A synchronous fusion positioning method based on arrival angle and RSSI value

Abstract

The invention discloses a synchronous fusion positioning method based on arrival angle and RSSI value, comprising: a base station sends a terminal MAC address, an arrival angle, an RSSI value, a count value and a base station MAC address extracted from a radio signal periodically broadcast by a terminal to a server; the server classifies received data according to the terminal MAC address, groups each type of data according to the base station MAC address, and sorts each group of data according to the count value; each group of data is subjected to extended Kalman filtering in the order of the count value, and abnormal data in the arrival angle and RSSI value are processed; each type of data selects corresponding arrival angle and RSSI value data from each group according to the count value for weight design, combines the base station position coordinates and the tag working height information, and realizes fusion positioning by using a triangulation positioning method and a pole method; finally, each type of fusion positioning estimation value is subjected to Kalman filtering according to the count value to obtain a stable fusion result.

Description

Technical Field

[0001] The present invention relates to the technical field of radio signal direction finding, and in particular to a synchronous fusion positioning method based on arrival angle and RSSI value. Background Art

[0002] With the development of wireless communication technology and the popularization of mobile intelligent terminals, indoor positioning has become a hot application in the current information industry and presents a broad market prospect. The system based on multi-station radio direction finding positioning has high positioning accuracy, but it is affected by multipath, interference, occlusion, etc., and the positioning stability faces great challenges. The present invention classifies and groups observation data such as multi-station direction finding and RSSI values ​​according to the terminal MAC address and base station MAC address, and sorts them according to the count value, and performs extended Kalman filtering on each group of data to process the abnormal data in the arrival angle and RSSI value, and selects the corresponding base station and terminal information from each group according to the count value for each type of data. Weight design is performed, and the triangulation positioning method and the pole method are used to realize fusion positioning, and a stable fusion result is obtained through Kalman filtering. The present invention enables the server to obtain a stable and accurate terminal position estimate, and has the characteristics of distributed filtering, synchronous fusion, and high positioning accuracy. Summary of the invention

[0003] In order to overcome the deficiencies in the prior art, the present invention provides a synchronous fusion positioning method based on arrival angle and RSSI value, which can enable the server to obtain a stable and accurate terminal position estimation value, and has the characteristics of distributed filtering, synchronous fusion and high positioning accuracy.

[0004] In order to achieve the above-mentioned invention object, the technical solution adopted to solve the technical problem is as follows:

[0005] A synchronous fusion positioning method based on arrival angle and RSSI value includes the following steps:

[0006] Step S1: The base station sends the terminal MAC address, arrival angle, RSSI value, count value and base station MAC address extracted from the radio signal periodically broadcast by the terminal to the server;

[0007] Step S2: The server classifies the received data according to the terminal MAC address, groups each type of data according to the base station MAC address, and sorts each group of data according to the count value;

[0008] Step S3: Each set of data is subjected to extended Kalman filtering in the order of count values, and abnormal data in the arrival angle and RSSI value are processed;

[0009] Step S4: For each type of data, the corresponding arrival angle and RSSI value data are selected from each group according to the count value for weight design, and the fusion positioning is realized by using the triangulation positioning method and the pole method in combination with the base station location coordinates and the tag working height information;

[0010] Step S5: Perform Kalman filtering on each type of fused positioning estimation value according to the count value to obtain a stable fusion result.

[0011] Furthermore, in step S1:

[0012] Step S11: The radio signal periodically broadcast by the terminal includes a MAC address for distinguishing the terminal and a count value for time synchronization;

[0013] Step S12: The base station observes the radio signal broadcast by the terminal and extracts the arrival angle, RSSI value, MAC address and count value;

[0014] Step S13: The base station actively pushes the extracted terminal information and the MAC address of the base station to the server.

[0015] Furthermore, the step S2 includes the following steps:

[0016] Step S21: the server receives multiple terminal observation data pushed by multiple base stations;

[0017] Step S22: The server classifies the received data according to the terminal MAC address, and all data in each category corresponds to all information of a terminal;

[0018] Step S23: the server groups each type of data according to the base station MAC address, and all data in each group corresponds to all information of a terminal observed by a base station;

[0019] Step S24: the server sorts each group of data in order according to the count value.

[0020] Furthermore, the step S3 includes the following steps:

[0021] Step S31: filtering the nonlinear observations in each set of data by using an extended Kalman filter algorithm;

[0022] Step S32: based on the base station angle coverage range and distance coverage range, the pitch angle and RSSI value threshold are set as the access conditions for the terminal to enter the base station working area. When the observation data meets the access conditions, it is considered that the terminal enters the base station working range, and the starting state of the extended Kalman filter is set according to the latest observation data;

[0023] Step S33: based on the base station angle coverage range, distance coverage range, and count value, the pitch angle, RSSI value threshold, and count value threshold are set as the departure condition for the terminal to leave the base station working area. When the observed data or count value meets the departure condition, it is considered that the terminal has left the base station working range and the extended Kalman filter is stopped.

[0024] Step S34: within the coverage of the base station, perform extended Kalman filtering on the observed data. When the predicted value deviates greatly from the observed value, take the predicted value. When the count value is lost, use the predicted value instead.

[0025] Furthermore, the step S4 includes the following steps:

[0026] Step S41: for each type of data, corresponding arrival angle and RSSI value data are selected from each group according to the count value;

[0027] Step S42: Bringing together the observation coordinate systems of each base station and correcting the angle observation data of each base station;

[0028] Step S43: The closer the terminal is to the base station, the larger the pitch angle observed by the base station and the stronger the energy. The weight is designed according to the pitch angle and the RSSI value. The larger the pitch angle and the stronger the energy, the larger the weight value. On the contrary, the smaller the pitch angle and the weaker the energy, the smaller the weight value.

[0029] Step S44: combining the base station location coordinates and the tag working height information, using the triangulation positioning method and the pole method to perform fusion positioning;

[0030] Step S45: cluster and group data by weight, and group data with similar weights as a cluster group and assign them the same weight;

[0031] Step S46: When there are two or more observation data with similar weights in each cluster group, the triangulation positioning method is used to obtain the positioning result of the cluster group; when there is only one observation data, the pole method is used to obtain the positioning result of the cluster group;

[0032] Step S47: Combining the positioning results of each cluster group with the cluster weight, weighted processing is performed to obtain a fused positioning estimation value corresponding to the current count value.

[0033] Furthermore, the step S5 includes the following steps:

[0034] Step S51: smoothing the fused positioning estimate value by Kalman filtering;

[0035] Step S52: design the access conditions for the terminal to enter the system working area in combination with the coverage of the positioning system, the base station observed elevation angle and the RSSI value. When the observed data meets the access conditions, it is considered that the terminal enters the system working range, and the starting state of the Kalman filter is set according to the latest fusion positioning estimate value;

[0036] Step S53: design the leaving condition of the terminal leaving the system working area in combination with the coverage of the positioning system, the pitch angle observed by the base station, the RSSI value and the count value. When the observation data or the count value meets the leaving condition, it is considered that the terminal leaves the system working range and the Kalman filter is stopped.

[0037] Step S54: Perform Kalman filtering on the fused positioning estimate within the system coverage area.

[0038] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0039] The invention discloses a synchronous fusion positioning method based on arrival angle and RSSI value, which can enable a server to obtain a stable and accurate terminal position estimation value, and has the positive effects of distributed filtering, synchronous fusion and high positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0041] Figure 1 It is a flow chart of a synchronous fusion positioning method based on arrival angle and RSSI value of the present invention;

[0042] Figure 2 It is a schematic diagram of a positioning system corresponding to a synchronous fusion positioning method based on arrival angle and RSSI value of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] This embodiment discloses a synchronous fusion positioning method based on arrival angle and RSSI value. The process is as follows: Figure 1 As shown, the corresponding positioning system is as Figure 2As shown, the specific steps include:

[0045] Step S1: The base station sends the terminal MAC address, arrival angle, RSSI value, count value and base station MAC address extracted from the radio signal periodically broadcast by the terminal to the server;

[0046] Step S2: The server classifies the received data according to the terminal MAC address, groups each type of data according to the base station MAC address, and sorts each group of data according to the count value;

[0047] Step S3: Each set of data is subjected to extended Kalman filtering in the order of count values, and abnormal data in the arrival angle and RSSI value are processed;

[0048] Step S4: For each type of data, the corresponding arrival angle and RSSI value data are selected from each group according to the count value for weight design, and the fusion positioning is realized by using the triangulation positioning method and the pole method in combination with the base station location coordinates, the tag working height and other information;

[0049] Step S5: Perform Kalman filtering on each type of fused positioning estimation value according to the count value to obtain a stable fusion result.

[0050] Furthermore, in step S1:

[0051] Step S11: The radio signal periodically broadcast by the terminal includes a MAC address for distinguishing the terminal and a count value for time synchronization;

[0052] Step S12: The base station observes the radio signal broadcast by the terminal and extracts the arrival angle, RSSI value, MAC address and count value;

[0053] Step S13: The base station actively pushes the extracted terminal information and the MAC address of the base station to the server.

[0054] Furthermore, the step S2 includes the following steps:

[0055] Step S21: the server receives multiple terminal observation data pushed by multiple base stations;

[0056] Step S22: The server classifies the received data according to the terminal MAC address, and all data in each category corresponds to all information of a terminal;

[0057] Step S23: the server groups each type of data according to the base station MAC address, and all data in each group corresponds to all information of a terminal observed by a base station;

[0058] Step S24: the server sorts each group of data in order according to the count value. This is because active push may be out of order due to channel congestion.

[0059] Furthermore, the step S3 includes the following steps:

[0060] Step S31: filtering the nonlinear observations (arrival angle and RSSI value) in each set of data by using an extended Kalman filter algorithm;

[0061] Step S32: based on the base station angle coverage range and distance coverage range, the pitch angle and RSSI value threshold are set as the access conditions for the terminal to enter the base station working area. When the observation data meets the access conditions, it is considered that the terminal enters the base station working range, and the starting state of the extended Kalman filter is set according to the latest observation data;

[0062] Step S33: based on the base station angle coverage range, distance coverage range, and count value, the pitch angle, RSSI value threshold, and count value threshold are set as the departure condition for the terminal to leave the base station working area. When the observed data or count value meets the departure condition, it is considered that the terminal has left the base station working range and the extended Kalman filter is stopped.

[0063] Step S34: within the coverage of the base station, perform extended Kalman filtering on the observed data. When the predicted value deviates greatly from the observed value, take the predicted value. When the count value is lost, use the predicted value instead.

[0064] Furthermore, the step S4 includes the following steps:

[0065] Step S41: for each type of data, corresponding arrival angle and RSSI value data are selected from each group according to the count value;

[0066] Step S42: Bringing together the observation coordinate systems of each base station and correcting the angle observation data of each base station;

[0067] Step S43: The closer the terminal is to the base station, the larger the pitch angle observed by the base station and the stronger the energy. The weight is designed according to the pitch angle and the RSSI value. The larger the pitch angle and the stronger the energy, the larger the weight value. On the contrary, the smaller the pitch angle and the weaker the energy, the smaller the weight value.

[0068] Step S44: Combine the base station location coordinates, tag working height and other information to perform fusion positioning using triangulation positioning method and pole method;

[0069] Step S45: cluster and group data by weight, and group data with similar weights as a cluster group and assign them the same weight;

[0070] Step S46: When there are two or more observation data with similar weights in each cluster group, the triangulation positioning method is used to obtain the positioning result of the cluster group; when there is only one observation data, the pole method is used to obtain the positioning result of the cluster group;

[0071] Step S47: Combining the positioning results of each cluster group with the cluster weight, weighted processing is performed to obtain a fused positioning estimation value corresponding to the current count value.

[0072] Furthermore, the step S5 includes the following steps:

[0073] Step S51: smoothing the fused positioning estimate value by Kalman filtering;

[0074] Step S52: design the access conditions for the terminal to enter the system working area in combination with the coverage of the positioning system, the base station observed elevation angle and the RSSI value. When the observed data meets the access conditions, it is considered that the terminal enters the system working range, and the starting state of the Kalman filter is set according to the latest fusion positioning estimate value;

[0075] Step S53: design the leaving condition of the terminal leaving the system working area in combination with the coverage of the positioning system, the pitch angle observed by the base station, the RSSI value and the count value. When the observation data or the count value meets the leaving condition, it is considered that the terminal leaves the system working range and the Kalman filter is stopped.

[0076] Step S54: Perform Kalman filtering on the fused positioning estimate within the system coverage area.

[0077] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A synchronous fusion positioning method based on arrival angle and RSSI value, characterized in that: The following steps are involved: Step S1: The base station sends the terminal MAC address, arrival angle, RSSI value, count value and base station MAC address extracted from the radio signal periodically broadcast by the terminal to the server; Step S2: The server classifies the received data according to the terminal MAC address, groups each type of data according to the base station MAC address, and sorts each group of data according to the count value; Step S3: Each set of data is subjected to extended Kalman filtering in the order of count values, and abnormal data in the arrival angle and RSSI value are processed; Step S4: For each type of data, the corresponding arrival angle and RSSI value data are selected from each group according to the count value for weight design, and the fusion positioning is realized by using the triangulation positioning method and the pole method in combination with the base station location coordinates and the tag working height information; The step S4 includes the following steps: Step S41: for each type of data, corresponding arrival angle and RSSI value data are selected from each group according to the count value; Step S42: Bringing together the observation coordinate systems of each base station and correcting the angle observation data of each base station; Step S43: The closer the terminal is to the base station, the larger the pitch angle observed by the base station and the stronger the energy. The weight is designed according to the pitch angle and the RSSI value. The larger the pitch angle and the stronger the energy, the larger the weight value. On the contrary, the smaller the pitch angle and the weaker the energy, the smaller the weight value. Step S44: combining the base station location coordinates and the tag working height information, using the triangulation positioning method and the pole method to perform fusion positioning; Step S45: cluster and group data by weight, and group data with similar weights as a cluster group and assign them the same weight; Step S46: When there are two or more observation data with similar weights in each cluster group, the triangulation positioning method is used to obtain the positioning result of the cluster group; when there is only one observation data, the pole method is used to obtain the positioning result of the cluster group; Step S47: combining the positioning results of each cluster group with the cluster weight, and performing weighted processing to obtain a fused positioning estimation value corresponding to the current count value; Step S5: Perform Kalman filtering on each type of fused positioning estimation value according to the count value to obtain a stable fusion result.

2. The synchronous fusion positioning method based on arrival angle and RSSI value according to claim 1 is characterized in that: In step S1: Step S11: The radio signal periodically broadcast by the terminal includes a MAC address for distinguishing the terminal and a count value for time synchronization; Step S12: The base station observes the radio signal broadcast by the terminal and extracts the arrival angle, RSSI value, MAC address and count value; Step S13: The base station actively pushes the extracted terminal information and the MAC address of the base station to the server.

3. The synchronous fusion positioning method based on arrival angle and RSSI value according to claim 1 is characterized in that: The step S2 includes the following steps: Step S21: the server receives multiple terminal observation data pushed by multiple base stations; Step S22: The server classifies the received data according to the terminal MAC address, and all data in each category corresponds to all information of a terminal; Step S23: the server groups each type of data according to the base station MAC address, and all data in each group corresponds to all information of a terminal observed by a base station; Step S24: the server sorts each group of data in order according to the count value.

4. The synchronous fusion positioning method based on arrival angle and RSSI value according to claim 1 is characterized in that: The step S3 includes the following steps: Step S31: filtering the nonlinear observations in each set of data by using an extended Kalman filter algorithm; Step S32: based on the base station angle coverage range and distance coverage range, the pitch angle and RSSI value threshold are set as the access conditions for the terminal to enter the base station working area. When the observation data meets the access conditions, it is considered that the terminal enters the base station working range, and the starting state of the extended Kalman filter is set according to the latest observation data; Step S33: based on the base station angle coverage range, distance coverage range, and count value, the pitch angle, RSSI value threshold, and count value threshold are set as the departure condition for the terminal to leave the base station working area. When the observed data or count value meets the departure condition, it is considered that the terminal has left the base station working range and the extended Kalman filter is stopped. Step S34: within the coverage of the base station, perform extended Kalman filtering on the observed data. When the predicted value deviates greatly from the observed value, take the predicted value. When the count value is lost, use the predicted value instead.

5. The synchronous fusion positioning method based on arrival angle and RSSI value according to claim 1 is characterized in that: The step S5 includes the following steps: Step S51: smoothing the fused positioning estimate value by Kalman filtering; Step S52: design the access conditions for the terminal to enter the system working area in combination with the coverage of the positioning system, the base station observed elevation angle and the RSSI value. When the observed data meets the access conditions, it is considered that the terminal enters the system working range, and the starting state of the Kalman filter is set according to the latest fusion positioning estimate value; Step S53: design the leaving condition of the terminal leaving the system working area in combination with the coverage of the positioning system, the pitch angle observed by the base station, the RSSI value and the count value. When the observation data or the count value meets the leaving condition, it is considered that the terminal leaves the system working range and the Kalman filter is stopped. Step S54: Perform Kalman filtering on the fused positioning estimate within the system coverage area.

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