An indoor positioning method and system based on feeder multiplexing of BeiDou dual frequencies

Abstract

This invention relates to the field of satellite navigation and wireless communication technology, and discloses an indoor positioning method and system based on feeder-multiplexed BeiDou dual-frequency signals. The method involves receiving outdoor B1I and B2a signals via a high-gain BeiDou dual-frequency receiving antenna. Frequency separation and signal conditioning are performed using a dual-frequency separation filter. The B1I signal is then transmitted to an indoor distributed antenna for broadcasting via an existing communication feeder network, while the B2a signal is transmitted to a central directional antenna to establish a time synchronization reference. A mobile terminal receives the dual-frequency signals, performs acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations. Observation data from the same antenna are paired, the pseudorange difference between the two frequencies is calculated, and system errors are eliminated. Weighted least squares and Kalman filtering are used for position calculation. This invention solves the problem that traditional GPS signals cannot cover indoor environments and has promising application prospects in scenarios such as underground commercial complexes, tunnels, and parking lots.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and wireless communication technology, and more specifically, to an indoor positioning method and system based on feeder multiplexing of BeiDou dual frequencies. Background Technology

[0002] With the completion of the global network of the BeiDou Navigation Satellite System, its positioning accuracy and reliability in outdoor environments have reached world-class levels. However, in indoor environments, due to the obstruction and attenuation of satellite signals by buildings, traditional GNSS signals cannot be directly used for indoor positioning, which severely limits the application of navigation and positioning technology in indoor scenarios.

[0003] Existing indoor positioning technologies primarily rely on communication methods such as Wi-Fi, Bluetooth, and UWB. Wi-Fi positioning technology uses signal strength indicators for location, but is affected by signal interference and multipath effects, resulting in positioning accuracy typically between 5-15 meters, which is insufficient for high-precision requirements. While Bluetooth positioning technology is less expensive, its signal coverage is limited and susceptible to environmental interference, with positioning accuracy usually between 1-3 meters. UWB positioning technology offers high accuracy, reaching centimeter-level precision, but its high equipment cost and complex deployment limit its large-scale application.

[0004] Currently, there is a lack of a low-cost, high-precision indoor positioning solution that can fully utilize existing communication feeder networks and perform indoor GNSS signal relay via passive antennas. Traditional solutions that use leaky cables for GNSS signal retransmission, while providing continuous coverage, are inflexible in deployment, costly, and difficult to integrate with existing communication systems, thus failing to meet the comprehensive needs of modern buildings for indoor positioning systems. Summary of the Invention

[0005] This invention provides an indoor positioning method and system based on feeder multiplexing of Beidou dual frequencies, solving technical problems such as inflexible deployment, high cost, and difficulty in integrating with existing communication systems in related technologies.

[0006] This invention provides an indoor positioning method based on feeder multiplexing of BeiDou dual frequencies, comprising the following steps:

[0007] The Beidou dual-frequency receiving antenna receives B1I and B2a signals, which are then separated by a dual-frequency separation filter. Automatic gain control and phase calibration are performed by a signal conditioning circuit.

[0008] The separated B1I and B2a signals are fed into power amplifiers for signal power amplification, and an impedance matching network is set at the output.

[0009] The B1I signal is transmitted through the existing communication feeder network and a duplexer is installed at the input end to combine it with the original communication signal for transmission. The B2a signal is transmitted to the central directional antenna through a dedicated feeder.

[0010] Distributed antennas are installed along the feeder network to broadcast B1I signals indoors. Each distributed antenna is equipped with a time synchronization module to achieve network-wide synchronization via B2a signals.

[0011] The mobile terminal receives dual-frequency signals, performs signal acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations.

[0012] B1I and B2a observation data from the same signal source are paired and processed to calculate the difference between the two frequency pseudoranges, eliminate systematic errors, and smooth the data using a moving average filtering algorithm.

[0013] A set of position calculation equations is established based on the precise pseudorange difference, and the weighted least squares method is used to solve them. The calculation results of multiple consecutive epochs are processed by Kalman filtering to output three-dimensional coordinates.

[0014] In a preferred embodiment, the dual-frequency separation filter includes a B1I signal separation filter and a B2a signal separation filter. The center frequency of the B1I signal separation filter is 1561.098MHz, the bandwidth is 20MHz, and the out-of-band rejection is greater than 40dB. The center frequency of the B2a signal separation filter is 1176.45MHz, the bandwidth is 20MHz, and the out-of-band rejection is greater than 40dB. The separated signals are subjected to automatic gain control and phase calibration by a signal conditioning circuit.

[0015] In a preferred embodiment, the signal power amplification includes a B1I signal power amplifier and a B2a signal power amplifier. The output power of the B1I signal power amplifier is determined according to the feeder length and the number of branches, with a typical output power of 20-30 dBm. The output power of the B2a signal power amplifier is 25-35 dBm. The power amplifier has a built-in temperature compensation circuit to maintain output power stability within the range of -20 degrees Celsius to +60 degrees Celsius.

[0016] In a preferred embodiment, the feeder network uses low-loss coaxial cable. The B1I signal is transmitted through the feeder network of the existing communication system and a duplexer is installed at the input end to combine with the original communication signal for transmission. A power divider is set every 50 to 100 meters to distribute the B1I signal to the distributed antennas in the local area. The B2a signal is transmitted to the high-gain directional antenna through a dedicated feeder.

[0017] In a preferred embodiment, the time synchronization module incorporates a high-stability temperature-controlled crystal oscillator with a frequency stability better than 1×10⁻⁶. -9Synchronization accuracy is achieved through a phase-locked loop circuit, and the synchronization error is controlled within 10 nanoseconds. An indoor distributed antenna is installed every 30 to 50 meters.

[0018] In a preferred embodiment, the digital signal processing includes a signal acquisition process that uses a fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal and search for pseudo-random code peaks, and a signal tracking process that uses an extended Kalman filter algorithm to establish a signal state and observation model to estimate the carrier phase and pseudorange.

[0019] In a preferred embodiment, the calculation of the dual-frequency pseudorange difference includes calculating the pseudorange difference between B1I and B2a of the same signal source, eliminating the time delay difference of the dual-frequency signal system, and using a moving average filtering algorithm to smooth the pseudorange difference of multiple epochs, with a filtering window length of 5 epochs.

[0020] In a preferred embodiment, the position calculation uses the weighted least squares method, the weight matrix is ​​determined according to the signal quality, and the observations with high signal-to-noise ratios have higher weights. The calculation results of multiple consecutive epochs are processed by Kalman filtering to establish a motion state model, and the position accuracy after filtering is better than 1 meter.

[0021] In a preferred embodiment, an indoor positioning method based on feeder multiplexing of BeiDou dual frequencies further includes using optical fiber transmission technology to replace coaxial cable transmission, and converting B1I and B2a signals into optical signals for transmission through a radio frequency optical fiber transmission system, thereby reducing transmission loss and improving system transmission distance and signal quality.

[0022] In a preferred embodiment, an indoor positioning system based on feeder-multiplexed BeiDou dual-frequency, used to execute the above-described indoor positioning method based on feeder-multiplexed BeiDou dual-frequency, includes:

[0023] The BeiDou dual-frequency signal receiving and preprocessing unit receives B1I and B2a signals through the BeiDou dual-frequency receiving antenna, performs frequency separation through a dual-frequency separation filter, and performs automatic gain control and phase calibration through a signal conditioning circuit.

[0024] The signal power amplification and feeder adaptation unit sends the separated B1I and B2a signals to the power amplifier for power amplification, and sets an impedance matching network at the output end.

[0025] The feeder multiplexing transmission network deployment unit transmits the B1I signal through the existing communication feeder network and combines it with the original communication signal through a duplexer installed at the input end. The B2a signal is transmitted to the central directional antenna through a dedicated feeder.

[0026] The indoor distributed signal coverage unit installs distributed antennas along the feeder network to broadcast B1I signals indoors. Each distributed antenna is equipped with a time synchronization module to achieve full network synchronization via B2a signals.

[0027] The mobile terminal signal receiving and data extraction unit receives dual-frequency signals, performs signal acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations.

[0028] The dual-frequency pseudorange differential processing unit processes B1I and B2a observation data from the same signal source in pairs, calculates the difference between the two frequencies, eliminates system errors, and uses a moving average filtering algorithm for smoothing.

[0029] The position calculation and output unit establishes a set of position calculation equations based on the precise pseudorange difference, solves them using the weighted least squares method, performs Kalman filtering on the calculation results of multiple consecutive epochs, and outputs three-dimensional coordinates.

[0030] The beneficial effects of this invention are as follows:

[0031] By fully utilizing the existing DAS feeder network of the communication system, redundant wiring and infrastructure construction are avoided, reducing system deployment costs. Reusing existing feeder resources lowers the overall deployment cost compared to standalone indoor positioning systems, while also minimizing modifications to the building structure and improving the system's engineering feasibility. High integration with existing communication infrastructure makes system maintenance more convenient, fault location faster, and operating costs significantly reduced.

[0032] Employing BeiDou dual-frequency signal processing technology, and through pseudorange differential processing of B1I and B2a signals, ionospheric errors and system delay errors are effectively eliminated, achieving meter-level positioning accuracy. Compared to traditional Wi-Fi positioning technology, positioning accuracy is significantly improved, reaching a practical level. Through distributed antenna arrays and time synchronization technology, seamless coverage of large indoor spaces is achieved, solving the coverage blind spot problem of traditional positioning technologies. It is particularly suitable for high-precision positioning needs in complex indoor environments such as large commercial complexes and underground spaces. Attached Figure Description

[0033] Figure 1 This is a flowchart of an indoor positioning method based on feeder multiplexing of Beidou dual frequencies according to the present invention;

[0034] Figure 2 This is a block diagram of an indoor positioning system based on feeder multiplexing of Beidou dual frequencies according to the present invention. Detailed Implementation

[0035] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0036] At least one embodiment of the present invention discloses an indoor positioning method based on feeder multiplexing of BeiDou dual frequencies, such as... Figure 1 As shown, it includes the following steps:

[0037] The S100 receives B1I and B2a signals through a Beidou dual-frequency receiving antenna, performs frequency separation through a dual-frequency separation filter, and performs automatic gain control and phase calibration through a signal conditioning circuit.

[0038] A high-gain BeiDou dual-band receiving antenna is installed on the roof of the building, with an antenna gain of no less than 30dBi, supporting simultaneous reception of signals from the B1I band (1561.098MHz) and the B2a band (1176.45MHz). The receiving antenna is connected to a dual-band low-noise amplifier to perform primary amplification of the received signal, with an amplification gain of 20dB and a noise figure of less than 1.5dB.

[0039] The B1I and B2a signals are frequency-separated using a dual-frequency separation filter. The center frequency of the B1I signal separation filter is 1561.098MHz, the bandwidth is 20MHz, and the out-of-band rejection is greater than 40dB. The center frequency of the B2a signal separation filter is 1176.45MHz, the bandwidth is 20MHz, and the out-of-band rejection is greater than 40dB. The two separated signals are then fed into independent signal conditioning circuits.

[0040] The signal conditioning circuit includes an automatic gain control module and a phase calibration module. The automatic gain control module automatically adjusts the gain according to the received signal strength to ensure that the output signal amplitude is stable at the target level. The phase calibration module performs phase synchronization calibration on the two signals using a built-in high-stability crystal oscillator reference, controlling the phase error to within 1 degree.

[0041] Optionally, in some embodiments, digital signal processing technology is used instead of analog filters. A high-speed analog-to-digital converter converts the radio frequency signal into a digital signal, and then digital filtering algorithms are used to achieve signal separation and processing. This can achieve higher separation accuracy and better frequency selectivity, which is superior to the performance of analog filters.

[0042] S200, the separated B1I and B2a signals are sent to the power amplifier for signal power amplification, and an impedance matching network is set at the output.

[0043] The separated B1I and B2a signals are respectively fed into dedicated power amplifiers for power amplification. The output power of the B1I signal power amplifier is determined based on the feeder length and the number of branches, with a typical output power of 20-30 dBm. The power amplifier adopts a linear amplification design, with third-order intermodulation product rejection greater than 30 dBc, ensuring that signal quality is not affected.

[0044] The B2a signal power amplifier has an output power of 25-35dBm and is used to drive high-gain directional antennas for indoor broadcasting. The power amplifier has a built-in temperature compensation circuit, maintaining output power stability better than ±0.5dB over a temperature range of -20℃ to +60℃.

[0045] An impedance matching network is installed at the output of the power amplifier to match the output impedance to a standard 50-ohm impedance, reducing signal reflection loss. The matching network includes L-type and π-type matching circuits, which are adaptively adjusted according to the characteristic impedance of different feeders.

[0046] To prevent the power amplifier from overloading, a power detection circuit is installed at the input terminal. When the input power exceeds the safety threshold, the power protection mechanism is automatically activated to avoid damage to the equipment.

[0047] Optionally, in some embodiments, digital predistortion technology is used to improve the linearity of the power amplifier. The nonlinear characteristics of the amplifier are monitored in real time by a digital signal processor, and a predistortion signal is generated to pre-compensate the input signal, thereby significantly improving the linear performance and spectral purity of the amplifier and effectively improving signal quality.

[0048] S300, B1I signal is transmitted through the existing communication feeder network and a duplexer is installed at the input end to combine with the original communication signal for transmission, while B2a signal is transmitted to the central directional antenna through a dedicated feeder.

[0049] The B1I signal is transmitted through the feeder network of the existing DAS communication system. A duplexer is installed at the feeder input to combine the B1I signal with the existing communication signal for transmission. The insertion loss of the B1I signal channel in the duplexer is less than 1 dB, the insertion loss of the communication signal channel is less than 0.5 dB, and the isolation is greater than 40 dB.

[0050] The feeder network uses low-loss coaxial cable, typically 7 / 8-inch foam-filled coaxial cable, with a transmission loss of approximately 0.05 dB / m in the B1I band. A power divider is installed every 50-100 meters to distribute the B1I signal to the distributed antennas in the local area. The power divider uses a Wilkinson design, with power balance at each output port better than ±0.3 dB.

[0051] The B2a signal is transmitted via a dedicated feeder to a high-gain directional antenna located at the center of the building. The dedicated feeder uses 1 / 2-inch low-loss coaxial cable with a transmission loss of approximately 0.08 dB / m. A high-gain planar antenna with a gain of 15-18 dBi and a beamwidth of 60-80 degrees is installed at the end of the feeder.

[0052] Signal monitors are installed at key nodes of the feeder to monitor the power level and signal quality of the transmitted signal in real time. When the signal power falls below a preset threshold or the signal quality deteriorates, the system automatically issues an alarm message, facilitating timely handling by maintenance personnel.

[0053] Optionally, in some embodiments, fiber optic transmission technology is used instead of coaxial cable transmission. The B1I and B2a signals are converted into optical signals for transmission through a radio frequency fiber optic transmission system. This can reduce transmission loss, improve the transmission distance and signal quality of the system, and is particularly suitable for the long-distance signal transmission needs of ultra-large buildings. At the same time, it has excellent electromagnetic compatibility and environmental adaptability.

[0054] S400, distributed antennas are installed along the feeder network to broadcast B1I signals indoors. Each distributed antenna is equipped with a time synchronization module to achieve network-wide synchronization via B2a signals.

[0055] An indoor distributed antenna is installed every 30-50 meters along the feeder network for indoor B1I signal broadcasting. The indoor antennas can be omnidirectional or directional, with the appropriate antenna type selected based on the shape and size of the coverage area. Omnidirectional antennas are suitable for open areas with a gain of 3-6 dBi; directional antennas are suitable for narrow corridors with a gain of 8-12 dBi.

[0056] Each distributed antenna is equipped with an independent time synchronization module, achieving network-wide time synchronization by receiving the B2a synchronization reference signal. The time synchronization module incorporates a high-stability temperature-controlled crystal oscillator with a frequency stability better than 1×10⁻⁶. -9 Synchronization accuracy is achieved through a phase-locked loop circuit, with synchronization error controlled within 10 nanoseconds.

[0057] A B2a signal transmitting antenna is installed in the center of the building. This is a high-gain planar antenna, vertically mounted, radiating to cover the entire indoor area. The transmit power is adjusted according to the coverage area, typically ranging from 1 to 5 watts. Reflectors are placed around the antenna to enhance signal directionality and coverage.

[0058] To reduce multipath interference, signal absorbing materials are installed at key locations to reduce signal reflection. The absorbing material is a tapered absorbing material with a gradually changing dielectric constant, and the reflection loss in the B1I and B2a frequency bands is greater than 20dB.

[0059] Optionally, in some embodiments, adaptive beamforming technology is used to optimize signal coverage. By dynamically adjusting the beam direction and beamwidth of the smart antenna array according to user distribution and signal propagation environment, the optimal allocation of signal power is achieved, thereby improving signal coverage quality and positioning accuracy.

[0060] S500: The mobile terminal receives dual-frequency signals, performs signal acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations.

[0061] The mobile receiving terminal is equipped with a BeiDou dual-frequency receiving module, supporting simultaneous reception of B1I and B2a signals. The receiving module adopts a multi-channel parallel processing architecture, allocating 8 tracking channels for B1I signals and 4 tracking channels for B2a signals, ensuring simultaneous tracking of signals from multiple antennas.

[0062] The receiver module's RF front-end includes a low-noise amplifier, a downconverter, and an analog-to-digital converter (ADC). The low-noise amplifier has a noise figure of less than 2dB and a gain of 20dB. The downconverter converts the RF signal to an intermediate frequency (IF) signal at 70MHz with a image rejection greater than 40dB. The ADC has a sampling rate of 140MHz and a quantization bit depth of 8 bits.

[0063] The digital signal processing unit performs correlation processing on the received signal to extract navigation data and ranging information. The signal acquisition process employs a Fast Fourier Transform (FFT) algorithm, specifically implemented by performing an FFT on a time-domain signal x(n) of length N. The transform formula is as follows:

[0064]

[0065] Where X(k) is the frequency domain signal; x(n) is the time domain signal; k is the frequency index; n is the time domain sampling point index; N is the signal length; and j is the imaginary unit.

[0066] The peak value of the pseudo-random code is searched in the frequency domain, and peak detection is achieved by calculating the power spectral density:

[0067] P(k)=|X(k)| 2

[0068] Where P(k) is the power spectral density at the k-th frequency point; |X(k)| 2 It is the square of the magnitude of the frequency domain signal.

[0069] Based on the peak position k max Determine the carrier frequency of the signal:

[0070] f c =k max ·f s / N

[0071] Among them, fc The carrier frequency is expressed in Hz; k max f is the frequency index corresponding to the maximum power spectral density; s The sampling frequency is in Hz; N is the signal length.

[0072] Signal tracking employs the extended Kalman filter algorithm to establish a signal state vector containing carrier phase, carrier frequency and its rate of change, and code phase and its rate of change.

[0073]

[0074] Where, x k Let φ be the signal state vector at time k; k The carrier phase, in radians; f k The carrier frequency is expressed in Hz. τ is the rate of change of carrier frequency, in Hz / s. k This refers to the code phase, measured in chips. is the code phase change rate, in chips / s; T represents matrix transpose; k is the time index.

[0075] The state transition equation is:

[0076] x k+1 =F k x k +w k

[0077] Where, x k+1 x is the state vector at time k+1; k Let F be the state vector at time k; k Let w be the state transition matrix at time k; k Let k be the process noise vector at time k; k is the time index.

[0078] The observation equation is:

[0079] z k =h(x k )+v k

[0080] Among them, z k Let x be the observation vector at time k; h(·) is the nonlinear observation function (equivalent to the activation function); k v is the state vector at time k; k Let k be the observation noise vector at time k; k is the time index.

[0081] Linearization via Jacobian matrix:

[0082]

[0083] Among them, H k Let be the Jacobian matrix at time k; Let h be the partial derivative of the observation function h with respect to the state vector x; This is the predicted state value at time k; k is the time index.

[0084] The carrier phase and pseudorange of the signal are estimated through prediction and update steps.

[0085] Pseudorange and carrier phase observations are extracted from the tracked signal. The pseudorange measurement accuracy is better than 1 meter, and the carrier phase measurement accuracy is better than 0.01 cycles. The observation data includes parameters such as signal arrival time, carrier phase, signal strength, and signal-to-noise ratio.

[0086] The observed data undergoes quality checks to remove outliers and low-quality data. These checks include signal-to-noise ratio (SNR) checks, pseudorange rate checks, and carrier phase continuity checks. Appropriate thresholds are set to ensure data quality.

[0087] Before proceeding with further processing, necessary preprocessing of the observation data is required. Signal strength data is normalized using a min-max normalization method to map signal strength values ​​to the [0,1] interval, avoiding excessive differences in signal strength that could affect subsequent calculations. Simultaneously, a unified time reference is established by adjusting the timestamps of all observation data to relative times, eliminating the impact of absolute time differences on calculation accuracy.

[0088] Optionally, in some embodiments, machine learning algorithms are employed to improve the accuracy of signal recognition and data extraction. A deep neural network model is constructed to achieve automatic identification and classification of signal features. The network comprises an input layer, a hidden layer, and an output layer. The input layer receives normalized signal feature vectors (including features such as signal strength, signal-to-noise ratio, and carrier phase). The hidden layer uses the ReLU activation function for nonlinear transformation. The output layer uses the Softmax function to calculate the probability distribution of each category, thereby automatically optimizing signal processing parameters and significantly improving signal reception capability and data quality in weak signal environments.

[0089] S600 pairs B1I and B2a observation data from the same signal source, calculates the dual-frequency pseudorange difference, eliminates system errors, and uses a moving average filtering algorithm for smoothing.

[0090] B1I and B2a signal observation data from the same transmitting antenna are paired. The pairing principle is to pair observation data with similar signal reception times and the same signal source identifier. The time window is set to 1 second, and observation data within this time window are considered synchronous observations.

[0091] Calculate the pseudorange difference between B1I and B2a from the same signal source. The dual-frequency pseudorange differential formula is:

[0092] Δρ i =ρ B1I,i -ρ B2a,i

[0093] Where, Δρ i ρ represents the dual-frequency pseudorange difference of the i-th signal source, in meters; B1I,i ρ represents the pseudorange observation value of the B1I signal from the i-th signal source, in meters; B2a,i The value represents the pseudorange observation of the B2a signal from the i-th signal source, in meters; i represents the signal source number.

[0094] Since the ionosphere has a relatively small impact on the indoor environment, the main consideration is the difference in propagation delay between the two-frequency signals. This difference in delay is primarily caused by the group delay difference in the signal processing circuitry, and can be eliminated through system calibration.

[0095] The precise pseudorange difference after eliminating systematic errors is:

[0096] ρ corrected,i =Δρ i -Δ sys

[0097] Where, ρ corrected,i Δρ represents the precise pseudorange difference of the i-th signal source after eliminating systematic errors, in meters; i Δ represents the original dual-frequency pseudorange difference of the i-th signal source, in meters; s ys represents the time delay difference of the dual-frequency signal system obtained from system calibration, in meters; i represents the signal source number.

[0098] The pseudorange differences across multiple epochs are smoothed using a moving average filtering algorithm to reduce observation noise. A filter window length is set, and data within the window are assigned different weights for weighted averaging. The weighting function uses a Gaussian distribution, effectively reducing observation noise and improving the accuracy and stability of the pseudorange differences.

[0099] Optionally, in some embodiments, a wavelet denoising algorithm is used to preprocess the pseudorange observation data. The signal is decomposed into multiple wavelet components using the Daubechies wavelet basis function, then soft-threshold denoising is applied to the high-frequency detail coefficients, and finally, the denoised signal is obtained through wavelet reconstruction. This effectively removes high-frequency noise and anomalous jumps from the observation data, further improving the accuracy and stability of pseudorange differential processing.

[0100] The S700 establishes a set of position calculation equations based on the precise pseudorange difference, solves them using the weighted least squares method, performs Kalman filtering on the calculation results of multiple consecutive epochs, and outputs three-dimensional coordinates.

[0101] A three-dimensional spatial coordinate system was established, with a fixed point on the building as the origin, forming a right-handed rectangular coordinate system. The coordinate positions of each distributed antenna were obtained through high-precision measurement, with an accuracy better than 0.1 meters.

[0102] A set of position calculation equations is established based on the pseudorange difference after error elimination. For n signal sources, the position calculation equations are:

[0103]

[0104] Where, ρ corrected,i (x, y, z) represents the precise pseudorange difference of the i-th signal source after eliminating system errors, in meters; (x, y, z) represents the three-dimensional coordinates of the mobile terminal to be solved, in meters; (x i ,y i ,z i () represents the known three-dimensional coordinates of the i-th signal source, in meters; c represents the speed of light constant, with a value of 3 × 10⁻⁶. 8 m / s; δt represents the mobile terminal clock deviation in seconds; i represents the signal source number, i = 1, 2, ..., n1; n1 represents the total number of available signal sources.

[0105] The weighted least squares method is used to solve the system of equations. The weight matrix is ​​determined based on signal quality; observations with high signal-to-noise ratio (SNR) have larger weights, and observations with low SNR have smaller weights. First, the SNR in decibels is converted to a linear value:

[0106]

[0107] Among them, SNR linear,i SNR is the linear signal-to-noise ratio of the i-th observation, dimensionless; dB,i is the signal-to-noise ratio in dB for the i-th observation; i represents the observation number, i = 1, 2, ..., n; n represents the total number of available signal sources.

[0108] Then, normalization is performed to ensure that the sum of the weights is 1. The weight calculation formula is:

[0109]

[0110] Among them, w i SNR represents the dimensionless weighting coefficient of the i-th observation; linear,i This represents the linear signal-to-noise ratio of the i-th observation; The sum of the linear signal-to-noise ratios of all available signal sources is represented; j represents the summation variable, j = 1, 2, ..., n1; n1 represents the total number of available signal sources; i represents the observation number.

[0111] When there are more than four available signal sources, the system can simultaneously calculate the three-dimensional coordinates and clock offset. When there are three signal sources, assuming the terminal is on a known height plane, the system calculates the two-dimensional coordinates.

[0112] Kalman filtering is applied to the solution results from multiple consecutive epochs to establish a motion state model including position, velocity, and acceleration. The state vector is defined as follows:

[0113]

[0114] in, Let x, y, z be the state vector at time k1; x, y, z are the three-dimensional position coordinates in meters; v x ,v y ,v z Three-dimensional velocity components, in meters per second; a x ,a y ,a z The acceleration components are in three dimensions, with units of meters per second²; T represents matrix transpose.

[0115] The state transition equation is:

[0116]

[0117] in, This is the state vector at time k1+1; Let k1 be the state vector. The state transition matrix at time k1; Let be the process noise vector at time k1.

[0118] The observation equation is:

[0119]

[0120] in, Let be the observation vector at time k1; The observation matrix at time k1; Let k1 be the state vector. Let be the observation noise vector at time k1.

[0121] The prediction steps of Kalman filtering are as follows:

[0122]

[0123] in, This is the predicted state value at time k1; This is the state estimate at time k1-1; Let be the state transition matrix at time k1; Let be the prediction error covariance matrix at time k1; Let be the estimation error covariance matrix at time k1-1; This is the transpose of the state transition matrix; Let be the process noise covariance matrix at time k1.

[0124] The update steps are as follows:

[0125]

[0126] in, Let K1 be the Kalman gain matrix; Let be the prediction error covariance matrix at time k1; Let be the observation matrix at time k1; This is the transpose of the observation matrix; Let be the observation noise covariance matrix at time k1; This is the state estimate at time k1; This is the predicted state value at time k1; Let be the observation vector at time k1; Let I be the estimation error covariance matrix at time k1; I is the identity matrix.

[0127] The process noise and observation noise parameters were determined based on actual test data, and the position accuracy after filtering was better than 1 meter.

[0128] The system outputs real-time 3D coordinates, positioning accuracy assessment, and motion status information of the terminal. The coordinate output format is in the building's local coordinate system, while also providing an interface for conversion to a geographic coordinate system. The positioning accuracy assessment includes horizontal and vertical accuracy factors.

[0129] Optionally, in some embodiments, a particle filtering algorithm is employed to handle position calculations in nonlinear and non-Gaussian noise environments. Multiple particles are initialized, each representing a possible state. New particles are then generated through a prediction step, and particle weights are calculated in an update step. Weight normalization is performed, and finally, a weighted average is used to obtain the state estimate. Resampling is performed when the number of effective particles falls below a threshold, improving positioning accuracy and robustness in complex environments, and is particularly suitable for indoor environments with severe multipath interference.

[0130] like Figure 2 As shown, this invention provides an indoor positioning system based on feeder-multiplexed BeiDou dual-frequency, comprising:

[0131] The BeiDou dual-frequency signal receiving and preprocessing unit receives B1I and B2a signals through the BeiDou dual-frequency receiving antenna, performs frequency separation through a dual-frequency separation filter, and performs automatic gain control and phase calibration through a signal conditioning circuit.

[0132] The signal power amplification and feeder adaptation unit sends the separated B1I and B2a signals to the power amplifier for power amplification, and sets an impedance matching network at the output end.

[0133] The feeder multiplexing transmission network deployment unit transmits the B1I signal through the existing communication feeder network and combines it with the original communication signal through a duplexer installed at the input end. The B2a signal is transmitted to the central directional antenna through a dedicated feeder.

[0134] The indoor distributed signal coverage unit installs distributed antennas along the feeder network to broadcast B1I signals indoors. Each distributed antenna is equipped with a time synchronization module to achieve full network synchronization via B2a signals.

[0135] The mobile terminal signal receiving and data extraction unit receives dual-frequency signals, performs signal acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations.

[0136] The dual-frequency pseudorange differential processing unit processes B1I and B2a observation data from the same signal source in pairs, calculates the difference between the two frequencies, eliminates system errors, and uses a moving average filtering algorithm for smoothing.

[0137] The position calculation and output unit establishes a set of position calculation equations based on the precise pseudorange difference, solves them using the weighted least squares method, performs Kalman filtering on the calculation results of multiple consecutive epochs, and outputs three-dimensional coordinates.

[0138] In one embodiment of the present invention, an application example of an indoor positioning method based on feeder multiplexing of BeiDou dual frequencies is provided:

[0139] The indoor positioning system of this invention was deployed on the B1 level of a large underground commercial complex. This level has an area of ​​approximately 15,000 square meters, contains 120 shops and restaurants, and has an average daily customer flow of approximately 8,000 people. The existing DAS communication system covers the entire level, with a total feeder length of approximately 2,000 meters.

[0140] Example of BeiDou dual-frequency signal reception and preprocessing:

[0141] A Trimble Zephyr 3 high-gain BeiDou dual-frequency receiving antenna with a gain of 32 dBi was installed on the roof of the building. The received B1I and B2a signals were amplified by a dual-frequency low-noise amplifier and then frequency-separated by a dual-frequency separation filter. Actual test data are shown in Table 1.

[0142] Table 1: Signal Reception and Preprocessing Parameters

[0143] Parameter name B1I band B2a band unit Received signal strength -128 -125 dBm Amplifier gain 20 20 dB Filter out-of-band suppression 42 45 dB Phase calibration error 0.8 0.6 Spend Signal quality factor 8.5 9.2 Dimensionless

[0144] Table 1 illustrates the key performance parameters for BeiDou dual-frequency signal reception and preprocessing, including received signal strength, amplifier gain, filter performance, phase calibration accuracy, and signal quality evaluation indicators.

[0145] Example of signal power amplification and feeder adaptation:

[0146] The B1I signal is amplified by a 25dBm power amplifier, and the B2a signal is amplified by a 30dBm power amplifier. The power amplifiers used are Mini-Circuits ZVE-3W-83+ models, achieving a third-order intermodulation product suppression of 35dBc. An impedance matching network precisely matches the output impedance to 50 ohms, as shown in Table 2.

[0147] Table 2: Power Amplification and Matching Parameters

[0148] Parameter name B1I signal B2a signal unit Output power 25 30 dBm Power stability ±0.3 ±0.4 dB Third-order cross-modulation inhibition 35 33 dBc Impedance matching 1.15:1 1.12:1 VSWR Temperature compensation range -20～+60 -20～+60 ℃

[0149] Table 2 summarizes the core performance indicators of dual-frequency signal power amplification and feeder adaptation, and verifies the power output stability and impedance matching effect of the system in different frequency bands.

[0150] Example of deploying a feeder multiplexed transmission network:

[0151] Existing 7 / 8-inch foam-filled coaxial cable was used for B1I signal transmission, while a dedicated 1 / 2-inch coaxial cable was used for B2a signal transmission. Anritsu MS2038C signal monitors were installed at 12 key nodes of the feeder network to monitor transmission quality in real time, as shown in Table 3.

[0152] Table 3: Feeder Transmission Network Parameters

[0153] Parameter name B1I transmission B2a transmission unit Feeder type 7 / 8” foam filling 1 / 2” low loss - Transmission loss 0.048 0.082 dB / m Number of power dividers 8 1 indivual Port balance ±0.25 ±0.30 dB Signal monitoring point 12 4 indivual

[0154] Table 3 summarizes the deployment configuration and performance indicators of the feeder multiplexing transmission network, demonstrating the effectiveness of loss control and quality monitoring of dual-frequency signals during transmission.

[0155] Example of indoor distributed signal coverage implementation:

[0156] Twenty indoor distributed antennas are deployed along the feeder network, including 16 omnidirectional antennas (5 dBi gain) for open areas and 4 directional antennas (10 dBi gain) for narrow corridors. A single B2a high-gain planar antenna (17 dBi gain) is installed in the central atrium. Each antenna is equipped with a Microsemi SA.45s time synchronization module, as shown in Table 4.

[0157] Table 4: Distributed Antenna Deployment Parameters

[0158] Antenna type quantity Gain Coverage radius Synchronization accuracy Omnidirectional antenna 16 5 28 8 Directional antenna 4 10 35 6 B2a reference antenna 1 17 All regions 5 unit indivual dBi rice nanosecond

[0159] Table 4 summarizes the configuration schemes of indoor distributed antenna systems, showing the performance differences of different types of antennas in terms of coverage and time synchronization.

[0160] Example of mobile terminal signal reception and data extraction implementation:

[0161] The mobile terminal uses a u-blox ZED-F9P dual-band receiver module, configured with 12 tracking channels (8 for B1I and 4 for B2a). The digital signal processing unit uses an ARM Cortex-A72 processor operating at 1.5GHz. In actual testing, the average signal acquisition time was 2.3 seconds, and the tracking stability reached 99.2%.

[0162] Example of dual-frequency pseudorange differential processing implementation:

[0163] B1I signals from 20 distributed antennas and one B2a reference signal were paired and processed. A 1-second time window was used for data synchronization, and the standard deviation of the dual-frequency pseudorange difference was 0.8 meters. A moving average filtering window length of 5 epochs was set, resulting in a 35% improvement in pseudorange difference accuracy after filtering.

[0164] Example of 3D position calculation and output:

[0165] A right-handed rectangular coordinate system was established with the central atrium as the origin. The coordinates of each antenna were measured using a Leica TS16 total station with an accuracy of 0.05 meters. The position was solved using the weighted least squares method, with the weights determined based on the signal-to-noise ratio. Kalman filtering was used to process the continuous epoch data. The state vector contains 9 parameters (3 components each for position, velocity, and acceleration), as shown in Table 5.

[0166] Table 5: Positioning Performance Verification Data

[0167] Test area Horizontal error (m) Vertical error (m) Location success rate (%) Response time (s) central atrium 0.75 1.15 98.8 1.2 East Corridor 0.92 1.48 97.5 1.4 West side shops 1.18 1.72 96.3 1.6 South side dining area 1.35 1.95 95.1 1.8 North entrance / exit 1.02 1.58 97.2 1.5

[0168] Table 5 summarizes the performance of the indoor positioning system in different areas, reflecting the distribution characteristics of the system's positioning accuracy and response capability in complex indoor environments.

[0169] Implementation results:

[0170] Through three months of continuous testing, the present invention achieved the following technical effects in this underground commercial complex:

[0171] Positioning accuracy is improved; compared to the original Wi-Fi positioning system (average error 5.8 meters), the average positioning error of this invention is reduced to 1.04 meters, an 82% improvement in accuracy. Within the 95% confidence interval, the positioning error is less than 2.5 meters. Coverage is expanded, achieving seamless coverage of 15,000 square meters, reaching a coverage rate of 99.5%, eliminating 12 positioning blind spots of the original system. System stability is enhanced; after 90 days of continuous operation, system availability reaches 99.7%, with an average fault recovery time of 3.2 minutes. Positioning success rate remains above 95% even in multipath interference environments. Deployment costs are reduced by fully utilizing existing DAS feeder networks, avoiding rewiring and saving approximately 65% ​​compared to independently deploying a UWB positioning system. User experience is improved, with an average positioning response time of 1.5 seconds, supporting simultaneous service for 500 mobile terminals. User satisfaction surveys show that navigation accuracy scores improved from 6.2 to 8.7 out of 10.

[0172] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. An indoor positioning method based on feeder multiplexing of BeiDou dual frequencies, characterized in that, Includes the following steps: The Beidou dual-frequency receiving antenna receives B1I and B2a signals, which are then separated by a dual-frequency separation filter. Automatic gain control and phase calibration are performed by a signal conditioning circuit. The separated B1I and B2a signals are fed into power amplifiers for signal power amplification, and an impedance matching network is set at the output. The B1I signal is transmitted through the existing communication feeder network and a duplexer is installed at the input end to combine it with the original communication signal for transmission. The B2a signal is transmitted to the central directional antenna through a dedicated feeder. Distributed antennas are installed along the feeder network to broadcast B1I signals indoors. Each distributed antenna is equipped with a time synchronization module to achieve network-wide synchronization via B2a signals. The mobile terminal receives dual-frequency signals, performs signal acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations. B1I and B2a observation data from the same signal source are paired and processed to calculate the difference between the two frequency pseudoranges, eliminate systematic errors, and smooth the data using a moving average filtering algorithm. A set of position calculation equations is established based on the precise pseudorange difference, and the weighted least squares method is used to solve them. The calculation results of multiple consecutive epochs are processed by Kalman filtering to output three-dimensional coordinates.

2. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The dual-frequency separation filter includes a B1I signal separation filter and a B2a signal separation filter. The center frequency of the B1I signal separation filter is 1561.098MHz, the bandwidth is 20MHz, and the out-of-band rejection is greater than 40dB. The center frequency of the B2a signal separation filter is 1176.45MHz, the bandwidth is 20MHz, and the out-of-band rejection is greater than 40dB. The separated signals are subjected to automatic gain control and phase calibration by a signal conditioning circuit.

3. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The signal power amplification includes a B1I signal power amplifier and a B2a signal power amplifier. The output power of the B1I signal power amplifier is determined according to the feeder length and the number of branches, with a typical output power of 20-30 dBm. The output power of the B2a signal power amplifier is 25-35 dBm. The power amplifier has a built-in temperature compensation circuit to maintain output power stability within the range of -20 degrees Celsius to +60 degrees Celsius.

4. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The feeder network uses low-loss coaxial cable. The B1I signal is transmitted through the feeder network of the existing communication system and a duplexer is installed at the input end to combine with the original communication signal for transmission. A power divider is set every 50 to 100 meters to distribute the B1I signal to the distributed antennas in the local area. The B2a signal is transmitted to the high-gain directional antenna through a dedicated feeder.

5. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The time synchronization module incorporates a high-stability temperature-controlled crystal oscillator with a frequency stability better than 1×10⁻⁶. -9 Synchronization accuracy is achieved through a phase-locked loop circuit, and the synchronization error is controlled within 10 nanoseconds. An indoor distributed antenna is installed every 30 to 50 meters.

6. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The digital signal processing includes a signal acquisition process that uses a fast Fourier transform algorithm to convert the time-domain signal into a frequency-domain signal and search for pseudo-random code peaks, and a signal tracking process that uses an extended Kalman filter algorithm to establish a signal state and observation model to estimate the carrier phase and pseudorange.

7. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The calculation of the dual-frequency pseudorange difference includes calculating the pseudorange difference between B1I and B2a from the same signal source, eliminating the time delay difference of the dual-frequency signal system, and using a moving average filtering algorithm to smooth the pseudorange difference of multiple epochs. The filtering window length is 5 epochs.

8. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, The position calculation adopts the weighted least squares method. The weight matrix is ​​determined according to the signal quality. Observations with high signal-to-noise ratio have higher weights. The calculation results of multiple consecutive epochs are processed by Kalman filtering to establish a motion state model. The position accuracy after filtering is better than 1 meter.

9. The indoor positioning method based on feeder multiplexing of BeiDou dual frequencies according to claim 1, characterized in that, It also includes using fiber optic transmission technology to replace coaxial cable transmission, and using a radio frequency fiber optic transmission system to convert B1I and B2a signals into optical signals for transmission, thereby reducing transmission loss and improving system transmission distance and signal quality.

10. An indoor positioning system based on feeder-multiplexed BeiDou dual-frequency, used to execute the indoor positioning method based on feeder-multiplexed BeiDou dual-frequency as described in any one of claims 1-9, characterized in that, include: The BeiDou dual-frequency signal receiving and preprocessing unit receives B1I and B2a signals through the BeiDou dual-frequency receiving antenna, performs frequency separation through a dual-frequency separation filter, and performs automatic gain control and phase calibration through a signal conditioning circuit. The signal power amplification and feeder adaptation unit sends the separated B1I and B2a signals to the power amplifier for power amplification, and sets an impedance matching network at the output end. The feeder multiplexing transmission network deployment unit transmits the B1I signal through the existing communication feeder network and combines it with the original communication signal through a duplexer installed at the input end. The B2a signal is transmitted to the central directional antenna through a dedicated feeder. The indoor distributed signal coverage unit installs distributed antennas along the feeder network to broadcast B1I signals indoors. Each distributed antenna is equipped with a time synchronization module to achieve full network synchronization via B2a signals. The mobile terminal signal receiving and data extraction unit receives dual-frequency signals, performs signal acquisition and tracking through digital signal processing, and extracts pseudorange and carrier phase observations. The dual-frequency pseudorange differential processing unit processes B1I and B2a observation data from the same signal source in pairs, calculates the difference between the two frequencies, eliminates system errors, and uses a moving average filtering algorithm for smoothing. The position calculation and output unit establishes a set of position calculation equations based on the precise pseudorange difference, solves them using the weighted least squares method, performs Kalman filtering on the calculation results of multiple consecutive epochs, and outputs three-dimensional coordinates.

Images