Indoor navigation positioning precision improving method and system based on filtering algorithm

Abstract

The invention discloses an indoor navigation positioning precision improving method and system based on a filtering algorithm, and relates to the technical field of signal processing. The method comprises the following steps: suppressing signal fluctuation and multipath interference by adopting a Bluetooth positioning technology in combination with a segmented power law regression model and Kalman filtering; ultrasonic-assisted positioning is introduced, the distance is calculated through the time difference between pulses and Bluetooth, and the anti-interference capability is improved in cooperation with temperature correction and spectral analysis; multi-mode dynamic fusion is carried out, when shielding is little, Bluetooth positioning is corrected through high-precision ultrasonic waves, when shielding is serious, Bluetooth data is smoothed through moving average, and positioning coordinates are optimized in combination with a least square method; a base station and a reference object are marked through a vector map, the position of a terminal is dynamically displayed, and positioning reliability is visually presented through an error semitransparent circle; a positioning base station array, a mobile terminal and a rear-end processing module are integrated, and low-power-consumption multi-mode fusion positioning is achieved. The method reduces the positioning error in a complex indoor environment, and is suitable for scenes such as personnel / equipment real-time tracking.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to a method and system for improving indoor navigation positioning accuracy based on a filtering algorithm. Background Art

[0002] Location-based service is the second most basic service, and its application scenarios have penetrated into all aspects of life, such as personnel search, location search, traffic management, vehicle navigation and route planning, etc. In different usage scenarios, it can also be divided into indoor and outdoor. In the relevant indoor service processes, such as navigation, object search, smart home, etc., indoor positioning is particularly critical. Accurate, fast and efficient positioning of the indoor location of the target point is the key to providing these services. The present invention mainly solves the current situation, that is, providing positioning in complex indoor environments, as well as problems such as positioning accuracy and response speed, so as to apply it to navigation. The existing technology is difficult to locate mobile phones in underground, indoor and other environments, and cannot realize location services. Summary of the Invention

[0003] In order to overcome the shortcomings and deficiencies of the above-mentioned prior art, the first purpose of the present invention is to provide a method for improving indoor navigation positioning accuracy based on a filtering algorithm; the second purpose of the present invention is to provide a system for improving indoor navigation positioning accuracy based on a filtering algorithm.

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

[0005] The indoor navigation positioning accuracy improvement method based on the filtering algorithm has the following process:

[0006] Bluetooth positioning: The mobile terminal receives Bluetooth signals and extracts the transmit power baseline value. Based on the relative attenuation ratio of the received signal strength to the baseline value, the relative distance to the base station is calculated using a piecewise power-law regression model. The distance value is then optimized using a Kalman filter. The nearest base station is selected and the plane coordinates are obtained using triangulation positioning.

[0007] Ultrasonic-assisted positioning: The mobile terminal synchronously records the arrival time of the ultrasonic and Bluetooth signals, calculates the time difference and combines it with the speed of sound to obtain the initial distance. After waveform correction and filtering of reflected waves and abnormal waves, it is weighted and fused with the Bluetooth positioning distance to suppress multipath effects.

[0008] Multi-mode fusion positioning: Dynamically switches fusion strategies based on the effectiveness of Bluetooth and ultrasonic signals. When both signals are valid, the fusion distance is used to correct the Bluetooth positioning result. When the ultrasonic signal is lost, the Bluetooth result is mainly used and combined with historical ultrasonic distances for sliding average filtering;

[0009] Coordinate conversion and visualization: Based on the map data of the back-end processing module, the mobile terminal location and positioning error range are dynamically marked.

[0010] Preferably, the step of calculating the relative distance based on the received signal strength in Bluetooth positioning includes:

[0011] Extract the 1-meter-distance approved signal strength broadcast by the base station as the transmit power reference value;

[0012] Obtain the actual Bluetooth signal strength received by the mobile terminal and calculate the relative attenuation ratio between the two;

[0013] A piecewise power law regression model is used according to the size of the relative attenuation ratio: when the ratio is less than 1, the tenth power is used to fit the near-field distance; when the ratio is greater than or equal to 1, the seven point seven hundred and ninety-five power is used in combination with the constant term calibration to fit the far-field distance.

[0014] Preferably, the steps of Kalman filter optimization include:

[0015] Using the transmit power reference value as the initial estimate, setting the initial estimate error covariance, measurement noise variance, and process noise variance;

[0016] Each time the current received signal strength is input, the prior estimate and covariance are obtained through the prediction stage, and then the Kalman gain is calculated through the update stage, and the posterior estimate and covariance are corrected. Iterative optimization is performed to smooth the signal strength fluctuations and thus optimize the distance calculation results.

[0017] Preferably, the steps of calculating and correcting the distance difference in ultrasound-assisted positioning include:

[0018] Calculate the difference between the ultrasonic wave arrival time and the Bluetooth signal arrival time, which is approximately the ultrasonic wave propagation time;

[0019] The initial distance is calculated based on a sound speed of 340 m / s corrected for ambient temperature, where the sound speed is corrected as the temperature increases according to the formula;

[0020] Spectral analysis is used to filter out reflected waves with frequency deviations exceeding ±5%, and amplitude analysis is used to filter out abnormal peaks and troughs exceeding ±3 times the standard deviation of the mean value, retaining the distance value corresponding to the main peak.

[0021] Preferably, the step of weighted fusion to suppress multipath effects includes:

[0022] The weighted average of the Bluetooth positioning distance and the ultrasonic correction distance is calculated according to the initial weight as the final distance;

[0023] The weight is dynamically adjusted according to the degree of environmental occlusion. When the occlusion is severe, the Bluetooth distance weight is increased, and when the environment is open, the ultrasonic distance weight is increased.

[0024] Preferably, the multi-mode fusion positioning strategy includes:

[0025] The fusion frequency is synchronized with the Bluetooth signal cycle and the ultrasonic signal cycle;

[0026] When both signals are valid, the weighted fusion distance is used first to correct the Bluetooth positioning coordinates;

[0027] When the ultrasonic signal is lost, the Bluetooth positioning result is combined with the most recent valid ultrasonic distance to perform sliding average filtering to maintain positioning continuity.

[0028] Preferably, the steps of coordinate conversion and visualization include:

[0029] Based on a preset base station coordinate matrix, the absolute coordinates are solved by combining distance equations from the mobile terminal to at least two base stations, where the square difference between the mobile terminal coordinates and the base station coordinates is equal to the square of the corresponding distance;

[0030] The vector map of the pre-stored indoor scene supports horizontal, vertical, flat and 3D effects, marks the base station location and reference objects, and marks the error range of the mobile terminal location with a translucent circle.

[0031] The second purpose of the present invention adopts the following technical solution:

[0032] The indoor navigation positioning accuracy improvement system based on the filtering algorithm is used for the indoor navigation positioning accuracy improvement method based on the filtering algorithm. The system includes:

[0033] Positioning base station array: consists of multiple base stations deployed at preset coordinate points indoors. Each base station integrates a low-power Bluetooth module and an ultrasonic transmitter module. Preferably, the Bluetooth module is used to transmit Bluetooth signals, and preferably, the ultrasonic transmitter module is used to transmit ultrasonic pulses.

[0034] Mobile terminal: Built-in Bluetooth receiving module, ultrasonic receiving module and microprocessor, used to receive Bluetooth signals and ultrasonic pulses transmitted by the base station, analyze signal parameters and perform distance measurement, filter optimization and positioning calculation;

[0035] Back-end processing module: stores base station coordinate matrix and indoor vector map data, receives positioning data reported by mobile terminals, and completes coordinate conversion and visualization display.

[0036] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0037] 1. The present invention combines the technical advantages of Bluetooth's strong anti-obstruction ability and ultrasonic positioning high accuracy to design a dynamic fusion strategy. When both are effective, ultrasonic waves are used to correct Bluetooth to improve accuracy. When ultrasonic waves are lost, Bluetooth plus sliding average is used to smooth fluctuations. This strategy adapts to different indoor environments, takes into account both positioning accuracy and environmental adaptability, and achieves stable positioning in complex scenarios.

[0038] 2. The present invention uses piecewise power-law regression to accurately fit the near-field / far-field signal attenuation characteristics, Kalman filtering to dynamically smooth RSSI fluctuations, and combines ultrasonic spectrum analysis to filter reflected waves, filter abnormal peaks and troughs, and extract main peaks to suppress multipath interference, signal mutations, and noise, thereby improving the stability and accuracy of distance measurement.

[0039] 3. The present invention is based on multi-base station screening and simultaneous solution of geometric equations, combined with least squares method to optimize errors and linear interpolation to improve edge accuracy, achieving meter-level positioning accuracy; combined with pre-stored vector maps and dynamic error range visualization, it intuitively displays positioning credibility, improving user navigation experience and result reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 The flowchart of the method for improving indoor navigation positioning accuracy based on the filtering algorithm of the present invention is shown;

[0042] Figure 2 The module diagram of the indoor navigation positioning accuracy improvement system based on the filtering algorithm of the present invention is shown. DETAILED DESCRIPTION

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

[0044] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0045] Example 1:

[0046] See Figure 1 As shown, the indoor navigation positioning accuracy improvement method based on the filtering algorithm of this embodiment includes a Bluetooth positioning method.

[0047] Step 1: Single point / multi-point distance measurement.

[0048] The mobile terminal parses the Data field broadcast by the base station through the Bluetooth receiving module and extracts the TxPower (the signal strength at 1 meter, which is set to a fixed value such as -65dBm when the base station is initialized).

[0049] TxPower: This is the transmit power baseline value, representing the approved signal strength at 1 meter (e.g., -65dBm). This is a fixed value set by the base station during initialization. It provides a baseline reference. When a mobile terminal is exactly 1 meter from the base station, the received RSSI should theoretically be equal to TxPower (ignoring environmental interference).

[0050] RSSI: The actual Bluetooth signal strength received by the mobile terminal (unit: dBm, negative value; larger values ​​indicate stronger signals, for example, -60dBm is stronger than -70dBm). Because signals may attenuate during propagation due to factors such as distance, obstacles, and multipath reflections, the actual RSSI is usually less than TxPower (when the distance is > 1 meter) or occasionally greater than TxPower (when the distance is < 1 meter and the signal is not significantly attenuated).

[0051] Calculate the relative distance to the base station based on the RSSI attenuation model:

[0052] S11. Calculate the ratio value: the normalized signal attenuation ratio.

[0053] ; Divide the actual received signal strength RSSI by the reference strength TxPower at 1 meter to get a relative attenuation ratio. For example: if TxPower = -65dBm, when the mobile terminal is 2 meters away from the base station, the RSSI may attenuate to -70dBm, then (>1); if the distance is 0.5 meters (very close), the RSSI may be -60dBm, then (<1).

[0054] S12. Piecewise power-law regression: fitting the attenuation characteristics of different distances.

[0055] The attenuation of wireless signals is not linear, but rather follows a "power law" as distance increases. Furthermore, the attenuation patterns differ between the near field (distance < 1 meter) and the far field (distance > 1 meter). Therefore, segmented calculations are required:

[0056] When ratio < 1 (usually corresponding to distance < 1 meter), the distance ;

[0057] At this time, the signal attenuation is relatively slow (because the distance is very close, the attenuation is not obvious), use Fitting near-field characteristics. For example, if ratio = 0.923 (distance 0.5 meters), then d ≈ (0.923) ^ 10 ≈ 0.43 meters (close to the actual distance).

[0058] When ratio ≥ 1 (usually corresponding to distance ≥ 1 meter), the distance .

[0059] At this point, signal attenuation is more pronounced. We use the power of 7.7095 to fit the far-field characteristics, and the constant term of 0.111 is used to calibrate for close-range deviation. For example, if ratio = 1.077 (distance 2 meters), then d ≈ 0.89976 × (1.077) ^ 7.7095 + 0.111 ≈ 1.98 meters (close to the actual distance).

[0060] Step 2: Kalman filter optimization.

[0061] In order to smooth RSSI fluctuations, a Kalman filter is introduced with the following parameter settings:

[0062] Initial estimate : Initial guess of the “true RSSI” when filtering starts.

[0063] Initial estimate error covariance : describes the initial estimate The larger the value, the higher the uncertainty.

[0064] Measurement noise variance R=0.1: Describes the "noise level" of the RSSI measurement itself (the larger the value, the less reliable the measurement).

[0065] Process noise variance Q = 0.008: describes the "uncertainty of dynamic changes" of the system (for example, the RSSI change rate caused by the movement of the mobile terminal; the larger the value, the more drastic the change).

[0066] Update rule: Each time the measured value (current RSSI) is used as input, the estimated value is calculated and covariance , iteratively optimize the distance estimation as follows:

[0067] Prediction stage: Based on the estimation of the previous moment, guess the current state. Assume that the optimized RSSI estimation value has been obtained at the previous moment (k-1) and its uncertainty , then: predict the estimated value of the current RSSI (prior estimate): (Because the terminal moves slowly, the default RSSI change is small, so the previous value is directly used for prediction); prediction uncertainty (prior covariance): (Adding process noise Q, the uncertainty increases slightly because the system may have slight changes).

[0068] Update phase: Combine the current measurement value and correct the prediction result when the mobile terminal receives the new RSSI measurement value (the actual signal strength of the current measurement), use the measured value to correct the predicted value: calculate the Kalman gain : Weighing the confidence of the predicted value and the confidence of the measured value ( The larger it is, the more trust is placed on the measured value; the smaller it is, the more trust is placed on the predicted value). The formula is: ; (Because R=0.1, Q=0.008, Will tend to trust the measurement value, but not over-rely); get the optimized current RSSI estimate (posterior estimate): ; (Use the difference between the measured value and the predicted value, press Proportional correction forecast); Update uncertainty: ; (after correction, the uncertainty of the estimate is reduced).

[0069] Iterative optimization: Let RSSI go from "fluctuating" to "stable" each time a new RSSI measurement value is received , repeat the "predict → update" process and output the optimized For example, the original RSSI measurement value may fluctuate: -65→-70→-63→-68 (unit: dBm); after Kalman filtering, the output The possible smoothing is: -65→-67→-66→-67 (the fluctuation range is significantly reduced). The original RSSI will be replaced and the ratio and distance d in step 1 will be recalculated to make the distance calculation more stable.

[0070] Step 3: Base station screening and positioning.

[0071] According to the sorting of the filtered distance values, the nearest 3 or more base stations are selected (if there are only 2, two-point linear positioning is used; if there is only 1, single-point presence detection is used), and the plane coordinates of the mobile terminal are calculated by the triangulation positioning method.

[0072] The coordinates of base station A are (x1, y1), and the distance from the mobile terminal to A is d1;

[0073] The coordinates of base station B are (x2, y2), and the distance from the mobile terminal to B is d2;

[0074] The coordinates of base station C are (x3, y3), and the distance between the mobile terminal and C is d3. Then the coordinates of the mobile terminal (x, y) must satisfy the following three circle equations at the same time:

[0075]

[0076] Solve the simultaneous equations to get (x,y).

[0077] The beneficial effects of this embodiment are as follows: precise fitting of near-field / far-field signal attenuation characteristics through piecewise power-law regression, combined with dynamic smoothing of RSSI fluctuations by Kalman filtering (measurement noise variance R=0.1, process noise Q=0.008), effectively suppressing multipath interference and signal mutations, and improving distance measurement stability; and enhancing positioning robustness in complex indoor environments through multi-base station screening (≥3 nearest base stations) and solving triangulated positioning equations, ultimately achieving dynamic navigation positioning with meter-level accuracy, which is suitable for scenarios such as real-time tracking of personnel / equipment.

[0078] Example 2:

[0079] See Figure 2 As shown, the indoor navigation positioning accuracy improvement method based on the filtering algorithm of this embodiment includes an ultrasonic assisted positioning method.

[0080] Step 1: Send and receive sound wave pulses.

[0081] The base station sends 16.5kHz~23.5kHz ultrasonic pulses at fixed intervals (100~1000ms, accuracy is inversely proportional to the interval), and the mobile terminal records the arrival time of the sound waves through the ultrasonic receiving module At the same time, the Bluetooth module records the arrival time of the Bluetooth signal from the corresponding base station (Synchronize timestamps through the Data field).

[0082] Step 2: Distance difference calculation and correction.

[0083] Calculate the time difference between the sound wave and the Bluetooth signal ; Due to the extremely fast propagation speed of Bluetooth signals (radio waves) (≈3×10 8 m / s), its propagation time is negligible, so ≈The propagation time of ultrasonic waves from the base station to the terminal.

[0084] Based on the speed of sound (340m / s, ambient temperature correction) Calculate distance Temperature correction: The speed of sound increases with increasing temperature (speed of sound ≈ 331 + 0.6 × temperature). If the ambient temperature deviates significantly from the default value (e.g. 20°C), a temperature sensor must be used for real-time correction. , to avoid distance calculation errors.

[0085] Waveform correction: Filter out reflected waves (bands with frequency deviation exceeding ±5%) and abnormal peaks and troughs (bands with amplitude exceeding ±3σ of the average value) through spectrum analysis, and retain the corresponding peaks of the main wave. .

[0086] Waveform correction cleans the signal by:

[0087] Spectral analysis filters reflected waves: Due to the long propagation path, the frequency of reflected waves will shift due to the Doppler effect (usually by more than ±5%). Fast Fourier transform (FFT) analysis of the sound wave spectrum is used to filter out bands with frequency shifts exceeding ±5%, retaining the main peak (the direct path ultrasound).

[0088] Abnormal peak and trough filtering: Noise or interference can cause abnormal signal amplitudes (such as sudden spikes or dips). The system calculates the mean and standard deviation (σ) of the signal amplitude and filters out peaks and troughs exceeding ±3σ of the mean, retaining only signal segments that conform to normal propagation patterns.

[0089] Main peak extraction: In the corrected waveform, the peak with the strongest energy and the earliest arrival time is the main wave (ultrasound of the direct path), and its corresponding Δt is the true propagation time, which is used to calculate .

[0090] Step 3: Multipath effect suppression.

[0091] Distance combined with Bluetooth positioning Distance from ultrasonic wave , take the weighted average as the final distance: ; (The weight is dynamically adjusted according to the degree of ambient occlusion).

[0092] The beneficial effects of this embodiment include: positioning through the fusion of ultrasonic and Bluetooth time differences, using Bluetooth high-precision time synchronization to assist in ultrasonic propagation time measurement, combining temperature correction (dynamic adaptation of sound speed to the environment) and spectrum analysis to filter reflected waves (frequency deviation exceeding ±5% of the band) and abnormal peaks and troughs (amplitude exceeding ±3σ), retaining the main peak, and effectively suppressing multipath interference; and then through the weighted averaging of Bluetooth and ultrasonic distances (the weights dynamically adapt to the occlusion environment), further reducing the error of single signals, and improving the distance measurement stability and positioning accuracy in complex indoor scenarios.

[0093] Example 3:

[0094] The indoor navigation positioning accuracy improvement method based on the filtering algorithm of this embodiment includes a multi-mode fusion positioning method.

[0095] Bluetooth positioning (strong resistance to occlusion but limited accuracy) and ultrasonic positioning (high accuracy but susceptible to occlusion) have naturally complementary advantages. A dynamic fusion strategy is designed to intelligently combine the ranging results of these two technologies in different environments, ensuring both positioning accuracy (using ultrasonic waves) and environmental adaptability (using Bluetooth), ultimately delivering stable and reliable location information.

[0096] Fusion strategy:

[0097] 1. When both Bluetooth and ultrasonic signals are valid: Ultrasonic is used first to correct Bluetooth. Logic: Ultrasonic distance measurement is more accurate (time difference measurement error is small), but it is susceptible to multipath interference; Bluetooth distance measurement accuracy is slightly lower, but it has strong anti-blocking capabilities. When both are valid, it means that there is little environmental occlusion (ultrasonic is not lost), then the distance after ultrasonic optimization is used. Correcting Bluetooth positioning results can significantly improve accuracy. Correction method: For example, Bluetooth positioning alone might calculate the terminal's location at (x1, y1), while ultrasonic correction fine-tunes the coordinates to (x2, y2), making the result closer to the actual location (taking advantage of the high precision of ultrasonic waves).

[0098] 2. When the ultrasonic signal is lost (e.g., severely blocked): Bluetooth is used as the primary filter + sliding average filter. Logic: Ultrasonic signals are easily lost in densely blocked environments (e.g., walls or human bodies). If you completely rely on Bluetooth, RSSI fluctuations may cause positioning to jump. Sliding average of "Bluetooth result + the most recent valid ultrasonic distance" can smooth out fluctuations and maintain positioning continuity. Sliding average principle: Assume that the last valid distance before the ultrasonic signal is lost is , the subsequent Bluetooth distance measurement is The fusion distance is (Gradually reduce the impact of historical data and avoid lags).

[0099] Coordinate transformation: Using the known base station coordinates and the measured relative distance, the absolute position of the terminal is solved through geometric equations. The specific steps are as follows:

[0100] S31. Preset base station coordinate matrix: The system pre-stores the absolute coordinates of all base stations (such as base station A (x1, y1), base station B (x2, y2), etc., usually based on the indoor local coordinate system or WGS-84 / GCJ-02 coordinate system).

[0101] S32. Solve the simultaneous equations: If the distance from the terminal to base station A is D1 and the distance to base station B is D2, then the terminal coordinates (x, y) must satisfy the equations of both circles:

[0102] ;

[0103] The simultaneous solution can obtain (x, y) (if there are 3 or more base stations, the error can be optimized by the least squares method).

[0104] S33, Linear interpolation assistance: When the base station density is high, linear interpolation of the coordinates and distances of adjacent base stations can further simplify the calculation and improve the positioning accuracy in edge areas (such as the intersection of base station signal coverage).

[0105] The beneficial effects of this embodiment include: dynamic fusion positioning of Bluetooth and ultrasound, combining the advantages of both (Bluetooth anti-occlusion, ultrasound high precision), and intelligent switching strategies in different environments: when both are effective, ultrasound is used to correct Bluetooth to improve accuracy; when ultrasound is lost, Bluetooth plus sliding average is used to smooth fluctuations and maintain positioning continuity; combined with geometric equation solving and least squares method to optimize errors, linear interpolation improves edge accuracy, and ultimately achieves high-precision, highly adaptable and stable positioning in complex indoor scenes.

[0106] Example 4:

[0107] The indoor navigation positioning accuracy improvement method based on the filtering algorithm of this embodiment includes coordinate conversion and visualization.

[0108] Mapping: Pre-store vector maps of indoor scenes (supporting landscape, portrait, flat, and 3D effects), annotating base station locations and surrounding reference objects (such as doors, pillars, and signboards); dynamically mark the location of mobile terminals, and indicate the error range with a translucent circle (the radius is the current positioning error; for example, the circle transparency is 80% when the error is <0.5m).

[0109] Results verified: Through self-developed testing software, actual measurements in complex environments (including obstructions and voltage fluctuations) have reduced positioning error from 0.5-1m for Bluetooth positioning alone to <0.5m, meeting the needs of indoor pedestrian navigation.

[0110] The beneficial effects of this embodiment include: enhancing environmental perception through pre-stored vector maps (supporting landscape / portrait / 3D) and reference object annotations; dynamically marking the terminal position and visualizing the error range (semi-transparent circle, with radius associated with error) to intuitively demonstrate positioning reliability; and actually reducing the positioning error from 0.5~1m to <0.5m in complex environments, meeting the accuracy requirements of indoor pedestrian navigation, and improving the user navigation experience and the reliability of positioning results.

[0111] Example 5:

[0112] The indoor navigation positioning accuracy improvement system based on the filtering algorithm of this embodiment includes: a positioning base station array, a mobile terminal and a backend processing module.

[0113] Positioning base station array: This consists of several base stations deployed indoors at preset coordinate points (such as the WGS-84 or GCJ-02 coordinate system with meter-level accuracy). Each base station integrates a low-power Bluetooth module (transmitting 2.4GHz Bluetooth signals) and an ultrasonic transmitter module (transmitting 16.5kHz to 23.5kHz sound wave pulses).

[0114] Mobile terminal: Built-in Bluetooth receiving module, ultrasonic receiving module and microprocessor, used to receive base station signals and perform positioning calculations;

[0115] Back-end processing module: stores base station coordinate matrix and map layer data, receives positioning data reported by mobile terminals and completes coordinate conversion and visualization.

[0116] The beneficial effects of this embodiment include: the system uses Bluetooth and ultrasonic multimodal fusion positioning, combined with a filtering algorithm to effectively suppress indoor multipath interference and improve positioning accuracy to the meter level; back-end coordinate conversion and map data correction reduce cumulative errors and achieve precise coordinate matching; low-power Bluetooth design reduces base station energy consumption and extends battery life; real-time positioning data visualization enhances the user navigation experience and is suitable for high-precision positioning needs in complex indoor scenarios.

[0117] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

[0118] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for improving indoor navigation positioning accuracy based on a filtering algorithm, characterized in that: The method flow is as follows: Bluetooth positioning: The mobile terminal receives Bluetooth signals and extracts the transmit power baseline value. Based on the relative attenuation ratio of the received signal strength to the baseline value, the relative distance to the base station is calculated using a piecewise power-law regression model. The distance value is then optimized using a Kalman filter. The nearest base station is selected and the plane coordinates are obtained using triangulation positioning. Ultrasonic-assisted positioning: The mobile terminal synchronously records the arrival time of the ultrasonic and Bluetooth signals, calculates the time difference and combines it with the speed of sound to obtain the initial distance. After waveform correction and filtering of reflected waves and abnormal waves, it is weighted and fused with the Bluetooth positioning distance to suppress multipath effects. Multi-mode fusion positioning: Dynamically switches fusion strategies based on the effectiveness of Bluetooth and ultrasonic signals. When both signals are valid, the fusion distance is used to correct the Bluetooth positioning result. When the ultrasonic signal is lost, the Bluetooth result is mainly used and combined with historical ultrasonic distances for sliding average filtering; Coordinate conversion and visualization: Based on the map data of the back-end processing module, the mobile terminal location and positioning error range are dynamically marked.

2. The method for improving indoor navigation positioning accuracy based on filtering algorithm according to claim 1, characterized in that: The step of calculating the relative distance based on the received signal strength in the Bluetooth positioning includes: Extract the 1-meter-distance approved signal strength broadcast by the base station as the transmit power reference value; Obtain the actual Bluetooth signal strength received by the mobile terminal and calculate the relative attenuation ratio between the two; A piecewise power law regression model is used according to the size of the relative attenuation ratio: when the ratio is less than 1, the tenth power is used to fit the near-field distance; when the ratio is greater than or equal to 1, the seven point seven hundred and ninety-five power is used in combination with the constant term calibration to fit the far-field distance.

3. The method for improving indoor navigation positioning accuracy based on filtering algorithm according to claim 1, characterized in that: The steps of Kalman filter optimization include: Using the transmit power reference value as the initial estimate, setting the initial estimate error covariance, measurement noise variance, and process noise variance; Each time the current received signal strength is input, the prior estimate and covariance are obtained through the prediction stage, and then the Kalman gain is calculated through the update stage, and the posterior estimate and covariance are corrected. Iterative optimization is performed to smooth the signal strength fluctuations and thus optimize the distance calculation results.

4. The method for improving indoor navigation positioning accuracy based on filtering algorithm according to claim 1, characterized in that: The ultrasonic assisted positioning specifically includes: Calculate the difference between the ultrasonic wave arrival time and the Bluetooth signal arrival time, which is approximately the ultrasonic wave propagation time; The initial distance is calculated based on a sound speed of 340 m / s corrected for ambient temperature, where the sound speed is corrected as the temperature increases according to the formula; Spectral analysis is used to filter out reflected waves with frequency deviations exceeding ±5%, and amplitude analysis is used to filter out abnormal peaks and troughs exceeding ±3 times the standard deviation of the mean value, retaining the distance value corresponding to the main peak.

5. The method for improving indoor navigation positioning accuracy based on filtering algorithm according to claim 1, characterized in that: The step of weighted fusion to suppress multipath effect includes: The weighted average of the Bluetooth positioning distance and the ultrasonic correction distance is calculated according to the initial weight as the final distance; The weight is dynamically adjusted according to the degree of environmental occlusion. When the occlusion is severe, the Bluetooth distance weight is increased, and when the environment is open, the ultrasonic distance weight is increased.

6. The method for improving indoor navigation positioning accuracy based on filtering algorithm according to claim 1, characterized in that: The multi-mode fusion positioning strategy includes: The fusion frequency is synchronized with the Bluetooth signal cycle and the ultrasonic signal cycle; When both signals are valid, the weighted fusion distance is used first to correct the Bluetooth positioning coordinates; When the ultrasonic signal is lost, the Bluetooth positioning result is combined with the most recent valid ultrasonic distance to perform sliding average filtering to maintain positioning continuity.

7. The method for improving indoor navigation positioning accuracy based on filtering algorithm according to claim 1, characterized in that: The steps of coordinate conversion and visualization include: Based on a preset base station coordinate matrix, the absolute coordinates are solved by combining distance equations from the mobile terminal to at least two base stations, where the square difference between the mobile terminal coordinates and the base station coordinates is equal to the square of the corresponding distance; The vector map of the pre-stored indoor scene supports horizontal, vertical, flat and 3D effects, marks the base station location and reference objects, and marks the error range of the mobile terminal location with a translucent circle.

8. A system for improving indoor navigation positioning accuracy based on a filtering algorithm, used in the method for improving indoor navigation positioning accuracy based on a filtering algorithm as claimed in claim 1, characterized in that: The system comprises: Positioning base station array: consists of multiple base stations deployed at preset coordinate points indoors. Each base station integrates a low-power Bluetooth module and an ultrasonic transmitter module. The Bluetooth module is used to transmit Bluetooth signals, and the ultrasonic transmitter module is used to transmit ultrasonic pulses. Mobile terminal: Built-in Bluetooth receiving module, ultrasonic receiving module and microprocessor, used to receive Bluetooth signals and ultrasonic pulses transmitted by the base station, analyze signal parameters and perform distance measurement, filter optimization and positioning calculation; Back-end processing module: stores base station coordinate matrix and indoor vector map data, receives positioning data reported by mobile terminals, and completes coordinate conversion and visualization display.

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