CN112822625A - FM and DTMB signal fingerprint positioning system based on multimodal Gaussian distribution model - Google Patents
FM and DTMB signal fingerprint positioning system based on multimodal Gaussian distribution model Download PDFInfo
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- CN112822625A CN112822625A CN201911137846.9A CN201911137846A CN112822625A CN 112822625 A CN112822625 A CN 112822625A CN 201911137846 A CN201911137846 A CN 201911137846A CN 112822625 A CN112822625 A CN 112822625A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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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
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
- G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention discloses an FM and DTMB signal fingerprint positioning system based on a multimodal Gaussian distribution model. The method comprises the following steps: sampling points are selected according to an indoor environment, sampling frequencies of FM and DTMB signals are respectively selected as reference frequencies, intensity information of the signals on the reference frequencies of the sampling points is recorded, a multimodal Gaussian distribution model is adopted to carry out real fitting on the signal intensity, and then a probabilistic positioning matching algorithm is utilized to realize indoor high-precision positioning. The invention uses FM and DTMB signals with stable signal intensity as positioning signals, and has the advantages of large coverage area, high stability, low cost, high precision and the like; the method adopts the multimodal Gaussian distribution model to carry out real fitting on the signals, and compared with the unimodal Gaussian distribution fitting, the positioning precision is more accurate; the probabilistic matching positioning algorithm provided by the invention can effectively estimate the position of the point to be positioned, and is simple, convenient and easy to implement and high in accuracy.
Description
Technical Field
The invention relates to a method for realizing indoor high-precision positioning by utilizing a multimodal Gaussian distribution fitting model of FM and DTMB signal intensity to perform position matching.
Background
With the development of the positioning and navigation technology, the positioning technology based on the systems such as the GPS, the Beidou and the like can realize high-precision positioning in the outdoor environment, and meet the position service requirements of people. However, under the complicated and changeable indoor situation, the receiver is difficult to acquire accurate satellite signals, and the ideal positioning effect is difficult to achieve only by positioning through the satellite in addition to the influence of non-line-of-sight errors and the like. Compared with outdoor positioning, indoor positioning has more complicated and changeable environmental conditions and higher precision requirement.
Currently, common indoor positioning technical methods include radio frequency tag positioning, ultrasonic positioning, infrared positioning, bluetooth positioning, ultra-wideband positioning, Wi-Fi positioning and the like, and also include technologies for indoor positioning by using light, geomagnetism, images and the like. The Wi-Fi positioning technology is widely applied, but the positioning effect is not ideal in a severe environment. The Wi-Fi signal frequency is 2.4GHz, is easily interfered by other signals with the same frequency, is easily influenced by the surrounding environment, has strong time-varying property and is not stable enough, and has great influence on the positioning precision of the system. On the other hand, when positioning is needed, the Wi-Fi is required to be opened to be in a networking state, so that the dependence of a positioning system on Wi-Fi signals is high, and the wireless access points need to be densely deployed, so that the positioning cost is improved invisibly. Therefore, the positioning technology is not completely mature, and a plurality of problems to be solved are still needed.
The invention provides an indoor positioning method based on FM (Frequency Modulation) and DTMB (Digital Television Terrestrial Broadcasting) signals on the basis, wherein the FM signals and the DTMB signals have stable strength, wide coverage area and strong anti-interference performance, and are relatively ideal positioning signals. Meanwhile, experimental demonstration finds that the multimodal Gaussian distribution fitting model has higher precision than a unimodal Gaussian distribution model, and the increase of DTMB signals can also improve the positioning precision.
Disclosure of Invention
The invention provides an FM and DTMB signal fingerprint positioning system based on a multimodal Gaussian distribution model, which overcomes some limitations of positioning by using Wi-Fi signals in the prior art, and realizes indoor high-precision positioning by using a multimodal Gaussian fitting model to perform probability position matching.
The technical scheme for realizing the invention is as follows:
the method determines the position of an indoor locating point to be located by receiving FM signals and DTMB signal strength information, and comprises the following specific steps:
(1) reasonably selecting sampling points according to indoor conditions, and selecting different frequencies of FM signals as reference frequencies; in the joint positioning, 675MHz DTMB signals are added behind FM signals;
(2) receiving two signals at each sampling point by using FM and DTMB signal receivers, and recording signal intensity information acquired on each reference frequency;
(3) on each sampling point, carrying out multimodal Gaussian model fitting on the strength distribution of FM and DTMB signals collected under each reference frequency to construct an off-line database, wherein the database also comprises the position information of each sampling point;
(4) and receiving FM and DTMB signals at the to-be-positioned point by the same equipment, and performing probability matching on the signal strength measured by the to-be-positioned point and a multimodal Gaussian fitting model in an offline database through a probabilistic positioning matching algorithm to obtain a final positioning result.
Furthermore, the FM signals are uniformly transmitted by the appointed transmitting station, and the FM signals which can be received in the same area all come from the same signal transmitting tower. The DTMB signal has high frequency and short wavelength, and can make up the defect that the FM signal cannot identify small-sized obstacles.
Further, the sampling points in step (1) are selected according to the indoor actual environment, and the reference frequency is a frequency with large difference in the receiving range. It was finally determined that 24 FM frequencies of signal were used, ranging from 87.8MHz to 106.8 MHz.
Further, when an off-line database is constructed, a multimodal Gaussian distribution model is adopted to carry out real fitting on the signal intensity, and a corresponding multimodal fitting model is established for each frequency on each reference point. The information contained in the offline database is: sampling point position coordinates, sampling frequency and a signal fitting model on each sampling point.
Further, the probabilistic matching algorithm is a location estimation algorithm that provides a fitting model that closely approximates the distribution of actual signal strengths. And obtaining the position of the experimental result of the point to be positioned according to the corresponding relation between the probability and the position by utilizing the probability characteristic of multimodal Gaussian distribution during positioning. In an actual positioning environment, most probability distributions of signal intensities are approximate to gaussian distributions, so that a multimodal gaussian fitting model is selected to fit the signal intensity distributions corresponding to the sampling points. At this time
Wherein P (rss)p|LOCr) Is LOCrPunctual rsspProbability of intensity, Q being the actual number of samples, LOCrRSS at a locationpqObey N (rss)prn，σn) Normal distribution of (a) as a peakn. When the to-be-positioned point is positioned, probability matching is carried out on the FM and DTMB signal intensity of the to-be-positioned point and the multimodal Gaussian distribution model to obtain the probability that the to-be-positioned point is positioned at the sampling point, and specific position information of the to-be-positioned point can be obtained according to the position of the sampling point.
Furthermore, the frequencies on each sampling point are independent, so that all the frequencies are not influenced by each other, and probability information on the same sampling point can be obtained through multiplication.
Furthermore, when a multimodal Gaussian distribution model is adopted for signal fitting, the positioning accuracy of the system is more accurate than that of unimodal Gaussian distribution, is improved by about 0.8 m, and can be used for realizing high-accuracy positioning.
Further, the number of peaks of the multi-peak gaussian distribution is determined by fitting parameters. The larger the number of peaks, the closer the fitted gaussian model is to the distribution of the real signal, and the more accurate the positioning result is. However, the higher the complexity of the fitting model is, the workload in the offline training stage will be increased to some extent, and the complexity of the system will also increase, so the fitting manner should be balanced between the positioning accuracy and the workload, so as to select the most needed method.
Compared with the prior art, the indoor positioning method provided by the invention has the following advantages:
(1) the multimodal Gaussian distribution fitting method adopted by the invention has higher positioning precision than a unimodal Gaussian model fitting system, and the method is used for carrying out real fitting on signals, and the fitting degree can reach 98%.
(2) When an off-line database of FM and DTMB signals is constructed, the influence of instability of obstacles and signals on system positioning accuracy is overcome through selection of different frequencies of the FM signals, the defect that small-size obstacles cannot be identified by the FM signals is overcome through addition of the DTMB signals, the difference of signal strength is increased, and the positioning performance of the system is improved.
(3) The invention adopts a probabilistic positioning matching method to determine the position of the point to be positioned, the two information mechanisms are different, the errors of the two information mechanisms are mutually independent, the two information mechanisms are combined, the diversity of the positioning information can be effectively increased, partial errors can be effectively counteracted, and the stability of the positioning method is improved.
(4) The method is suitable for any environment capable of receiving FM and DTMB signals simultaneously, and compared with the prior positioning technology, the method has the advantages of large coverage area, high stability, low cost, high precision and the like.
Drawings
FIG. 1 is a schematic diagram of an FM and DTMB signal fingerprint location system based on a multimodal Gaussian distribution model according to the present invention;
FIG. 2 is an exemplary graph of a multimodal Gaussian model fit;
FIG. 3 is a comparison graph of the cumulative probability curves of single and multi-peak Gaussian model positioning errors.
Detailed Description
The method of the present invention is described in detail with reference to the accompanying drawings and examples.
As shown in fig. 1, a schematic diagram of a FM and DTMB signal fingerprint positioning system of a multimodal gaussian distribution model specifically includes the following processes:
(1) reasonably selecting sampling points according to indoor conditions, and selecting different frequencies of FM signals as reference frequencies; for joint positioning, 675MHz DTMB signal is added after the FM signal.
For a determined indoor positioning environment, considering the specificity of signal intensity of each point in space and the actual positioning requirement, performing two-dimensional modeling on the space, wherein the spacing distance is determined according to the indoor structure and the specific positioning requirement. The central position of the grid is used as a sampling point, a coordinate system is established, two-dimensional position coordinates (x, y) of the sampling point are obtained, the serial number of the sampling point corresponds to the position one by one, and then the r-th sampling point is expressed as: pr(xr，yr) And r is 1, 2, and N is the total number of sampling points.
The FM signals all contain a plurality of frequencies, the signal intensity on each frequency is not suitable for fitting a multimodal gaussian distribution as a fingerprint, and if the intensity information of all frequencies is put into a database, the amount of calculation is increased, the matching of fingerprint information in the later period is also affected, and the accuracy of a positioning system is reduced. The present invention selects, as a reference frequency, a frequency having a large difference in the reception range from among the frequencies of the FM signal.
In an indoor environment, the size of the obstacle is typically 2 to 3 meters. When a signal with different wavelengths encounters an obstacle, its propagation path is different. In the invention, the frequency range of the FM signal is from 87.6MHz to 108MHz, the wavelength range is from 2.8m to 3.5m, the electromagnetic wave with the length can not be refracted when meeting an obstacle less than 3 m, the signal loss is small, the signal intensity change is not obvious enough, and the accurate positioning is not facilitated. The frequency of the DTMB signal is 675MHz, the wavelength of the DTMB signal is 0.44m, the DTMB signal is refracted when meeting an obstacle larger than 1 m, the signal loss is large, and the signal intensity is large in change. The present invention combines the joint localization of FM and DTMB signals. The selected reference frequency is denoted as FiI-1, 2, …, P, where P-25.
(2) And receiving two signals at each sampling point by using an FM receiving antenna and a DTMB signal receiver, and recording the signal intensity information collected on each reference frequency.
When the sampling points receive FM and DTMB signals, multiple measurements are carried out, and the average value is taken. The measured strength information of different frequencies of FM is represented as a vector, each term of which represents a signal strength mean of one frequency. Selecting N sampling points in the area, measuring the received signal intensity of P reference frequencies on each point, and expressing the signal intensity of the r-th point as follows:
wherein
Average of Q measurements for the ith frequency, i.e.
The position P of the sampling point obtained in the step (1)r(xr，yr) And the signal strength RSS obtained in this steprTogether, a fingerprint database is constructed.
(3) And performing multimodal Gaussian model fitting on the strength distribution of the FM and DTMB signals collected at each reference frequency at each sampling point.
The measured signal at each frequency at each sampling point is different for RSSrCarrying out multimodal Gaussian distribution fitting, and establishing a corresponding fitting model, wherein the information contained in the off-line database comprises: sampling point position coordinates, sampling frequency and a signal fitting model on each sampling point. Under the multimodal gaussian model, the probability density distribution function of signal strength is:
wherein, munAnd σnMean and standard deviation of multimodal variable of fraction n, anIs the weight of the nth part multimodal variable. As shown in fig. 2, it is a two-peak gaussian curve, i.e. n is 2.
(4) And receiving FM and DTMB signals at the to-be-positioned point by the same equipment, and performing probability matching on the signal strength measured by the to-be-positioned point and a multimodal Gaussian fitting model in an offline database through a probabilistic positioning matching algorithm to obtain a final positioning result.
A probabilistic matching algorithm is a position estimation algorithm that provides a fitted model that closely approximates the distribution of actual signal strengths. And obtaining the position of the experimental result of the point to be positioned according to the corresponding relation between the probability and the position by utilizing the probability characteristic of multimodal Gaussian distribution during positioning. The specific implementation mode is as follows: measured signal strength rs, localized at LOCrThe probability of (c) can be obtained by the following formula:
where P (rss) is the probability that the measurement is rss, P (LOC)r) For points to be located at LOCrProbability of (2), at first fix, P (LOC)r) Can be regarded as 1, with an initial probability, P (LOC)r) Each calculation can be iterated according to the last value, which is regarded as a prior probability. P (rss | LOC)r) Is LOCrThe probability of the rss intensity of a point is obtained, and because the frequencies of each sampling point are independent, all the frequencies are not influenced mutually, and probability information on the same sampling point can be obtained through multiplication. Namely:
in an actual positioning environment, a multimodal Gaussian fitting model is selected to fit the signal intensity distribution corresponding to each sampling point. At this time
Wherein P (rss)p|LOCr) Is LOCrPunctual rsspProbability of intensity, Q being the actual number of samples, LOCrRSS at a locationpqObey N (rss)prn，σn) Normal distribution of (a) as a peakn. The determination of n, namely the determination of the number of the multi-peak peaks, is determined through fitting parameters, and the more the number of the peaks is, the closer the fitted Gaussian model is to the distribution of the real signals, and the more accurate the positioning result is.
When a to-be-positioned point is positioned, the signal intensity of FM and DTMB is measured at the to-be-positioned point, the signal intensity is matched with a multimodal Gaussian distribution model in a database, the probability that the to-be-positioned point is positioned at each sampling point is obtained, the specific position information of the to-be-positioned point can be obtained according to the positions of the sampling points, and then information such as system positioning accuracy is finally obtained.
Compared with unimodal Gaussian distribution, the positioning accuracy of the system is improved by about 0.8 meter by the multimodal Gaussian model matching method, and as shown in FIG. 3, when FM plus DTMB signals are jointly positioned, a positioning error accumulation probability curve of the system is obtained under the fitting of the unimodal Gaussian model and the multimodal Gaussian model. When the distribution model adopts a multi-peak Gaussian distribution model, the average positioning error reaches 1.2 meters, the accumulated probability error reaches 3.3 meters, and the positioning precision is more accurate than that of a single-peak Gaussian model. The new fitting mode is closer to the real signal distribution condition, has low cost, has better resistance to the fluctuation of the signal in the indoor environment, is suitable for the indoor environment with complex signals, and can be used for indoor precise positioning.
The invention is further described and not intended to be limited to the practice of this patent, but rather to include equivalent practice within the scope of the claims.
Claims (5)
1. An FM and DTMB signal fingerprint positioning system based on a multimodal Gaussian distribution model is characterized in that the position of a to-be-positioned point in an indoor environment is determined by receiving strength information of an FM signal and a DTMB signal, and the method comprises the following steps:
1) reasonably selecting sampling points according to indoor conditions, and selecting different frequencies of FM signals as reference frequencies; in the joint positioning, 675MHz DTMB signals are added behind FM signals;
2) receiving two signals at each sampling point by using an FM receiving antenna and a DTMB signal receiver, and recording signal intensity information acquired on each reference frequency;
3) on each sampling point, carrying out multimodal Gaussian model fitting on the strength distribution of FM and DTMB signals collected under each reference frequency to construct an off-line database, wherein the database also comprises the position information of each sampling point;
4) and receiving FM and DTMB signals at the to-be-positioned point by the same equipment, and performing probability matching on the signal strength measured by the to-be-positioned point and a multimodal Gaussian fitting model in an offline database through a probabilistic positioning matching algorithm to obtain a final positioning result.
2. The system of claim 1, wherein the sampling points are selected based on an indoor environment, and the reference frequency is selected from frequencies with a large difference in reception range.
3. The multimodal gaussian distribution model based FM and DTMB signal fingerprint positioning system of claim 1 wherein during off-line database construction, the multimodal gaussian distribution model is used to truly fit the signal strength, and a corresponding multimodal fit model is built for each frequency at each reference point.
4. The system of claim 1, wherein the probabilistic matching algorithm is a position estimation algorithm that provides a close approximation of the distribution of actual signal strengths. And obtaining the position of the experimental result of the point to be positioned according to the corresponding relation between the probability and the position by utilizing the probability characteristic of Gaussian distribution during positioning.
5. The multimodal gaussian distribution model based FM and DTMB signal fingerprint positioning system of claim 1 wherein the multimodal gaussian distribution fits the signal with a higher accuracy than if only a unimodal gaussian distribution model were used.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105916202A (en) * | 2016-06-20 | 2016-08-31 | 天津大学 | Probabilistic WiFi indoor positioning fingerprint database construction method |
| CN106888504A (en) * | 2015-12-11 | 2017-06-23 | 南开大学 | Indoor location fingerprint positioning method based on FM Yu DTMB signals |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106888504A (en) * | 2015-12-11 | 2017-06-23 | 南开大学 | Indoor location fingerprint positioning method based on FM Yu DTMB signals |
| CN105916202A (en) * | 2016-06-20 | 2016-08-31 | 天津大学 | Probabilistic WiFi indoor positioning fingerprint database construction method |
Non-Patent Citations (2)
| Title |
|---|
| 吴虹等: "《Indoor localization using FM radio and DTMB signals》", 《RADIO SCIENCE》 * |
| 陈淼: "《基于多高斯混合模型的WLAN室内定位***》", 《华中科技大学学报》 * |
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Application publication date: 20210518 |