A Method and System for Improving Indoor Navigation and Positioning Accuracy Based on Filtering Algorithms

Abstract

This invention discloses a method and system for improving indoor navigation and positioning accuracy based on filtering algorithms, belonging to the field of signal processing technology. The method includes: employing Bluetooth positioning technology, combined with a piecewise power-law regression model and Kalman filtering to suppress signal fluctuations and multipath interference; introducing ultrasonic-assisted positioning, calculating distance through the pulse-to-Bluetooth time difference, and combining temperature correction and spectrum analysis to improve anti-interference capability; multimodal dynamic fusion, using high-precision ultrasonic correction for Bluetooth positioning when there is little obstruction, and smoothing Bluetooth data through moving averages when there is severe obstruction, combined with least squares method to optimize positioning coordinates; marking base stations and reference objects on a vector map, dynamically displaying the terminal position and visually presenting positioning reliability with semi-transparent error circles; integrating a positioning base station array, mobile terminal, and back-end processing module to achieve low-power multimodal fusion positioning. This method reduces positioning errors in complex indoor environments and is suitable for scenarios such as real-time tracking of personnel / equipment.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a method and system for improving indoor navigation and positioning accuracy based on filtering algorithms. Background Technology

[0002] Location services are the second most fundamental service, with applications permeating all aspects of life, such as people search, location finding, traffic management, vehicle navigation, and route planning. Depending on the usage scenario, they can be divided into indoor and outdoor types. In indoor services such as navigation, item finding, and smart home integration, indoor positioning is particularly crucial. Accurate, fast, and efficient location of the target point within the indoor environment is key to providing these services effectively. This invention primarily addresses the current situation, namely, providing positioning in complex indoor environments, and resolving issues related to positioning accuracy and response speed, thereby applying it to navigation. Existing technologies struggle to locate mobile phones and provide location services in underground or indoor environments. Summary of the Invention

[0003] In order to overcome the shortcomings and deficiencies of the existing technology, the first objective of this invention is to provide a method for improving indoor navigation and positioning accuracy based on filtering algorithms; the second objective of this invention is to provide an indoor navigation and positioning accuracy improvement system based on filtering algorithms.

[0004] The first objective of this invention is achieved through the following technical solution:

[0005] The method for improving indoor navigation and positioning accuracy based on filtering algorithms is as follows:

[0006] Bluetooth positioning: The mobile terminal receives Bluetooth signals, extracts the transmit power reference value, calculates the relative distance to the base station based on the relative attenuation ratio of the received signal strength to the reference value, and combines a piecewise power law regression model. The distance value is then optimized by Kalman filtering, and the nearest base station is selected to obtain the planar coordinates through triangulation.

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

[0008] Multi-mode fusion positioning: The fusion strategy is dynamically switched according to the validity 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 used as the main result and combined with the historical ultrasonic distance for moving average filtering.

[0009] Coordinate transformation and visualization: Based on map data from the backend processing module, dynamically mark the location of mobile terminals and the positioning error range.

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

[0011] The signal strength at 1 meter from the base station broadcast is used as the reference value for transmission power.

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

[0013] Based on the relative attenuation ratio, a piecewise power-law regression model is adopted: when the ratio is less than 1, the near-field distance is fitted using the power of 10; when the ratio is greater than or equal to 1, the far-field distance is fitted using the power of 7.7095 combined with a constant term.

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

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

[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 to correct the posterior estimate and covariance. Iterative optimization is performed to smooth signal strength fluctuations, thereby optimizing the distance calculation results.

[0017] Preferably, the steps for calculating and correcting distance differences in ultrasonic-assisted positioning include:

[0018] Calculate the difference between the arrival time of the ultrasonic wave and the arrival time of the Bluetooth signal; this difference is approximately the propagation time of the ultrasonic wave.

[0019] The initial distance is calculated based on the sound velocity of 340 m / s corrected for ambient temperature, where the sound velocity is corrected for temperature increase according to a formula.

[0020] Spectrum analysis filters out reflected waves with frequency offsets exceeding ±5%, amplitude analysis filters out abnormal peaks and troughs exceeding ±3 standard deviations of the average value, and retains the distance value corresponding to the main peak.

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

[0022] The final distance is calculated by weighting the Bluetooth positioning distance and the ultrasonically corrected distance according to the initial weights.

[0023] The weights are dynamically adjusted based on the degree of environmental obstruction. When the obstruction is severe, the weight of Bluetooth distance is increased, and when the environment is open, the weight of ultrasonic distance is increased.

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

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

[0026] When both signals are valid, the weighted and fused distance-corrected Bluetooth positioning coordinates shall be used first.

[0027] When the ultrasonic signal is lost, the Bluetooth positioning result is used as a basis, and a moving average filter is applied in combination with the most recent valid ultrasonic distance to maintain positioning continuity.

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

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

[0030] Pre-stored vector maps of indoor scenes, supporting landscape, portrait, 2D and 3D effects, marking base station locations and reference objects, and marking the error range of mobile terminal locations with semi-transparent circles.

[0031] The second objective of this invention is achieved through the following technical solution:

[0032] An indoor navigation and positioning accuracy improvement system based on filtering algorithms is provided. The system includes:

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

[0034] Mobile terminal: It has a built-in Bluetooth receiver module, an ultrasonic receiver module and a microprocessor, which are used to receive Bluetooth signals and ultrasonic pulses transmitted by the base station, analyze signal parameters and perform distance measurement, filtering optimization and positioning calculation;

[0035] Backend processing module: Stores base station coordinate matrix and indoor vector map data, receives location data reported by mobile terminals, and completes coordinate transformation 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. This invention combines the advantages of Bluetooth's strong anti-blocking capability and ultrasonic positioning's high accuracy, and designs a dynamic fusion strategy. When both are effective, ultrasonic correction is used to improve Bluetooth accuracy. When ultrasonic is lost, Bluetooth is used with a moving average to smooth fluctuations. This adapts to different indoor environments, balances positioning accuracy and environmental adaptability, and achieves stable positioning in complex scenarios.

[0038] 2. This invention accurately fits the attenuation characteristics of near-field / far-field signals through piecewise power-law regression, dynamically smooths RSSI fluctuations through Kalman filtering, and combines ultrasonic spectrum analysis to filter reflected waves, filter abnormal peaks and troughs, and extract the main peak, thereby suppressing multipath interference, signal abrupt changes, and noise, and improving the stability and accuracy of distance measurement.

[0039] 3. This invention is based on multi-base station screening and simultaneous solution of geometric equations, combined with least squares method to optimize error 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 the positioning reliability, improving the user navigation experience and the reliability of the results. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart of the indoor navigation and positioning accuracy improvement method based on filtering algorithm of the present invention is shown;

[0042] Figure 2 The block diagram of the indoor navigation and positioning accuracy improvement system based on the filtering algorithm of the present invention is shown. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of exemplary embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0045] Example 1:

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

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

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

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

[0050] RSSI: The actual Bluetooth signal strength received by the mobile terminal (unit: dBm, negative value; the larger the value, the stronger the signal, such as -60dBm is stronger than -70dBm). Since the signal will be attenuated during propagation due to distance, obstacles, multipath reflection, etc., 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, 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] The relative attenuation ratio (ratio) is obtained by dividing the actual received signal strength (RSSI) by the reference strength (TxPower) at 1 meter. For example, if TxPower = -65dBm, when the mobile terminal is 2 meters away from the base station, the RSSI may attenuate to -70dBm. (>1); If the distance is 0.5 meters (very close), the RSSI may be -60 dBm, then (<1).

[0054] S12, Piecewise Power-Law Regression: Fitting the decay characteristics at different distances.

[0055] The attenuation of wireless signals is not linear, but rather exhibits a power-law attenuation with increasing distance, and 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 a distance < 1 meter), the distance ;

[0057] At this point, the signal attenuation is relatively gradual (due to the extremely close distance, the attenuation is not significant), using Fit the near-field characteristics. For example: 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 a distance ≥ 1 meter), distance .

[0059] At this point, the signal attenuation is more significant. The far-field characteristics are fitted using a power of 7.7095, with a constant term of 0.111 used to calibrate the near-field 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] To smooth RSSI fluctuations, a Kalman filter is introduced, with the following parameter settings:

[0062] Initial estimate : The initial guess of the "true RSSI" at the start of filtering.

[0063] Initial estimation error covariance : Describe the initial estimate The "uncertainty" (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 (such as the rate of change of RSSI caused by the movement of mobile terminals; the larger the value, the more drastic the change).

[0066] Update rule: Each time, the estimated value is calculated using the measured value (current RSSI) as input. Covariance The distance estimation is iteratively optimized as follows:

[0067] Prediction Phase: Based on the estimate from the previous time step, guess the current state. Assume that the optimized RSSI estimate was obtained at the previous time step (k-1). and its uncertainty Then: Predict the estimated value of the current RSSI (prior estimate): (Due to the slow movement of the terminal, the RSSI change is assumed to be small, so the previous time value is used directly for prediction); Prediction uncertainty (prior covariance): (The addition of process noise Q slightly increases uncertainty due to the possibility of minor system variations.)

[0068] Update phase: Based on the current measurements, the prediction results are corrected when the mobile terminal receives new RSSI measurements. After determining the actual signal strength, correct the predicted value using the measured value: calculate the Kalman gain. : Balancing the "reliability of predicted values" and the "reliability of measured values" The larger the value, the more trust the measured value; the smaller the value, the more trust the predicted value. The formula is: (Because R=0.1, Q=0.008,) (It will be biased towards trust measurements, but not overly reliant on them); This yields the optimized current RSSI estimate (posterior estimate): ; (Use the difference between the measured value and the predicted value, according to (Proportional adjustment to forecasts); Updated uncertainty: (After the correction, the uncertainty of the estimate is reduced).

[0069] Iterative optimization: Allowing RSSI to transition from "fluctuation" to "stability" each time a new RSSI measurement is received. Then repeat the "prediction → update" process to output the optimized result. For example, the raw RSSI measurement may fluctuate: -65→-70→-63→-68 (in dBm); after Kalman filtering, the output... The possible smoothing pattern is: -65→-67→-66→-67 (with a significantly reduced fluctuation range). This smoothed result... It will replace the original RSSI and recalculate the ratio and distance d in step one, thus making the distance calculation more stable.

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

[0071] Based on the filtered distance values, select the ≥3 closest base stations (if there are only 2, use two-point straight-line positioning; if there is only 1, use single-point presence detection), and calculate the planar coordinates of the mobile terminal using the triangulation 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 from the mobile terminal to C is d3. Then the coordinates (x, y) of the mobile terminal must simultaneously satisfy the equations of the following three circles:

[0075]

[0076] The equations can be solved simultaneously to obtain (x, y).

[0077] The beneficial effects of this embodiment are as follows: by accurately fitting the near-field / far-field signal attenuation characteristics through piecewise power-law regression, and combining Kalman filtering to dynamically smooth RSSI fluctuations (measurement noise variance R=0.1, process noise Q=0.008), multipath interference and signal abrupt changes are effectively suppressed, and the stability of distance measurement is improved; furthermore, by using multi-base station screening (≥3 nearest base stations) and solving the simultaneous equations of triangulation, the positioning robustness in complex indoor environments is enhanced, and finally, dynamic navigation and positioning with meter-level accuracy is achieved, which is suitable for scenarios such as real-time tracking of personnel / equipment.

[0078] Example 2:

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

[0080] Step 1: Transmission and reception of acoustic pulses.

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

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

[0083] Calculate the time difference between sound waves and Bluetooth signals Because Bluetooth signals (radio waves) propagate extremely fast (≈3×10⁻⁶), 8 Its propagation time is negligible (m / s), therefore ≈ The propagation time of ultrasound 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℃), real-time correction via a temperature sensor is required. To avoid distance calculation errors.

[0085] Waveform correction: Spectrum analysis is used to filter reflected waves (bands with frequency shifts exceeding ±5%) and abnormal peaks and troughs (bands with amplitudes exceeding the average ±3σ), while retaining the peaks corresponding to the main wave. .

[0086] Waveform correction cleans the signal using the following methods:

[0087] Spectrum analysis filtering of reflected waves: Due to their long propagation path, reflected waves will have their frequencies shifted by the Doppler effect (typically by more than ±5%). By analyzing the sound wave spectrum using Fast Fourier Transform (FFT), bands with frequency shifts exceeding ±5% are filtered out, retaining the main peak (ultrasound from the direct path).

[0088] Abnormal peak / valley filtering: Noise or interference may cause abnormal signal amplitude (such as sudden spikes or troughs). Calculate the average and standard deviation (σ) of the signal amplitude, filter out peaks / valleys with amplitudes exceeding the average ±3σ, and retain only signal segments that conform to normal propagation patterns.

[0089] Main peak extraction: In the corrected waveform, the peak with the strongest energy and earliest arrival time is the main wave (ultrasound in the direct path). Its corresponding Δt is the true propagation time, used for calculation. .

[0090] Step 3: Suppression of multipath effects.

[0091] Distance combined with Bluetooth positioning Distance from ultrasound The weighted average is taken as the final distance: (The weights are dynamically adjusted based on the degree of environmental occlusion).

[0092] The beneficial effects of this embodiment are as follows: by using ultrasonic and Bluetooth time difference fusion positioning, Bluetooth high-precision time synchronization is used to assist in the calculation of ultrasonic propagation time. Combined with temperature correction (dynamic adaptation of sound speed to the environment) and spectrum analysis to filter reflected waves (frequency deviation exceeding ±5% band) and abnormal peaks and troughs (amplitude exceeding ±3σ), the main peak is retained, effectively suppressing multipath interference. Furthermore, by using Bluetooth and ultrasonic distance weighted averaging (weight dynamically adapted to the obstruction environment), the error of a single signal is further reduced, improving the stability of distance calculation and positioning accuracy in complex indoor scenarios.

[0093] Example 3:

[0094] The indoor navigation and positioning accuracy improvement method based on filtering algorithms in this embodiment includes a multi-mode fusion positioning method.

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

[0096] Fusion strategy:

[0097] 1. When both Bluetooth and ultrasonic signals are valid: Prioritize ultrasonic correction of Bluetooth signals. The rationale is that ultrasonic ranging has higher accuracy (smaller time difference measurement error), but is more susceptible to multipath reflection interference; Bluetooth ranging accuracy is slightly lower, but it has stronger resistance to obstruction. When both are valid, it indicates minimal environmental obstruction (ultrasonic signals are not lost), and the distance optimized by ultrasonic is used. Correcting Bluetooth positioning results can significantly improve accuracy. Correction method: For example, Bluetooth positioning alone might calculate the terminal as (x1, y1), while ultrasonic correction fine-tunes the coordinates to (x2, y2), making the result closer to the actual location (utilizing the high precision of ultrasound).

[0098] 2. When the ultrasonic signal is lost (e.g., due to severe obstruction): Bluetooth as the primary signal + moving average filtering logic is based on the fact that ultrasonic signals are easily lost in scenarios with dense obstruction (such as walls or people). In such cases, relying solely on Bluetooth may cause location jumps due to RSSI fluctuations. A moving average of "Bluetooth result + most recent effective ultrasonic distance" smooths out fluctuations and maintains location continuity. Moving average principle: Assuming the last effective distance before the ultrasonic signal was lost was... Subsequent Bluetooth ranging is The fusion distance is (Gradually reduce the influence of historical data to avoid lag).

[0099] Coordinate transformation: Using known base station coordinates and measured relative distances, the absolute position of the terminal is solved through geometric equations. Specific steps:

[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. Solving 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 simultaneously satisfy the equations of two circles:

[0102] ;

[0103] Solving the system of equations simultaneously yields (x, y) (if there are 3 or more base stations, the error can be optimized using the least squares method).

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

[0105] The beneficial effects of this embodiment are as follows: By dynamically fusing Bluetooth and ultrasonic positioning, combining the advantages of both (Bluetooth anti-blocking and ultrasonic high precision), a smart switching strategy is implemented in different environments: when both are effective, ultrasonic correction is used to improve Bluetooth accuracy; when ultrasonic is lost, Bluetooth is used with a moving average to smooth fluctuations and maintain positioning continuity; combined with solving geometric equations and using the least squares method to optimize errors, linear interpolation improves edge accuracy, and finally, high-precision and highly adaptable stable positioning is achieved in complex indoor scenarios.

[0106] Example 4:

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

[0108] Map drawing: Pre-store vector maps of indoor scenes (supporting landscape, portrait, 2D, and 3D effects), mark the location of base stations and surrounding reference objects (such as doors, pillars, and signs); dynamically mark the location of mobile terminals, with the error range represented by a semi-transparent circle (the radius is the current positioning error, such as 80% transparency when <0.5m).

[0109] Performance verification: Through self-developed testing software, the positioning error was reduced from 0.5~1m for Bluetooth positioning alone to <0.5m in a complex environment (including obstructions and voltage fluctuations), meeting the needs of indoor pedestrian navigation.

[0110] The beneficial effects of this embodiment are as follows: by pre-stored vector maps (supporting horizontal / vertical / 3D) and reference object markings, environmental perception is enhanced; the terminal position is dynamically marked and the error range is visualized (semi-transparent circles, the radius of which is related to the error), intuitively showing the reliability of the positioning; in actual tests, the positioning error is reduced 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 the positioning results.

[0111] Example 5:

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

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

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

[0115] Backend processing module: Stores base station coordinate matrix and map layer data, receives location data reported by mobile terminals and completes coordinate transformation and visualization.

[0116] The beneficial effects of this embodiment are as follows: the system uses Bluetooth and ultrasonic multimodal fusion positioning, combined with filtering algorithms to effectively suppress indoor multipath interference and improve positioning accuracy to the meter level; back-end coordinate transformation and map data correction reduce accumulated errors and achieve accurate coordinate matching; low-power Bluetooth design reduces base station power 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 embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0118] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for improving indoor navigation and positioning accuracy based on filtering algorithms, characterized in that, The method flow is as follows: Bluetooth positioning: The mobile terminal receives Bluetooth signals, extracts the transmit power reference value, calculates the relative distance to the base station based on the relative attenuation ratio of the received signal strength to the reference value, and combines a piecewise power law regression model. The distance value is then optimized by Kalman filtering, and the nearest base station is selected to obtain the planar coordinates through triangulation. Ultrasonic-assisted positioning: The mobile terminal synchronously records the arrival time of ultrasonic and Bluetooth signals, calculates the time difference and combines it with the speed of sound to obtain the initial distance. After waveform correction to filter reflected waves and abnormal waves, it is weighted and fused with the Bluetooth positioning distance to suppress multipath effects. Multi-mode fusion positioning: The fusion strategy is dynamically switched according to the validity 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 used as the main result and combined with the historical ultrasonic distance for moving average filtering. Coordinate transformation and visualization: Based on map data from the backend processing module, dynamically mark the location of mobile terminals and the positioning error range.

2. The method for improving indoor navigation and positioning accuracy based on filtering algorithm according to claim 1, characterized in that, The steps for calculating relative distance based on received signal strength in Bluetooth positioning include: The signal strength at 1 meter from the base station broadcast is used as the reference value for transmission power. Obtain the actual Bluetooth signal strength received by the mobile terminal and calculate the relative attenuation ratio between the two. Based on the relative attenuation ratio, a piecewise power-law regression model is adopted: when the ratio is less than 1, the near-field distance is fitted using the power of 10; when the ratio is greater than or equal to 1, the far-field distance is fitted using the power of 7.7095 combined with a constant term.

3. The method for improving indoor navigation and positioning accuracy based on filtering algorithms according to claim 1, characterized in that, The steps for optimizing the Kalman filter include: Using the transmit power reference value as the initial estimate, the initial estimate error covariance, measurement noise variance, and process noise variance are set. 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 to correct the posterior estimate and covariance. Iterative optimization is performed to smooth signal strength fluctuations, thereby optimizing the distance calculation results.

4. The method for improving indoor navigation and positioning accuracy based on filtering algorithm according to claim 1, characterized in that, The ultrasonic-assisted positioning specifically includes: Calculate the difference between the arrival time of the ultrasonic wave and the arrival time of the Bluetooth signal; this difference is approximately the propagation time of the ultrasonic wave. The initial distance is calculated based on the sound velocity of 340 m / s corrected for ambient temperature, where the sound velocity is corrected for temperature increase according to a formula. Spectrum analysis filters out reflected waves with frequency offsets exceeding ±5%, amplitude analysis filters out abnormal peaks and troughs exceeding ±3 standard deviations of the average value, and retains the distance value corresponding to the main peak.

5. The method for improving indoor navigation and positioning accuracy based on filtering algorithms according to claim 1, characterized in that, The steps of weighted fusion to suppress multipath effects include: The final distance is calculated by weighting the Bluetooth positioning distance and the ultrasonically corrected distance according to the initial weights. The weights are dynamically adjusted based on the degree of environmental obstruction. When the obstruction is severe, the weight of Bluetooth distance is increased, and when the environment is open, the weight of ultrasonic distance is increased.

6. The method for improving indoor navigation and positioning accuracy based on filtering algorithm according to claim 1, characterized in that, The multi-modal fusion positioning strategy includes: The fusion frequency is synchronized with the Bluetooth signal period and the ultrasonic signal period; When both signals are valid, the weighted and fused distance-corrected Bluetooth positioning coordinates shall be used first. When the ultrasonic signal is lost, the Bluetooth positioning result is used as a basis, and a moving average filter is applied in combination with the most recent valid ultrasonic distance to maintain positioning continuity.

7. The method for improving indoor navigation and positioning accuracy based on filtering algorithm according to claim 1, characterized in that, The coordinate transformation and visualization steps include: Based on the preset base station coordinate matrix, the absolute coordinates are solved by combining the distance equations from the mobile terminal to at least two base stations. The equations are in the form that the square difference between the coordinates of the mobile terminal and the coordinates of the base stations is equal to the square of the corresponding distance. The system includes pre-stored vector maps of indoor scenes, supporting landscape, portrait, 2D, and 3D effects. It marks base station locations and reference points, and uses semi-transparent circles to indicate the error range of mobile terminal locations.

8. An indoor navigation and positioning accuracy improvement system based on a filtering algorithm, used in the indoor navigation and positioning accuracy improvement method based on a filtering algorithm as described in claim 1, characterized in that, The system includes: Positioning base station array: It consists of multiple base stations deployed at preset coordinate points indoors. Each base station integrates a low-power Bluetooth module and an ultrasonic transmitting module. The Bluetooth module is used to transmit Bluetooth signals, and the ultrasonic transmitting module is used to transmit ultrasonic pulses. Mobile terminal: It has a built-in Bluetooth receiver module, an ultrasonic receiver module and a microprocessor, which are used to receive Bluetooth signals and ultrasonic pulses transmitted by the base station, analyze signal parameters and perform distance measurement, filtering optimization and positioning calculation; Backend processing module: Stores base station coordinate matrix and indoor vector map data, receives location data reported by mobile terminals, and completes coordinate transformation and visualization display.

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