CN113670312B - WiFi-RTT-based noise reduction and multipath effect removal system and method - Google Patents
WiFi-RTT-based noise reduction and multipath effect removal system and method Download PDFInfo
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- CN113670312B CN113670312B CN202110938075.4A CN202110938075A CN113670312B CN 113670312 B CN113670312 B CN 113670312B CN 202110938075 A CN202110938075 A CN 202110938075A CN 113670312 B CN113670312 B CN 113670312B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a system and a method based on WiFi-RTT noise reduction and multipath removal effect, wherein the system comprises a central processing unit, a data acquisition system, a mode screening monitoring system, a multipath removal effect system, a noise reduction system, a semi-system error calibration system and a positioning resolving system, wherein the data acquisition system transmits a data set to the central processing unit for data preprocessing, and the mode screening monitoring system performs mode distinction and obtains a state change time vector; the multipath effect removing system corrects the information after the data preprocessing to obtain multipath data removing information; correcting by a noise reduction system to obtain positioning resolving pre-information, and resolving the positioning resolving pre-information by a half-system error resolving system to obtain a half-system error vector; and finally, combining the half-system error vector and the state change time vector by the central processing unit to obtain a half-system error time correction, and resolving by using the half-system error time correction and the positioning resolving pre-information by the positioning resolving system.
Description
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a system and a method for noise reduction and multipath removal effect based on WiFi-RTT ranging.
Background
The application of location based services has led to an increasing interest in positioning techniques, while at the same time placing higher demands on the accuracy of the positioning results. With the popularization and application of smart phones, providing a plurality of mobile phones based on location services will become a main carrier for high-precision positioning of the masses in the future. Since navigation signals based on Global Navigation Satellite Systems (GNSS) are difficult to be received indoors, they cannot be used for indoor positioning. In order to solve the indoor positioning problem, various technical solutions have been proposed, such as WiFi, ultra wideband, bluetooth, inertial sensors, etc., and compared with other technologies, wiFi has received more attention due to its huge audience and low price. The distance measurement modes such as ToA, TDoA and the like have serious deviation of distance measurement results due to clock error, so that the distance measurement modes are extremely unreasonable to be used for positioning. The RTT ranging method can provide a more accurate ranging result due to the influence of eliminating the clock error, but still retains the characteristics of common radio frequency signals, namely strong multipath effect and large noise, and no reasonable solution is proposed at present.
Disclosure of Invention
The invention aims to provide a WiFi-RTT-based noise reduction and multipath effect removal algorithm so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the WiFi-RTT-based noise reduction and multipath removal effect system comprises a central processing unit, wherein the central processing unit is respectively connected with a data acquisition system, a mode screening monitoring system, a multipath removal effect system, a noise reduction system, a half-system error calibration system and a positioning resolving system,
the data acquisition system acquires and stores data into a data set;
the data acquisition system transmits the data set to the central processing unit for data preprocessing, and the central processing unit provides the information after the data preprocessing for the mode screening monitoring system;
the mode discrimination monitoring system performs mode discrimination through router information comparison, obtains a state change time vector, and then transmits the state change time vector to the central processing unit and the multipath effect removing system;
the de-multipath effect system is used for correcting the data preprocessed information through the state change time vector by using a de-multipath effect resolving algorithm to obtain de-multipath data information; the de-multipath effect system transmits the state change time vector and the de-multipath data information to the central processing unit and the noise reduction system;
the noise reduction system is used for correcting the multipath data information to obtain positioning calculation pre-information through the state change time vector by using a noise reduction algorithm, and then transmitting the state change time vector and the positioning calculation pre-information to the central processing unit and the half-system error calibration system;
the half-system error calibration system is used for calculating the positioning calculation pre-information through the state change time vector by utilizing a half-system error calculation algorithm to obtain a half-system error vector; the half-system error calibration system transmits the half-system error vector to the central processing unit;
the central processing unit combines the half-system error vector and the state change time vector to obtain a half-system error time correction, and then transmits the half-system error time correction and positioning calculation pre-information to the positioning calculation system;
the positioning calculation system is used for obtaining a final positioning calculation result through time interval limitation.
According to another aspect of the present invention, a method for noise reduction and multipath removal based on WiFi-RTT is provided, comprising the steps of:
step 7, the central processing unit combines the half-system error vector and the state change time vector to obtain a half-system error time correction, and then transmits the half-system error time correction and positioning calculation pre-information to the positioning calculation system;
and 8, the positioning calculation system obtains a final positioning calculation result through time interval limitation.
Further, in the step 4, the de-multipath effect resolving algorithm obtains the state change time vector and the preprocessed data to obtain de-multipath data information, which specifically includes the following steps:
(4.1) grouping data according to time according to the state change time vector;
(4.2) each set of data was subjected to a k-means clustering process, which was specifically described as follows:
(4.2.1) normalizing the data, specifically described as: x (i) = (x (i) -min (x ()))/(max (x (:) -min (x ()))) wherein x represents a data matrix, i represents current time data, max (x ()) and min (x ()) distributions represent maximum and minimum values in the x matrix, and step (4.2.2) is entered;
(4.2.2) defining a maximum number of classifications: k, and enter step (4.2.3);
(4.2.3) randomly giving an initial cluster center position, and adjusting the cluster center to be optimal according to the cost function;
(4.2.4) checking whether the data statistical information under the optimal clustering center is compliant with normal distribution, and if so, entering a step (4.2.5); otherwise, returning to the step (4.2.3) to replace the cost function for recalculation;
(4.2.5) calculating the sum of the cost of the optimal clustering centers under different classification numbers, obtaining a cost function distribution function, determining an optimal classification number ko, and entering a step (4.2.6);
(4.2.6) recalling the classification result under the optimal classification number ko as a k-means clustering result and entering the step (4.3);
and (4.3) carrying out post-processing on the k-means clustering processing result, respectively carrying out data statistics on different types of data, mapping other types of data into the first type of data according to probability distribution based on the type with the smallest mean value, namely ensuring that the processed result is different in type and has the same probability distribution as the first type of data, and carrying out mapping modification on the data.
Further, the cost function selects the sum of norms from each point to the clustering center, and the norms are used according to the following sequence: 2-norm, 1-norm, infinity-norm, p-norm.
Further, in the step 5, the noise reduction algorithm corrects the multipath data information to obtain positioning calculation pre-information through the state change time vector, and the specific steps are as follows:
(5.1) obtaining the state change time vector and the de-multipath data information, and grouping the de-multipath data information according to time according to the state change time vector;
(5.2) performing a generalized rayleigh quotient calculation for each set of data, the generalized rayleigh quotient calculation being specifically as follows:
(5.2.1) centralizing the de-multipath data information, proceeding to step (5.2.1);
(5.2.2) extracting the multipath-removed data information high frequency part using a high frequency filter, proceeding to step (5.2.3);
(5.2.3) calculating the de-multipath data information S respectively 1 The high-frequency part variance matrix of the multipath-removed data information comprises the following steps of: s is S 2 Step (5.2.4) is entered;
(5.2.4) calculating a generalized rayleigh quotient matrix: s is S 2 -1 S 1 Go to step (5).2.4)；
(5.2.5) calculating generalized Rayleigh maximum condition, and correcting the data result.
Furthermore, the noise reduction algorithm uses the passband cut-off frequency of the high-frequency filter to linearly relate to the average value of the RTT variance under the state of the router return value. Here an adaptation is performed until the high frequency energy ratio is reduced to less than 20% of the total energy, the minimum 1-fold variance average here essentially being characterized by variance as the data state.
Furthermore, the de-multipath effect resolving algorithm and the de-noising algorithm are respectively processed by taking the router number as a division basis.
Further, the data set includes one or more of RTT ranging results provided by a WiFi signal source, RTT variance, a time stamp, and data from an inertial navigation system.
Further, under the condition that WiFi-RTT data acquired by the data acquisition system are affected by multiple reflection surfaces and distance measurement results are affected by all reflection surfaces, selecting a minimum average value as a sight distance result;
further, the WiFi-RTT data acquired by the data acquisition system has different results under different router return value states due to multiple reflection pair ranging results, namely the influence of multipath effect and the influence of noise under different router return value states are considered to be different, and the influence of multipath effect and noise is corrected by distinguishing the data return value states.
As a first aspect of the present invention, it is considered that the WiFi-RTT data collected by the data collection system is affected by the multipath effect, so that the ranging result is layered, and is reflected as double peaks or multiple peaks on the data statistics distribution map, and the data deviation degree is more significant along with the enhancement of the reflection effect;
as a second aspect of the present invention, it is considered that the WiFi-RTT data collected by the data collection system is affected by multiple reflection surfaces on the ranging result by each reflection surface and is independently reflected in the statistical result, there is no correlation, the average value of the ranging result is overall larger due to multiple paths, but the line-of-sight result cannot disappear, and the minimum average value can be selected as the line-of-sight result;
as a third aspect of the present invention, it is considered that the WiFi-RTT data collected by the data collection system has different results under different router return value states due to multiple reflection pair ranging results, that is, it is considered that the influence of multipath effect and the influence of noise under different router return value states are different, and it is necessary to distinguish the data return value states to better correct the influence of multipath effect and noise;
compared with the prior art, the invention has the following beneficial effects:
the WiFi-RTT data property is utilized to obtain multipath effect removal and denoising data, conditions are provided for later semi-system error determination and high-precision positioning, in the multipath effect removal, the influence of the multipath effect on the data is calculated by using cluster analysis, the operand is sharply reduced, and the classification effect is outstanding; meanwhile, in the denoising process, rayleigh quotient is firstly used for calculation, the influence of signal noise on the data quality is considered while the signal characteristics are considered, the denoising effect is excellent, and great help is provided for the subsequent positioning calculation process; the positioning calculation system can obtain the final positioning calculation result with timing, little multipath effect influence, good denoising effect and little system error influence through time interval limitation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a data post-processing system based on WiFi-RTT ranging according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an adaptive de-multipath effect calculation algorithm for a de-multipath effect system according to an embodiment of the present invention;
fig. 3 is a noise reduction algorithm suitable for a noise reduction system according to an embodiment of the present invention.
Reference numerals:
1. a central processing unit; 2. a data acquisition system; 3. a mode screening monitoring system; 4. a multipath effect removal system; 5. a noise reduction system; 6. a semi-system error calibration system; 7. a positioning calculation system; 8. a data set; 9. RTT ranging information; 10. RTT variance; 11. a time stamp; 12. inertial navigation system data; 13. information after data preprocessing; 14. a state change time vector; 15. a de-multipath effect calculation algorithm; 16. multipath data information is removed; 17. a noise reduction algorithm; 18. positioning and resolving pre-information; 19. a half-system error calculation algorithm; 20. a semi-systematic error vector; 21. semi-systematic error time correction.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without the inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention. :
referring to fig. 1-3, a system and a method for reducing noise and removing multipath effects based on WiFi-RTT according to an embodiment of the present invention include a central processing unit 1, where the central processing unit 1 is respectively connected with a data acquisition system 2, a mode screening monitoring system 3, a multipath effect removing system 4, a noise reduction system 5, a half-system error calibration system 6 and a positioning resolving system 7;
the data acquisition system 2 acquires and stores data into a data set 8;
the data set 8 includes, but is not limited to, RTT ranging information 9, RTT variance 10, timestamp 11, and data 12 from an inertial navigation system provided by a WiFi signal source; the data acquisition system 2 transmits the data set 8 to the central processing unit 1 for data preprocessing;
the central processing unit 1 provides the data pre-processed information 13 to the mode screening monitoring system 3;
the mode discrimination monitoring system 3 performs mode discrimination through router information comparison and obtains a state change time vector 14, and then transmits the state change time vector 14 to the central processing unit 1 and the multipath effect removing system 4; the de-multipath effect system 4 is provided with a de-multipath effect calculation algorithm 15;
the de-multipath effect calculation algorithm 15 may correct the data pre-processed information 13 by the state change time vector 14 to obtain de-multipath data information 16; the de-multipath effect system 4 then passes the state change time vector 14 and the de-multipath data information 16 to the central processor 1 and the noise reduction system 5;
the noise reduction system 5 is provided with a noise reduction algorithm 17; the noise reduction algorithm 17 may correct the multipath data information 16 to obtain positioning solution pre-information 18 by using the state change time vector 14, and then transmit the state change time vector 14 and the positioning solution pre-information 18 to the central processing unit 1 and the half-system error calibration system 6;
the half-system error calibration system 6 is provided with a half-system error resolving algorithm 19, and the half-system error resolving algorithm 19 can be used for resolving the positioning resolving pre-information 18 through the state change time vector 14 to obtain a half-system error vector 20;
subsequently, the half-system error calibration system 6 transmits the half-system error vector 20 to the central processing unit 1, and the central processing unit 1 combines the half-system error vector 20 and the state change time vector 14 to obtain a half-system error time correction 21, and then transmits the half-system error time correction 21 and positioning solution pre-information 18 to the positioning solution system 7;
the positioning resolving system 7 can obtain the final positioning resolving result with timing, little multipath effect influence, good denoising effect and little systematic error influence through time interval limitation.
In the process of carrying out WiFi-RTT noise reduction and multipath effect removal algorithm, the WiFi-RTT data acquired by the data acquisition system 2 is considered to be affected by multipath effect to cause layering of a ranging result, the ranging result is reflected to be double peaks or multiple peaks on a data statistical distribution diagram, and the data deviation degree is more remarkable along with the enhancement of the reflection effect;
in the process of carrying out WiFi-RTT noise reduction and multipath effect removal algorithm, the WiFi-RTT data acquired by the data acquisition system 2 are considered to be influenced by each reflecting surface on the ranging result by multiple reflecting surfaces, the influence is independently reflected in the statistical result, the correlation is avoided, the average value of the ranging result is overall larger by multipath, but the sight distance result cannot disappear, and the minimum average value can be selected as the sight distance result;
in the process of carrying out WiFi-RTT noise reduction and multipath effect removal algorithm, the WiFi-RTT data acquired by the data acquisition system 2 are considered to have different results under different router return value states by multiple reflection pairs, namely, the influence of multipath effect and the influence of noise under different router return value states are considered to be different, and the data return value states need to be distinguished to better correct the influence of multipath effect and noise;
further, the cost function selects the sum of norms from each point to the clustering center, and the norms are generally performed according to the following sequence: 2 norms, 1 norms, infinity norms, p norms;
further, the noise reduction algorithm 17 uses a passband cut-off frequency of a high-frequency filter to relate to the average value of the RTT variance 10 in the state of the router return value;
further, the de-multipath effect resolving algorithm 15 and the de-noising algorithm 17 respectively process according to the router number as the division basis;
there is further provided in accordance with an embodiment of the present invention, an multipath-removing effect resolving algorithm 15 adapted for use in the multipath-removing effect system 4, as shown in fig. 2, in a specific application, for the multipath-removing effect resolving algorithm 15, the multipath-removing effect resolving algorithm 15 obtains the state change time vector 14 and the preprocessed data, and the multipath-removing data information 16 is obtained by resolving the state change time vector according to the following steps:
step S101, grouping data according to time according to the state change time vector;
step S102, carrying out k-means clustering processing on each group of data, wherein the k-means clustering processing is specifically as follows:
1) The normalization operation is carried out on the data, and specifically described as follows:
x (i) = (x (i) -min (x (:))/(max (x (:) -min (x (:)))), wherein x represents a data matrix, i represents current time data, and the max (x (:)) and min (x (:)) distributions represent the maximum and minimum values in the x matrix, and step 2) is entered;
2) Defining a maximum classification number: k, entering into step 3);
3) Randomly giving an initial clustering center position, and adjusting the clustering center to be optimal according to a cost function;
4) Checking whether the data statistical information under the optimal clustering center is subjected to normal distribution, and if so, entering a step 5); otherwise, returning to the step 3) to replace the cost function for recalculation;
5) Calculating the cost sum of the optimal clustering centers under different classification numbers, obtaining a cost function distribution function, determining an optimal classification number ko, and entering the step 6);
6) The classification result under the optimal classification number ko is called back as a k-means clustering processing result and the step S103 is entered;
step S103, carrying out post-processing on k-means clustering processing results, respectively carrying out data statistics on different types of data, mapping other types of data into the first type of data according to probability distribution based on the type with the smallest mean value, namely ensuring that the processed result is different in type and has the same probability distribution as the first type of data, and carrying out mapping modification on the data;
according to an embodiment of the present invention, there is further provided a noise reduction algorithm 17 suitable for the noise reduction system 5, as shown in fig. 3, in a specific application, for the multipath effect removing algorithm 15, where the noise reduction algorithm 17 may correct the multipath data information 16 through the state change time vector 14 to obtain positioning solution pre-information 18 according to the following steps:
step S201, the state change time vector and the multipath data information are obtained, and the multipath data information is subjected to grouping processing according to time according to the state change time vector;
step S202, performing generalized rayleigh quotient calculation on each set of data, where the generalized rayleigh quotient calculation is specifically as follows:
1> centralizing the multipath data information, and entering step 2>;
2> extracting the multipath data information removing high frequency part by using a high frequency filter, and entering step 3>;
3> respectively calculating the multipath data information and the high frequency part variance matrix of the multipath data information: s1 and S2, enter step 4>;
4>calculating a generalized Rayleigh quotient matrix: s is S 2 -1 S1, entering step 5>；
5> calculating a generalized Rayleigh quotient maximum condition, and correcting a data result;
although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The system based on WiFi-RTT noise reduction and multipath removal effect is characterized by comprising a central processing unit, wherein the central processing unit is respectively connected with a data acquisition system, a mode screening monitoring system, a multipath removal effect system, a noise reduction system, a semi-system error calibration system and a positioning resolving system,
the data acquisition system acquires and stores data into a data set;
the data acquisition system transmits the data set to the central processing unit for data preprocessing, and the central processing unit provides the information after the data preprocessing for the mode screening monitoring system;
the mode discrimination monitoring system performs mode discrimination through router information comparison, obtains a state change time vector, and then transmits the state change time vector to the central processing unit and the multipath effect removing system;
the de-multipath effect system is used for correcting the data preprocessed information through the state change time vector by using a de-multipath effect resolving algorithm to obtain de-multipath data information; the de-multipath effect system transmits the state change time vector and the de-multipath data information to the central processing unit and the noise reduction system;
the noise reduction system is used for correcting the multipath data information to obtain positioning calculation pre-information through the state change time vector by using a noise reduction algorithm, and then transmitting the state change time vector and the positioning calculation pre-information to the central processing unit and the half-system error calibration system;
the half-system error calibration system is used for calculating the positioning calculation pre-information through the state change time vector by utilizing a half-system error calculation algorithm to obtain a half-system error vector; the half-system error calibration system transmits the half-system error vector to the central processing unit;
the central processing unit combines the half-system error vector and the state change time vector to obtain a half-system error time correction, and then transmits the half-system error time correction and positioning calculation pre-information to the positioning calculation system;
the positioning calculation system is used for obtaining a final positioning calculation result through time interval limitation.
2. A method of WiFi-RTT-based noise reduction and multipath removal using the system of claim 1, comprising the steps of:
step 1, a data acquisition system acquires and stores data into a data set;
step 2, the data acquisition system transmits the data set to the central processing unit for data preprocessing, and the central processing unit provides the information after the data preprocessing for the mode screening monitoring system;
step 3, the mode discrimination monitoring system performs mode discrimination through router information comparison and obtains a state change time vector, and then the state change time vector is transmitted to the central processing unit and the multipath effect removing system;
step 4, the multipath effect removing system corrects the data preprocessing information by utilizing a multipath effect resolving algorithm through the state change time vector to obtain multipath data removing information; the de-multipath effect system transmits the state change time vector and the de-multipath data information to the central processing unit and the noise reduction system;
step 5, the noise reduction system corrects the multipath data information to obtain positioning calculation pre-information through the state change time vector by using a noise reduction algorithm, and then the state change time vector and the positioning calculation pre-information are transmitted to the central processing unit and the half-system error calibration system;
step 6, the half-system error calibration system utilizes a half-system error calculation algorithm to calculate the positioning calculation pre-information through the state change time vector to obtain a half-system error vector; the half-system error calibration system transmits the half-system error vector to the central processing unit;
step 7, the central processing unit combines the half-system error vector and the state change time vector to obtain a half-system error time correction, and then transmits the half-system error time correction and positioning calculation pre-information to the positioning calculation system;
and 8, the positioning calculation system obtains a final positioning calculation result through time interval limitation.
3. The method of claim 2, wherein in step 4, the de-multipath effect calculation algorithm corrects the preprocessed information by the state change time vector to obtain de-multipath data information, and the specific steps are as follows:
(4.1) grouping data according to time according to the state change time vector;
(4.2) each set of data was subjected to a k-means clustering process, which was specifically described as follows:
(4.2.1) normalizing the data, specifically described as: x (i) = (x (i) -min (x ()))/(max (x (:) -min (x ()))) wherein x represents a data matrix, i represents current time data, max (x ()) and min (x (:)) represent maximum and minimum values in the x matrix, respectively, and step (4.2.2) is entered;
(4.2.2) defining a maximum number of classifications: k, and enter step (4.2.3);
(4.2.3) randomly giving an initial cluster center position, and adjusting the cluster center to be optimal according to the cost function;
(4.2.4) checking whether the data statistical information under the optimal clustering center is compliant with normal distribution, and if so, entering a step (4.2.5); otherwise, returning to the step (4.2.3) to replace the cost function for recalculation;
(4.2.5) calculating the sum of the cost of the optimal clustering centers under different classification numbers, obtaining a cost function distribution function, determining an optimal classification number ko, and entering a step (4.2.6);
(4.2.6) recalling the classification result under the optimal classification number ko as a k-means clustering result and entering the step (4.3);
and (4.3) carrying out post-processing on the k-means clustering processing result, respectively carrying out data statistics on different types of data, mapping other types of data into the first type of data according to probability distribution based on the type with the smallest mean value, namely ensuring that the processed result is different in type and has the same probability distribution as the first type of data, and carrying out mapping modification on the data.
4. A method of denoising and multipath removal based on WiFi-RTT according to claim 3, wherein the cost function is selected from the sum of norms from each point to a cluster center, and the norms are used according to the following sequence: 2-norm, 1-norm, infinity-norm, p-norm.
5. The method of claim 2, wherein in step 5, the denoising algorithm corrects the multipath-removed data information by the state change time vector to obtain positioning solution pre-information, and the specific steps are as follows:
(5.1) obtaining the state change time vector and the de-multipath data information, and grouping the de-multipath data information according to time according to the state change time vector;
(5.2) performing a generalized rayleigh quotient calculation for each set of data, the generalized rayleigh quotient calculation being specifically as follows:
(5.2.1) centralizing the de-multipath data information, proceeding to step (5.2.1);
(5.2.2) extracting the multipath-removed data information high frequency part using a high frequency filter, proceeding to step (5.2.3);
(5.2.3) calculating the de-multipath data information S respectively 1 The high-frequency part variance matrix of the multipath-removed data information comprises the following steps of: s is S 2 Step (5.2.4) is entered;
(5.2.4) calculating a generalized rayleigh quotient matrix: s is S 2 -1 S 1 Step (5.2.4) is entered;
(5.2.5) calculating generalized Rayleigh maximum condition, and correcting the data result.
6. The method of claim 5, wherein the noise reduction algorithm uses a high frequency filter band-pass cut-off frequency in linear relation to the mean value of RTT variance in the router return state.
7. The method of claim 2, wherein the de-multipath effect calculation algorithm and the de-noising algorithm are each processed separately based on a router number division basis.
8. The method of claim 2, wherein the data set comprises one or more of RTT ranging results provided by a WiFi signal source, RTT variance, time stamp, and data from an inertial navigation system.
9. The method of claim 2, wherein when the WiFi-RTT data collected by the data collection system is affected by multiple reflection surfaces and the ranging result is affected by each reflection surface, selecting a minimum average value as the line-of-sight result.
10. The method of claim 2, wherein the WiFi-RTT data collected by the data collection system is subjected to multiple reflection pair ranging results and has different results in different router return value states, namely, the influence of the multipath effect and the influence of the noise in different router return value states are considered to be different, and the influence of the multipath effect and the noise is corrected by distinguishing the data return value states.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105765400A (en) * | 2013-11-27 | 2016-07-13 | 高通股份有限公司 | System and method for deploying an RTT-based indoor positioning system |
| CN112399350A (en) * | 2020-09-30 | 2021-02-23 | 中国科学院空天信息创新研究院 | WiFi-RTT (wireless fidelity-round trip time) -based ranging and positioning system and method suitable for realizing indoor positioning of mobile phone |
| CN112929822A (en) * | 2021-02-08 | 2021-06-08 | 中国科学院空天信息创新研究院 | Return value state monitoring system based on WiFi-RTT ranging |
| CN112954589A (en) * | 2021-02-08 | 2021-06-11 | 中国科学院空天信息创新研究院 | Monitoring and positioning system based on WIFI-RTT (wireless fidelity-round-trip time) ranging |
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| US9057771B2 (en) * | 2011-04-25 | 2015-06-16 | Disney Enterprises, Inc. | Carrier sense-based ranging |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105765400A (en) * | 2013-11-27 | 2016-07-13 | 高通股份有限公司 | System and method for deploying an RTT-based indoor positioning system |
| CN112399350A (en) * | 2020-09-30 | 2021-02-23 | 中国科学院空天信息创新研究院 | WiFi-RTT (wireless fidelity-round trip time) -based ranging and positioning system and method suitable for realizing indoor positioning of mobile phone |
| CN112929822A (en) * | 2021-02-08 | 2021-06-08 | 中国科学院空天信息创新研究院 | Return value state monitoring system based on WiFi-RTT ranging |
| CN112954589A (en) * | 2021-02-08 | 2021-06-11 | 中国科学院空天信息创新研究院 | Monitoring and positioning system based on WIFI-RTT (wireless fidelity-round-trip time) ranging |
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