Microcell electromagnetic space automatic measurement system and rapid localization modeling method

Abstract

The invention discloses a micro-cell electromagnetic space automatic measurement system and a localization modeling method. The system comprises a data interaction control center, a frequency spectrum measurement device and a geographic information positioning device, wherein the frequency spectrum measurement device and the geographic information positioning device are electrically connected with the data interaction control center. The data interaction control center is a computer, the frequency spectrum measurement device is a frequency spectrum analyzer, and the geographic information positioning device is a real-time differential positioning device. The measuring system disclosed by the invention realizes joint measurement of electromagnetic spectrum and geographical location information, can simultaneously record spectrum data and centimeter-level positioning information (delay is less than 0.01 s), and can be suitable for various flexible scenes such as fixed station monitoring, mobile station road test and the like.

Description

Technical Field

[0001] The invention belongs to the research field of radio wave propagation effect in microcells, and particularly relates to an electromagnetic space automatic measurement system in the field and a rapid localization modeling method based on measured data of the measurement system. Background Art

[0002] Liu Liming disclosed a comprehensive measurement technology of fixed station geographic location information and spectrum data based on distributed spectrum monitoring probes in "Distributed Spectrum Monitoring Black Broadcast Detection System". The method is to mark the installation position of the spectrum probe and manually record GPS data. Its shortcomings are also very obvious. It can only measure spectrum data at a fixed point, lacks automated measurement of geographic location information, has poor flexibility, and cannot cope with electromagnetic space measurement scenarios that require traversing the entire area; Liu Qi et al. disclosed an automated electromagnetic environment monitoring software in "Automated Electromagnetic Environment Monitoring Software Development and Implementation". By building test antennas, signal analyzers, RF receiving modules, gimbals, power modules, RF cables and other equipment, the software is based on the Virtual Instrument Software Architecture (VISA) with a built-in multi-device automated collaborative control algorithm, which realizes electromagnetic environment measurements of different bandwidths, different time periods, different directions, and different polarizations within the range of 100MHz-13.6GHz. However, due to too many integrated devices, although the measurable parameters are rich, it is not competent for scenarios that require frequent movement and can only be used for fixed station monitoring. Tang Lei et al. disclosed an automated electromagnetic space measurement solution for smart factories in "Investigation of the Effect of Moving Forklift on Data Transmission of IEEE 802.15.4 Wireless Sensor Radio". The authors deployed spectrum detection sensors and geographic location information sensors on mobile forklifts. The sensors realized real-time feedback of measured data through the IEEE 802.15.4 communication protocol and built an electromagnetic space prediction model based on the measured data. The advantages of this method are low cost and easy transplantation, which meets the monitoring needs of mobile stations. The disadvantages are, on the one hand, measurement errors caused by the sensor itself due to many reasons such as volume and heat dissipation, and on the other hand, independent measurement of geographic location information and spectrum, resulting in inconsistent timestamps and accuracy errors, making the model built based on measured data less accurate. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the disadvantage that the geographic location information and electromagnetic spectrum information in the prior art cannot be automatically measured simultaneously in a mobile scenario, resulting in large localization model errors, and to provide a microcell electromagnetic space automatic measurement system and a rapid localization modeling method.

[0004] The present invention adopts the following technical solution:

[0005] The invention discloses an automatic measurement system for electromagnetic space in a micro cell, the improvement of which lies in that it comprises a data interaction control center and a spectrum measurement device and a geographic information positioning device electrically connected thereto.

[0006] Furthermore, the data interaction control center is a computer, the spectrum measurement device is a spectrum analyzer, and the geographic information positioning device is a real-time differential positioning device.

[0007] A fast localized modeling method is provided, based on the measured data of the above measurement system, and its improvements are:

[0008] (1) Microcell electromagnetic space prediction model based on ITU model:

[0009] (11) Specific site model for sight distance:

[0010] For electromagnetic wave propagation in the UHF frequency range, the basic transmission loss characteristics are represented by two slopes and one inflection point. The specific formula is as follows:

[0011]

[0012] In the above formula, d is the distance between the transmitting and receiving points, L c is the fading margin, α1 and α2 are the attenuation coefficients, and R bp is the breakpoint distance, calculated as:

[0013]

[0014] In the above formula, λ is the wavelength, h1 represents the height of the transmitting antenna above the ground, and h2 represents the height of the receiving antenna above the ground;

[0015] L bp is the basic transmission loss value at the inflection point, calculated by the following formula:

[0016]

[0017] In the UHF band, when the path length is up to 1 km, R bp Calculate as follows:

[0018]

[0019] In the above formula, h sis the effective road height caused by vehicles on the road;

[0020] When h1>h s ,h2>h s When L bp Given by:

[0021]

[0022] The approximate lower bound is given by:

[0023]

[0024] In the above formula, R s It is the statistical experience value;

[0025] An approximate upper bound is given by:

[0026]

[0027] L s Defined as:

[0028]

[0029] The median value is given by:

[0030]

[0031] (12) Non-line-of-sight specific site model:

[0032] w1 represents the street width at the transmitting point, x1 represents the distance from the transmitting point to the intersection, and x2 represents the distance from the receiving point to the intersection. The overall basic transmission loss outside the corner area is calculated using the following formula:

[0033] L NLoS2 =L LoS +L c +L att

[0034] In the above formula, L LOS Indicates the path loss in the case of line-of-sight for transmission and reception;

[0035] For L c , when w1 / 2+1<x2≤w1 / 2+1+d corner hour:

[0036]

[0037] In the above formula, L corner and d corner All are statistical constants;

[0038] When x2>w1 / 2+1+dcorner hour:

[0039] L c =L corner

[0040] For L att , when x2>w1 / 2+1+d corner hour:

[0041]

[0042] When the four corners of the intersection are wedge-shaped buildings, β = 6;

[0043] When the four corners of the intersection are inclined buildings, β is calculated according to the following formula:

[0044] β=4.2+(1.4log 10 f-7.8)(0.8log 10 x1-1.0)

[0045] In the above formula, f is the frequency;

[0046] When x2≤w1 / 2+1+d corner When L att =0;

[0047] (2) Rapid localization method of electromagnetic space model parameters:

[0048] Assuming that the set of parameters to be optimized is x, the model difference function after the measured results and the optimized parameters are introduced is f(x), then the optimized parameters are expressed in mathematical form as:

[0049]

[0050] The iterative method is used to solve the problem. The specific steps are as follows:

[0051] (21) Given an initial value of x;

[0052] (22) For the kth iteration, find a step Δx k , so that Reach a minimum value;

[0053] (23) If Δx k If x is small enough, stop; otherwise, let x k+1 =x k +Δx k , returns (22).

[0054] Furthermore, in step (23), the Levenberg-Marquardt method is used to find a suitable Δx k , the specific steps are as follows:

[0055] (31) Determine the scope of the trust region:

[0056] In the above formula, J(x) represents the Jacobian matrix;

[0057] (32) For the kth iteration, solve:

[0058]

[0059] In the above formula, || || 2 represents the second-order norm, D represents the coefficient matrix, and μ represents the trust radius;

[0060] (33) Solve for ρ, if Then μ=2μ, if Then μ = 0.5μ, and μ remains unchanged in other cases;

[0061] (34) If ρ is greater than a certain threshold, the approximation is considered feasible, and x k+1 =x k +Δx k ;

[0062] (35) Determine whether the algorithm converges. If not, return to (32), otherwise terminate.

[0063] The beneficial effects of the present invention are:

[0064] The measurement system disclosed in the present invention realizes the joint measurement of electromagnetic spectrum and geographic location information, can simultaneously record spectrum data and centimeter-level positioning information (delay less than 0.01s), and can be applied to various flexible scenarios such as fixed station monitoring and mobile station road testing. The low error of recorded data will also provide a more scientific and accurate reference basis for the localization of path loss models, and can calibrate more accurate electromagnetic propagation statistical model parameters and perform more accurate electromagnetic propagation coverage prediction. It has broad application prospects in the fields of communication quality assessment and communication base station planning.

[0065] The rapid localized statistical model building method disclosed in the present invention provides assistance for the timeliness of model correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a schematic diagram of the principle of carrier phase ranging;

[0067] Figure 2 It is a schematic diagram of the double difference positioning model;

[0068] Figure 3 It is a schematic diagram of the composition of the measurement system disclosed in the present invention;

[0069] Figure 4 It is a schematic diagram of a typical propagation situation in a microcell in a dense urban area;

[0070] Figure 5 It is a schematic diagram of the typical propagation situation of the NLOS path in a microcell;

[0071] Figure 6 It is a satellite map of the experimental site;

[0072] Figure 7 It is a schematic diagram of the automated measurement system built;

[0073] Figure 8 It is a measured route planning map;

[0074] Fig. 9 This is the measured data at 2GHz;

[0075] Fig.10 This is the measured data at 4GHz;

[0076] Fig.11 This is the measured data at 6GHz;

[0077] Fig.12 This is the measured data at 8GHz;

[0078] Fig.13 This is the measured data at 10GHz;

[0079] Fig.14 This is the measured data at 12GHz;

[0080] Fig.15 This is the measured data at 14GHz;

[0081] Fig.16 This is the measured data at 16GHz;

[0082] Fig.17 This is the measured data at 18GHz;

[0083] Fig.18 This is a comparison chart of 2GHz localization model simulation and measured data;

[0084] Fig.19 This is a comparison chart of 4GHz localization model simulation and measured data;

[0085] Fig. 20 This is a comparison chart of 6GHz localization model simulation and measured data;

[0086] Fig.21 This is a comparison chart of 8GHz localization model simulation and measured data;

[0087] Fig. 22 This is a comparison chart of 10GHz localization model simulation and measured data;

[0088] Fig.23This is a comparison chart of 12GHz localization model simulation and measured data;

[0089] Fig.24 This is a comparison chart of 14GHz localization model simulation and measured data;

[0090] Fig.25 This is a comparison chart of 16GHz localization model simulation and measured data;

[0091] Fig.26 This is a comparison chart of 18GHz localization model simulation and measured data. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0093] First, the present invention discloses a microcell electromagnetic space automatic measurement system, which can synchronously and jointly measure portable geographic information and spectrum information. It includes a data interaction control center and a spectrum measurement device and a geographic information positioning device electrically connected thereto. The data interaction control center is a computer, the spectrum measurement device is a spectrum analyzer, and the geographic information positioning device is a real-time differential positioning device.

[0094] ① Spectrum measurement equipment: Spectrum measurement is performed by the professional spectrum analyzer Ceyear4042D provided by China Electronics Cosine Instrument. It supports multiple measurement function modes: spectrum analysis, interference analysis (waterfall diagram, RSSI), demodulation analysis (AM / FM / PM), channel scanning, high-precision power measurement (support external continuous wave and peak power probes), field strength measurement, etc.; resolution bandwidth can reach 1Hz; wide-band coverage range: 9kHz-67GHz; short scanning time: 1GHz sweep width, the fastest scanning time is less than 20ms. It can realize data interaction with the computer through the LAN port, and has built-in standard commands for programmable instruments (Standard Commands for Programmable Instruments, abbreviated as: SCPI), which is the basis for realizing joint synchronous measurement. Its working process includes: the signal input to the spectrum analyzer first passes through the attenuator, attenuates to the appropriate range, mixes the carrier with the local oscillator, intermediate frequency amplification, intermediate frequency filtering, detection, video filtering, analog-to-digital conversion, data storage, data calculation, graphic display, etc.

[0095] ② Geographic information positioning equipment: Geographic information measurement is performed by the M100 RTK (Real-time kinematic real-time differential positioning) provided by Sinan Navigation. The principle of differential positioning is generally that under the premise that the satellite navigation system error changes slowly and has a strong temporal and spatial correlation, most of the same error terms can be eliminated by taking the difference between two receivers, thereby improving positioning accuracy. A simple differential positioning system consists of three parts: base station receiver, mobile station receiver (user station) and data link. Data link refers to the radio equipment used for communication between two stations. The positioning technology used in this measurement system is carrier differential positioning.

[0096] The premise of carrier measurement is to lock the satellite, and then measure the difference between the carrier sent by the satellite and the replica signal generated by the receiver, which is less than 2π; the receiver initializes an integer counter, and when tracking the signal, if the fractional phase changes from 2π to 0, the counter is increased by 1. The carrier phase is the instantaneous value of the difference between the carrier signal phase generated by the receiver and the carrier phase received from the satellite. It is also another important measurement value obtained by the GPS receiver from the satellite signal in addition to the pseudorange, which can achieve decimeter and centimeter-level positioning accuracy.

[0097] Figure 1 The schematic diagram of the principle of carrier phase ranging is shown in Figure 2. Assuming that the carrier frequency f remains unchanged during the signal propagation process, when t A When the carrier reaches point A, the phase at this time can be recorded as When t B When the carrier reaches point B, the phase at this time can be recorded as Then the following holds:

[0098]

[0099] The time delay caused by the signal from A to B is:

[0100]

[0101] The AB distance can be calculated:

[0102]

[0103] In the above formula, λ represents the carrier wavelength; c is the wave speed; N is the number of integer cycles between AB; is a phase of less than one cycle. Assume that at t i At time , receiver k starts to measure the distance between it and satellite j:

[0104]

[0105] Assume that from ti At the beginning of the moment, the receiver locks the satellite, then at the next moment, the whole week value counts Then, the distance from the satellite to the user can be expressed as:

[0106]

[0107] Formula (5) includes t i Unknowns in the moment observation equation At time t i+1 The directly measured whole cycle part and decimal fraction are and It can be remembered as:

[0108]

[0109] Formula (6) can be used as the time t i+1 Carrier phase observation value. When the receiver performs n carrier phase measurements, the carrier phase observation equation is:

[0110]

[0111] If the influence of error is considered, equation (7) can be rewritten as:

[0112]

[0113] where dτ * is the receiver clock deviation; dτ j is the clock bias of satellite j; is the pseudorange error caused by the ephemeris error of satellite j; For ionospheric advance; is the tropospheric delay.

[0114] Carrier phase differential is to calculate the difference between satellites, receivers and different epochs. Directly calculating the first difference of the observation value equation is called a single difference positioning model; calculating the second difference again on the basis of the first difference is called a double difference; calculating the difference again on the second difference is called a triple difference model. Usually, the first difference is calculated between receivers, the double difference is calculated between satellites, and the triple difference is calculated between different epochs. The third difference equation will amplify the error due to its strong correlation and will not improve the accuracy of the differential positioning result. Therefore, it is not often used in practice. The RTK used in this measurement system is a double difference model. The specific schematic diagram is as follows: Figure 2 As shown, B is the reference station, U is the user station, and the two stations observe satellite j at the same time. Then, according to equation (8), the single-station differential positioning observation equation can be written as:

[0115]

[0116] When the distance between the base station and the user station (mobile station) is less than 100 km:

[0117]

[0118] Simplifying formula (9), we get:

[0119]

[0120] make Formula (11) can be rewritten as:

[0121]

[0122] In formula (12), is the direction vector between the user station and satellite j; Δx u It is the user station coordinate correction item; is the distance between the base station and satellite j, is the distance between the user station and the satellite. Double difference is to find the difference between satellites based on single difference positioning. For another reference satellite k, an equation similar to equation (12) can also be obtained:

[0123]

[0124] Subtracting equation (13) from equation (12), we get:

[0125]

[0126] make The above formula can be simplified to:

[0127]

[0128] In the double-difference observation equation, if the base station and the user station observe n satellites at the same time, assuming that the reference satellite number is 1, the unknowns are the corrections of the three coordinates and the double-difference integer ambiguity, then equation (15) can be written in matrix form:

[0129] V=AX-L (16)

[0130]

[0131] In the solution process, a key issue is the correlation between the double-difference observation equations. This is because all equations select the highest angle satellite as the reference satellite when making the secondary difference, which makes the calculation results inaccurate. In order to reduce the influence of the double-difference reference satellite, the weighted least squares method is usually used to reduce the correlation of the double-difference carrier phase observations. The inverse of the covariance matrix of the double-difference carrier phase observations is used as an auxiliary solution, that is:

[0132] X=(A T RA) -1 A T RL (18)

[0133] ③Data interaction control center:

[0134] I. Regarding the spectrum analyzer control, the spectrum analyzer supports LAN port communication, and the computer can send control instructions to the spectrum analyzer through the same IP address through tools such as Matlab. The first step is that the computer establishes a normal connection with the spectrum analyzer through the addressing string: the addressing string is TCPIP0::172.141.11.202::5000::SOCKET, where 172.141.11.202 is the default IP address of the spectrum analyzer, which can be modified at any time according to the actual IP. The specific command to use Matlab to establish a connection with the spectrum analyzer is: visadev('TCPIP0::192.168.10.50::5000::SOCKET'); the second step is that the computer sends various control instructions to the spectrum analyzer, including the center frequency, scanning bandwidth, number of sampling points, cutoff frequency, start frequency, etc. of the spectrum analyzer scan. These instructions can be sent to the spectrum analyzer through the writeline function of Matlab. The specific instruction content is shown in Table 1 below.

[0135] Table 1 Some spectrum analyzer control instructions

[0136]

[0137] II. Regarding RTK data reception, the official RTK host computer receives RTK data through a USB interface and implements related parameter settings, but the host computer cannot synchronize RTK data reception with the issuance of spectrum analyzer instructions. For this reason, the present invention implements the RTK data recording part of the host computer by itself through Matlab. Matlab establishes communication with the serial port corresponding to RTK through the serialport function, and can continuously record the "GPGGA" format geographic coordinate information returned by RTK.

[0138] III. RTK and spectrum analyzer synchronous measurement, as shown in the schematic diagram Figure 3 As shown in the figure, every time the computer receives RTK feedback data, it immediately sends the corresponding spectrum analysis command for setting frequency, bandwidth and other measurement parameters to the spectrum analyzer and records the spectrum analysis data. The actual measurement results show that the time difference between a single measurement of RTK and the spectrum analyzer does not exceed 100ms. For non-high-speed mobile scenarios, its measurement error can be ignored.

[0139] A fast localized modeling method is proposed. Based on the measured data of the above-mentioned measurement system and the microcell radio wave propagation effect recommended by ITU, the model localization parameter correction is realized within 1 minute to obtain an accurate electromagnetic space deduction model with a root mean square error of no more than 5dB.

[0140] ① Microcell electromagnetic space prediction model based on ITU model:

[0141] Based on ITU recommendations, the scenario where both the transmitting and receiving antennas are located below the roof is studied. The schematic diagram of the transmitting and receiving antennas is shown in the figure below. Figure 4 As shown. Where h1 represents the height of the transmitting antenna from the ground, and h2 represents the height of the receiving antenna from the ground. Due to experimental conditions, the transmitting antenna in this article cannot be erected on the highest roof during the specific experiment. The following discussion on the path loss prediction model is based on the case where the height of the transmitting and receiving antennas is less than the maximum roof height.

[0142] I. Specific site model for Line of Sight (LoS)

[0143] For electromagnetic wave propagation in the UHF (Ultra High Frequency) frequency range, the basic transmission loss can be characterized by two slopes and one inflection point. The specific formula is as follows:

[0144]

[0145] Where d is the length of the line connecting the sending and receiving points or the distance between the sending and receiving points, R bp is the vertex distance and is calculated as:

[0146]

[0147] where λ is the wavelength (m). The lower bound is based on a two-ray planar earth reflection model.

[0148] L bp is the basic transmission loss value at the inflection point, defined as:

[0149]

[0150] L c is the fading margin, and the ITU recommends a value of 20 dB. It may vary in actual environments. α1 and α2 are attenuation coefficients, and the upper and lower limits given by ITU are 40 and 20, respectively.

[0151] In the SHF band, when the path length is up to about 1 km, road traffic will affect the effective road height and thus the breakpoint distance. bp It can be estimated as follows:

[0152]

[0153] where h s It is the effective road height derived from the research objects such as vehicles on the road and pedestrians near the road. s Depends on the traffic volume of the road. ITU provides reference values ​​for effective road heights during busy and idle hours. The h given in Tables 2 and 3 below s The values ​​are taken from daytime and nighttime measurements, corresponding to busy and off-peak traffic conditions, respectively. Busy-hour traffic corresponds to a road vehicle coverage of 10-20%, and a pedestrian occupation ratio of 0.2-1%. Off-peak traffic refers to a road coverage of 0.1-0.5%, and a pedestrian occupation ratio of less than 0.001%. The measurement experiment of the present invention was carried out from 13:00 noon to 18:00 in the afternoon. The road vehicle coverage was measured to be between 10-20%. The following experimental data processing part h s Calculations are based on peak hour traffic conditions.

[0154] Table 2 Effective height of road h s Reference table (busy hour traffic)

[0155]

[0156] Table 3 Effective height of road h s Reference table (off-peak traffic)

[0157]

[0158] When h1,h2>h s When the basic transmission loss of the SHF band can be calculated using formula (1), L bp Given by:

[0159]

[0160] On the other hand, when h1<h s or h2<h s There is no inflection point. When the two terminals are close (d<R s ), the basic transmission loss is similar to the basic propagation loss in the UHF range. When the two terminals are far apart, the propagation characteristics cause the attenuation coefficient to be a power of 3. (d≥R s ) is approximately bounded by:

[0161]

[0162] In the above formula, R s It is a statistical experience value, usually 20m;

[0163] An approximate upper bound is given by:

[0164]

[0165] Basic transmission loss L s Defined as:

[0166]

[0167] R s The empirical value is generally determined to be 20m. Based on ITU measurement values, the median value is given by the following formula:

[0168]

[0169] When the frequency is above about 10 GHz, the inflection point distance R in formula (24) bp This is far beyond the expected maximum cell radius (500m). This means that there is no expected fourth-power law in this band, but it will be close to the free space law with a path loss of about 1.9-2.2 powers.

[0170] II. Specific site model for non-line-of-sight (NLoS):

[0171] For NLoS scenarios where both antennas are below rooftop height, diffraction and reflection waves at street intersection corners must be considered. Typical propagation scenarios for microcell NLOS paths are as follows: Figure 5 shown.

[0172] exist Figure 5 In the figure, w1 represents the street width at the transmitting point (m), w2 represents the street width at the receiving point (m), x1 represents the distance from the transmitting point to the intersection (m), x2 represents the distance from the receiving point to the intersection, α represents the arc of the street corner, and STN is the abbreviation of station, which represents the location of the transmitting point and the receiving point.

[0173] The basic transmission loss characteristics can be divided into two parts: the corner loss region and the NLoS region. The corner loss region extends from a point d 1 m away from the edge of the LoS street. corner Then enter the NLoS street. Corner loss L corner It is represented by d corner Additional attenuation over distance. The NLoS region is outside the corner loss region and a coefficient parameter β applies. By using x1, x2 and w1, the overall basic transmission loss outside the corner region (x2>w1 / 2+1) can be calculated as follows:

[0174]

[0175] In the above formula, L LOS Represents the path loss in the line-of-sight situation; its value is within the lower bound L obtained by the specific site model of "I. Line-of-sight" LoS,l and upper bound LLoS,u between".

[0176] For L c , when w1 / 2+1<x2≤w1 / 2+1+d corner hour:

[0177]

[0178] In the above formula, L corner and d corner are statistical constants, L corner 20dB in urban environments and 30dB in residential environments. corner 30dB in both urban and residential environments.

[0179] When x2>w1 / 2+1+d corner hour:

[0180] L c =L corner (30)

[0181] For L att , when x2>w1 / 2+1+d corner hour:

[0182]

[0183] When x2≤w1 / 2+1+d corner When L att =0.

[0184] Here L Los is the basic transmission loss on the LoS street when (x1>20m). In formula (12), L corner The reference value is 20dB in urban environments and 30dB in residential environments. corner In both environments it is generally 30m.

[0185] In formula (31), when the four corners of the intersection are wedge-shaped buildings, β = 6. If the four corners of the intersection are sloped buildings, β is calculated according to formula (14). This is because the mirror reflection path of the sloped building seriously affects the basic transmission loss in the non-line-of-sight area.

[0186] β=4.2+(1.4log 10 f-7.8)(0.8log 10 x1-1.0) (32)

[0187] Where f is the frequency in megahertz (MHz).

[0188] ② Rapid localization method of electromagnetic space model parameters:

[0189] Assuming that the set of parameters to be optimized is x, and the model difference function after the measured results and the optimized parameters are introduced is f(x), the optimized parameters can be expressed in mathematical form as:

[0190]

[0191] Considering the nonlinearity of the function, it is impossible to solve the optimal parameters analytically. Therefore, an iterative method is needed. The specific steps are as follows:

[0192] I. Given an initial value of x.

[0193] II. For the kth iteration, find a step Δx k , so that Reached minimum value.

[0194] III. If Δx k If it is small enough, stop.

[0195] IV. Otherwise, let x k+1 =x k +Δx k , return to II.

[0196] In order to quickly find the appropriate Δx k , using the Levenberg-Marquadt method, the specific steps are as follows:

[0197] I. Determine the scope of the trust region: The numerator is the drop value of the actual function, and the denominator is the drop value of the approximate model. The ratio ρ of the two is best close to 1. J(x) represents the Jacobian matrix;

[0198] II. For the kth iteration, solve:

[0199]

[0200] In the above formula, || || 2 represents the second-order norm, D represents the coefficient matrix, and μ represents the trust radius. Both D and μ need to be set according to the actual convergence speed. D is generally set to the unit matrix, and μ should be set according to the order of magnitude of the specific parameters.

[0201] III. Solve for ρ, if Then μ=2μ, if Then μ=0.5μ, and μ remains unchanged in other cases.

[0202] IV. If ρ is greater than a certain threshold, the approximation is considered feasible, and x k+1 =x k +Δxk .

[0203] V. Determine whether the algorithm converges. If not, return to II, otherwise end.

[0204] Example 1: In order to verify the accuracy of system measurement and the reliability of model localization, a series of electromagnetic space measurement verification tests were carried out in a residential area in Huangdao District, Qingdao City. Figure 6 The experimental equipment preparation list is as follows:

[0205] ①Signal source selection: Rohde&Schwarz SMA100.

[0206] ② Transceiver antenna selection: omnidirectional antenna adapted to 2-18GHz.

[0207] ③Spectrum measurement: Ceyear4042D spectrum analyzer.

[0208] ④ Geographic location information measurement: Sinan Navigation M100 RTK, Beidou GPS magnetic antenna.

[0209] ⑤ Means of transportation: car with sunroof.

[0210] ⑥Automated measurement system control center: laptop computer

[0211] ⑦Others: RF cables, fixing ties, tapes, and mobile power supplies.

[0212] A wideband antenna suitable for 2-18GHz is used as the transceiver antenna, and an electromagnetic space automatic measurement system is built based on the Ceyear4042D spectrum analyzer and M100 RTK. Figure 7 As shown, the experimental team rented a car with a sunroof, attached a magnetic GPS antenna to the metal shell next to the sunroof, and extended the receiving antenna to the outside of the sunroof about 2.5 meters above the ground through a bracket. The transmitting antenna was set up on the terrace 7 meters above the ground after obtaining the consent of the hotel owner. In order to comprehensively measure and record the various radio wave propagation effects in the micro-cell, the path selection of this experiment is mainly based on traversing various scenes as much as possible, including the wide roads outside the community, the narrow lanes in the community, and the parking lot, etc. However, due to the large size and low flexibility of the car, it is impossible to pass through every area of ​​the community. The actual route planning is as follows: Figure 8 The spectrum analyzer uses the frequency of the transmitted signal as the center frequency. Considering the Doppler frequency shift caused by the movement of the receiving point, the scanning bandwidth is set to 10kHz, 1001 points are sampled at a time, and a group of measurements are made every 2GHz from 2GHz to 18GHz. The recorded data is as follows Figure 9-17As shown. The x-axis is the frequency in GHz, the y-axis is the sampling number, which is counted from the measurement start point to the end of the measurement, and the z-axis is the power spectrum value. Due to the limitation of the instrument's own resolution, the received signal below -115dBm will be submerged in the noise. For this reason, the present invention first removes the obvious bad points caused by various reasons such as equipment and environment, and then screens out the received signal data with power below -115dBm.

[0213] Based on the measured results, according to the model parameter fast localization method mentioned above, α1, α2, and the constant term L in formula (19) are optimized. c , and because the distance between the 2-18GHz transmitting and receiving antennas during the test satisfies d≤R bp , so we only need to optimize α1 and L c The optimized parameters and results are shown in Table 4. The optimized model simulation results are compared with the actual measurement results. Figure 18-26 shown.

[0214] Table 4 Parameter correction results and model accuracy after correction

[0215]

[0216] The data recorded by RTK is in "GPGGA" format. Therefore, after obtaining the longitude, latitude and altitude information of each point, the longitude and latitude (WGS84 coordinates) are converted to UTM (UNIVERSAL TRANSVERSE MERCARTOR GRID SYSTEM) projection, and the coordinates of the transmitting point are used as the UTM coordinate zero point to obtain the relative coordinate position of each receiving point.

[0217] After obtaining the relative coordinates of the transmitting point, the relative coordinates of the receiving point, the receiving power and other parameter information, the corresponding localization model parameters can be solved based on the Levenberg-Marquadt method to reduce the root mean square error between the model simulation value and the measured data. The root mean square error calculation formula is:

[0218]

[0219] Among them, n represents the total number of data, P i represents the model simulation value of the i-th point, Represents the measured data value of the i-th point.

Claims

1. A microcell electromagnetic space automatic measurement system, characterized by: It includes a data interaction control center and spectrum measurement equipment and geographic information positioning equipment electrically connected thereto.

2. According to claim 1, the microcell electromagnetic space automatic measurement system is characterized by: The data interaction control center is a computer, the spectrum measurement device is a spectrum analyzer, and the geographic information positioning device is a real-time differential positioning device.

3. A fast localized modeling method based on the measured data of the measurement system of claim 1, characterized in that: (1) Microcell electromagnetic space prediction model based on ITU model: (11) Specific site model for sight distance: For electromagnetic wave propagation in the UHF frequency range, the basic transmission loss characteristics are represented by two slopes and one inflection point. The specific formula is as follows: In the above formula, d is the distance between the transmitting and receiving points, L c is the fading margin, α1 and α2 are the attenuation coefficients, and R bp is the breakpoint distance, calculated as: In the above formula, λ is the wavelength, h1 represents the height of the transmitting antenna above the ground, and h2 represents the height of the receiving antenna above the ground; L bp is the basic transmission loss value at the inflection point, calculated by the following formula: In the UHF band, when the path length is up to 1 km, R bp Calculate as follows: In the above formula, h s is the effective road height caused by vehicles on the road; When h1>h s ,h2>h s When L bp Given by: The approximate lower bound is given by: In the above formula, R s It is the statistical experience value; An approximate upper bound is given by: L s Defined as: The median value is given by: (12) Non-line-of-sight specific site model: w1 represents the street width at the transmitting point, x1 represents the distance from the transmitting point to the intersection, and x2 represents the distance from the receiving point to the intersection. The overall basic transmission loss outside the corner area is calculated using the following formula: L NLoS2 =L LoS +L c +L att In the above formula, L LOS Indicates the path loss in the case of line-of-sight for transmission and reception; For L c , when w12+1<x2≤w12+1+d corner hour: In the above formula, L corner and d corner All are statistical constants; When x2>w12+1+d corner hour: L c =L corner For L att , when x2>w1 / 2+1+d corner hour: When the four corners of the intersection are wedge-shaped buildings, β = 6; When the four corners of the intersection are inclined buildings, β is calculated according to the following formula: β=4.2+(1.4log 10 f-7.8)(0.8log 10 x1-1.0) In the above formula, f is the frequency; When x2≤w1 / 2+1+d corner When L att =0; (2) Rapid localization method of electromagnetic space model parameters: Assuming that the set of parameters to be optimized is x, the model difference function after the measured results and the optimized parameters are introduced is f(x), then the optimized parameters are expressed in mathematical form as: The iterative method is used to solve the problem. The specific steps are as follows: (21) Given an initial value of x; (22) For the kth iteration, find a step Δx k , so that Reach a minimum value; (23) If Δx k If x is small enough, stop; otherwise, let x k+1 =x k +Δx k , returns (22).

4. The rapid localization modeling method according to claim 3, characterized in that: In step (23), the Levenberg-Marquardt method is used to find a suitable Δx k , the specific steps are as follows: (31) Determine the scope of the trust region: In the above formula, J(x) represents the Jacobian matrix; (32) For the kth iteration, solve: In the above formula, 2 represents the second-order norm, D represents the coefficient matrix, and μ represents the trust radius; (33) Solve for ρ, if Then μ=2μ, if Then μ = 0.5μ, and μ remains unchanged in other cases; (34) If ρ is greater than a certain threshold, the approximation is considered feasible, and x k+1 =x k +Δx k ; (35) Determine whether the algorithm converges. If not, return to (32), otherwise terminate.

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