, the set is obtained by constructing electromagnetic situation information from the received signal data collected in the space and performing preliminary positioning based on the electromagnetic situation information to obtain one or more potential position coordinate points of the signal emission source.

3. The method of claim 1, wherein the method further comprises: determining a location of the candidate point based on the monitoring data. The expansion distance is configured as 0.2m or 0.5m according to the preliminary positioning error range and the environmental complexity.

4. The method of claim 1, wherein the method further comprises: determining a location of the candidate point based on the monitoring data. In the step S3, the spatial filtering and smoothing processing includes: For the received signal strength at each receiving device location, identifying all other measurement data points within a preset threshold distance in terms of spatial geometric distance; Performing an arithmetic average operation on the received signal strength values of the data points to take the average value as the updated received signal strength value of the location point.

5. The method of claim 1, wherein: In the step S4, the electromagnetic wave propagation model is a modified free space propagation model, and its path loss calculation formula is: ; wherein f is the frequency of the received signal; d is the distance between the transceiving devices; a is a distance impact weight coefficient, taking a value of 1.5-4.

6. The method of claim 1, wherein: 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 values.

7. The method of claim 6, wherein: The calculation formula of the root mean square error is: ; wherein, n is the number of measurement data; is the difference 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.

8. The method of claim 7, wherein: The calculation formula of the standard deviation of the difference data value is: ; wherein, is the average of .

9. The method of claim 1, wherein: In the step S6, the final positioning result is output in the form of two-dimensional coordinates.

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