A bluetooth and sound signal fusion positioning method for substation indoor unmanned aerial vehicle inspection

Abstract

The application discloses a kind of Bluetooth and sound signal fusion positioning methods for substation indoor unmanned aerial vehicle inspection, belong to indoor positioning technical field;Including for: deployment based on sound signal and unmanned aerial vehicle positioning system of Bluetooth;Received sound signal is carried out sound signal quality evaluation with reference signal;Full set of sound signal time difference of arrival is calculated and estimates position;Sound signal positioning quality evaluation;Bluetooth signal reception and multilateration positioning;Apply linear positioning constraint of Bluetooth;Multi-source data fusion positioning;Construction and update Bluetooth fingerprint database.The application utilizes sound signal positioning real-time update Bluetooth fingerprint library, optimizes Bluetooth positioning, carries out fusion with sound signal positioning data and Bluetooth, realizes unmanned aerial vehicle indoor high-precision real-time positioning, effectively solves the problem of satellite positioning failure under the complex environment of substation indoor;At the same time, realize the high-precision indoor positioning of unmanned aerial vehicle, expand the coverage range in the complex scene of substation indoor, improve the robustness and reliability of unmanned aerial vehicle inspection positioning.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of indoor positioning, and particularly relates to a Bluetooth and sound signal fusion positioning method for indoor unmanned aerial vehicle (UAV) inspection of a transformer substation. BACKGROUND

[0002] With the promotion of intelligent transformer substations, the application of UAV technology in indoor and outdoor inspection has become a trend. Due to its small size, light weight, high flexibility, wide view angle and high efficiency, the UAV has been widely used in main and distribution network line and outdoor transformer substation inspection. However, the indoor inspection task still faces many technical bottlenecks and challenges, which affect the application effect and reliability of task execution of the UAV. First, the indoor environment of the transformer substation is complex, and the high-density metal structure and concrete wall form a satellite denial environment, which causes the satellite positioning technology such as Beidou and GPS to fail, and the UAV cannot achieve accurate positioning through satellite navigation technology. Second, the existing indoor positioning technologies such as laser radar, binocular vision, UWB and WI-FI are usually based on a single signal source, which can achieve positioning to a certain extent, but generally have the disadvantages of high cost, poor environmental adaptability, insufficient accuracy, large power consumption and sensitivity to light, especially in the strong electromagnetic interference, low illumination and weak texture environment of the transformer substation, which is difficult to achieve accurate positioning. Therefore, a new fusion positioning solution is urgently needed for indoor inspection of the transformer substation to provide high-precision real-time positioning and ensure stable flight and accurate inspection of the UAV in the complex power environment. SUMMARY

[0003] The purpose of the present application is to provide a Bluetooth and sound signal fusion positioning method for indoor UAV inspection of a transformer substation to solve the problems raised in the background.

[0004] The purpose of the present application is achieved by a Bluetooth and sound signal fusion positioning method for indoor UAV inspection of a transformer substation, characterized in that the method comprises the following steps:

[0005] Step S1: deploying a UAV positioning system based on sound signals and Bluetooth;

[0006] Step S2: receiving sound signals and performing sound signal quality evaluation on the received sound signals and reference signals;

[0007] Step S3: calculating the time difference of arrival of the full set of sound signals and performing position estimation;

[0008] Step S4: evaluating the positioning quality of the sound signals;

[0009] Step S5: receiving Bluetooth signals and performing multilateration positioning;

[0010] Step S6: applying a linear positioning constraint of Bluetooth;

[0011] Step S7: Multi-source data fusion positioning.

[0012] Preferably, the step S1 deploys a drone positioning system based on acoustic signals and Bluetooth, specifically:

[0013] The drone positioning system includes a drone terminal, a Bluetooth beacon, an acoustic signal base station, and a central positioning server. N acoustic signal base stations are deployed at the four corners of the ceiling edge to emit synchronous and delayed predefined acoustic signals.

[0014] The Bluetooth beacon is placed on the ceiling above the area to be positioned, so that the Bluetooth message emitted by it covers the entire target positioning area.

[0015] The drone terminal is equipped with a microphone sensor and a Bluetooth module. The on-board microphone receives the frequency-modulated acoustic signals from the acoustic signal base station, and the on-board Bluetooth module receives the BLE messages with RSSI transmitted by the Bluetooth beacon.

[0016] The Bluetooth beacon, acoustic base station, drone terminal, and central positioning server for positioning communicate with each other.

[0017] Preferably, the step S2 receives the acoustic signal and performs acoustic signal quality evaluation on the received acoustic signal and the reference signal, specifically:

[0018] Step S2-1: Signal strength evaluation:

[0019] The signal strength evaluation S is based on the power calculation of the received signal, which is represented by the following formula:

[0020]

[0021] Where P r is the received power of the acoustic signal, and P ref is the received power of the reference signal.

[0022] Step S2-2: Signal-to-noise ratio evaluation:

[0023] The signal-to-noise ratio is used to evaluate the relative strength of the received acoustic signal and noise, which is calculated by the following formula:

[0024]

[0025] Where P noi is the power of the background noise.

[0026] Step S2-3: Cross-correlation coefficient evaluation:

[0027] The cross-correlation coefficient evaluates the similarity between the received signal and the reference signal. The cross-correlation function R xy (τ) between the two signals is represented as:

[0028]

[0029] wherein x(t) is a reference signal, y(t) is a received signal, and τ is a signal time delay;

[0030] Step S2-4: constructing a unified sound signal quality evaluation standard.

[0031] Preferably, the step S2-4 of constructing a unified sound signal quality evaluation standard specifically comprises:

[0032] Step S2-4-1: setting the signal intensity, signal-to-noise ratio, and correlation between signals as the criterion layer, and setting the comprehensive signal quality evaluation as the target layer.

[0033] The relative importance between these dimensions is evaluated by expert scoring, and is converted into a 1-9 scoring scale to construct a judgment matrix, wherein each element in the matrix represents the importance of the ith dimension relative to the jth dimension, and specifically as follows:

[0034]

[0035] wherein p S,SNR represents the importance of the signal intensity relative to the signal-to-noise ratio, p S,R represents the importance of the signal intensity relative to the cross-correlation coefficient; and p SNR,R represents the importance of the signal-to-noise ratio relative to the cross-correlation coefficient.

[0036] The judgment matrix is normalized to obtain the weight of each signal evaluation dimension, i.e., the signal intensity weight w S , the signal-to-noise ratio weight w SNR , and the correlation between signals weight w R .

[0037] Step S2-4-2: consistency check of the matrix, i.e., calculating the consistency ratio CR:

[0038] The maximum eigenvalue λ max of the judgment matrix is calculated, and the corresponding consistency index CI is calculated:

[0039]

[0040] wherein z is the dimension of the matrix.

[0041] The random consistency index RI is introduced, and for a judgment matrix with a dimension of 3, RI is 0.58, and at this time,

[0042]

[0043] If CR is less than a certain value, the consistency of the matrix can be accepted;

[0044] Step S2-4-3: Construct a comprehensive signal quality evaluation formula by the weights obtained in steps S2-4-1-S2-4-2:

[0045] U = w S ·S norm +w SNR ·SNR norm +w ρ ·ρ norm +w CIR ·CIR norm ;

[0046] According to the comprehensive score U, set a threshold U th to judge the quality of the received signal; if the comprehensive score U exceeds the threshold U th , it is considered that the signal quality meets the requirements and is suitable for position estimation; otherwise, the received signal is considered to be unsatisfactory.

[0047] Preferably, the set of sound signal time difference of arrival is calculated and position estimation is performed in step S3, specifically:

[0048] Step S3-1: Calculation of the set of TDOA measurement values:

[0049] The UAV positioning system based on sound signals includes M sound signal base stations with known positions, whose positions are s i , i = 1, 2, …, M;

[0050] For sound signal base station i and sound signal base station j, the TDOA measurement value r ij between the two is defined as:

[0051] r ij = ∥u-s i ∥-∥u-s j ∥+n ij +o ij +η ij ;

[0052] wherein u is the unknown position vector of the signal source; s i and s j are the position vectors of sound signal base station i and sound signal base station j, respectively; n ij is the line-of-sight measurement noise, which is subject to zero-mean Gaussian distribution; o ij is the error of abnormal data, which is caused by measurement noise or equipment error; η ij is the deviation of non-line-of-sight path, which is positive;

[0053] The TDOA measurement values of all pairs of sound signal base stations are constructed into a matrix R:

[0054]

[0055] where r ij denotes the TDOA value from sensor i to sensor j;

[0056] Step S3-2: Geometric constraint based cyclic redundancy detection method to identify NLOS measurements:

[0057] Using the geometric constraint property of the full set TDOA matrix, the abnormal value is judged by the cyclic sum of the closed path. For any acoustic signal base station closed path, its TDOA measurement value satisfies the following cyclic sum condition:

[0058] r 12 +r 23 +r 31 ≈0;

[0059] If the cyclic sum of a certain path does not satisfy this condition, i.e. |r 12 +r 23 +r 31 |>∈, it indicates that there is a non-line-of-sight signal in the path;

[0060] The full set TDOA matrix R should satisfy the rank constraint in the ideal case. When there is only LOS signal, the rank of the matrix should be 2. If the rank of R is greater than 2, it indicates that the matrix contains abnormal data or NLOS path;

[0061] Step S3-3: NLOS detection and removal algorithm:

[0062] Based on the cyclic sum and the rank constraint, a set of iterative steps are defined to identify and remove NLOS measurements, as follows:

[0063] Define the initialization loss function L to evaluate the consistency of the measurement data:

[0064] L =∑ (i,j)∈S (r ij -(∥Γ-s i ∥-∥Γ-s j ∥)) 2 ;

[0065] Where S is the set of base station pairs of TDOA measurements, Γ is the estimate of the source position, and the minimum loss function is obtained by minimizing the loss function. In the minimization process of the loss function, the TDOA measurement value of each base station pair is traversed one by one, and it is checked whether it satisfies the cyclic sum condition:

[0066] |r ij +r jk +r ki |>∈;

[0067] For paths that do not satisfy this condition, the TDOA measurement with larger error is marked as NLOS value and is removed from the set S; after removing the NLOS measurement, the set S is updated, the source position estimate u is recalculated, and the process is iterated until all closed paths satisfy the loop closure condition or the loss function is no longer significantly reduced;

[0068] Step S3-4: Final position estimate:

[0069] After removing the NLOS measurement, the final source position is estimated by minimizing the updated loss function:

[0070]

[0071] Where S LOS is the set of line-of-sight (LOS) measurements after NLOS removal.

[0072] Preferably, the quality of the acoustic signal positioning is evaluated in step S4, specifically:

[0073] Step S4-1: Calculate the mean of the current positioning sample:

[0074] The sample mean represents the central tendency of multiple measurement results, indicating the optimal estimate of the coordinate position, and the expression is as follows:

[0075]

[0076] Where, and are the mean of the position estimate for the current n measurements, x i and y i are the x and y coordinates of the i-th measurement, respectively.

[0077] Step S4-2: Calculate the standard deviation of the current positioning sample:

[0078] The sample standard deviation of the positioning coordinates measures the dispersion of multiple measurement results, reflecting the distribution of positioning data around the mean. The larger the standard deviation, the more dispersed the measurement results, and the greater the uncertainty of the positioning. The calculation process is as follows:

[0079]

[0080] Where s x and s y are the standard deviations of the x and y coordinates of the n measurements updated in real time, respectively.

[0081] Step S4-3: Calculate the confidence interval of the current positioning point:

[0082] According to the sample mean, standard deviation and t value, the confidence interval of the current coordinate is calculated:

[0083]

[0084] Where CI x and CI y are the confidence intervals of x and y coordinates respectively; the t value is 2.262;

[0085] The confidence interval width of the position estimation represents the fluctuation degree of the two-dimensional positioning estimation at the position, as follows:

[0086]

[0087] Where d wx and d wy represent the confidence interval width of x and y coordinates respectively at the current confidence level.

[0088] Preferably, the Bluetooth signal receiving and multilateration in step S5 are specifically as follows:

[0089] Step S5-1: Bluetooth RSSI signal receiving and distance estimation:

[0090] In the UAV positioning system, the relationship between RSSI value and distance is represented using a path loss model:

[0091] RSSI = -(10clog 10 (d) + A);

[0092] Where RSSI is the received signal strength, with the unit of dBm; d is the distance between the device and the Bluetooth beacon; A is the signal strength value measured at a reference distance of 1 meter; c is the path loss exponent;

[0093] RSSI = -(10nlog 10 (d) + A) is transformed, the estimated distance d between the device and the Bluetooth beacon is derived:

[0094]

[0095] Step S5-2: Bluetooth beacon multilateration positioning:

[0096] Let the coordinates of the Bluetooth beacon i be (x i , y i ), and the position coordinates of the UAV to be positioned be (x, y), and the distance between them and the beacon i be d i , then the distance relationship between the UAV and the beacon is:

[0097]

[0098] Utilizing the distance d between multiple beacons in space and the drone i Multiple nonlinear equations are constructed, and the above nonlinear equation system is simplified into a linear equation system by the elimination method, that is, the position coordinates of the UAV are solved.

[0099] Preferably, applying Bluetooth linear positioning constraints in step S6 specifically involves:

[0100] The likelihood function derived from the RSSI distance model is used to calculate the distance error from the device to the beacon;

[0101] Create a circular density function around each beacon Its radius is determined based on the measurement data, and all measurements are summed, as shown in the following expression:

[0102]

[0103] in, To locate the two-dimensional position of the terminal, d is the two-dimensional position of the i-th beacon. i The distance measured for the i-th beacon; the distance d between the UAV terminal and the beacon is estimated by measuring the signal strength. i And find a target position x such that the actual distance from the drone to the beacon Distance d from the inference i Minimize the difference between them; by minimizing this likelihood function. This allows us to estimate the actual location of the drone;

[0104] To further optimize the positioning, the following linear constraints are used to limit the location estimation:

[0105]

[0106] Where A is a linear constraint matrix, representing the range of the region to be located;

[0107] Assuming the region to be located is a rectangle, in Ax≤b, A and vector b are represented as follows:

[0108]

[0109] Where, x min and x max Indicates the left and right boundaries of the room, y min and y max This indicates the upper and lower boundaries of the room.

[0110] Preferably, the multi-source data fusion positioning in step S7 specifically includes:

[0111] Step S7-1: Define the state vector

[0112] The state vector X of the UAV terminal centroid setting contains two parts of position p and its speed v:

[0113]

[0114] Step S7-2: System state transition equation setting, the nonlinear state transition equation f of the system is expressed as:

[0115] X k|k-1 =f(X k-1|k-1 ,u k ,q k );

[0116] Wherein, u k is the control input vector, q k is the process noise vector;

[0117] The state transition of the system follows the kinematic model, the position of the UAV terminal changes with the speed, the state transition equation describes the change of position and speed, which can be expressed as:

[0118]

[0119] v k =v k-1 +a k-1 Δt;

[0120] Wherein, a is the acceleration, Δt is the time step, and the motion model is substituted into the state transition function:

[0121]

[0122] The corresponding Jacobian matrix F of the corresponding state transition function f k is:

[0123]

[0124] Wherein, is the partial derivative of the state transition function f to the state vector X, which represents the degree of local linearization of f to the state; I is the unit matrix, which represents the autocorrelation of the variable;

[0125] Step S7-3: Prediction covariance matrix update, set the state covariance matrix prediction equation as:

[0126]

[0127] Wherein, P is the covariance matrix of the state vector, Q is the covariance matrix of the process noise, L k is the Jacobian matrix of the noise;

[0128] Step S7-4: Observation model and update equation:

[0129] Considering the fusion of acoustic signals and Bluetooth fingerprint positioning information, the observation equation Z k is the combined information of two sensors:

[0130] Z k = h(X k|k-1 ,r k );

[0131] where h(X) is the observation function, and Bluetooth fingerprint and acoustic signal provide different position information, r k is the observation noise;

[0132] The Jacobian matrix H k of the observation equation is expressed as:

[0133]

[0134] Preferably, the observation update step includes calculating the residual, innovation covariance, Kalman gain and update of the state vector, as follows:

[0135] Calculate the residual, compare the observed value Z k of the sensor and the system predicted state X k|k-1 , and calculate the observation residual Y k :

[0136] Y k = Z k -h(X k|k-1 ,r k );

[0137] where Z k represents the observed data of the sensor, and h(X) is the observation function, representing the observed value estimated by the system from the state vector;

[0138] Calculate the innovation covariance matrix, combine the observation noise R k and the state covariance matrix P k|k-1 to calculate the innovation covariance matrix S k :

[0139]

[0140] Calculate the Kalman gain, and use the Kalman gain K k to adjust the degree of trust of the system to the observed data:

[0141]

[0142] Combine the predicted state X k|k-1 and the observation residual Y k , and use the Kalman gain Kk updating the state vector X of the intelligent terminal k|k :

[0143] X k|k = X k|k-1 + K k Y k ;

[0144] As the observation information is continuously introduced, the value of the covariance matrix gradually decreases:

[0145] P k|k = (I - K k H k )P k|k-1 ;

[0146] Wherein, H k is the Jacobian matrix of the observation equation.

[0147] Compared with the prior art, the present application has the following improvements and advantages:

[0148] 1. The acoustic signal positioning is used to update the Bluetooth fingerprint library in real time, optimize the Bluetooth positioning, and fuse the Bluetooth and acoustic signal positioning data, so as to realize the indoor high-precision real-time positioning of the unmanned aerial vehicle, effectively solve the problem of satellite positioning failure in the complex indoor environment of the substation, and realize the high-precision indoor positioning of the unmanned aerial vehicle, expand the coverage range of the unmanned aerial vehicle in the complex indoor scene of the substation, and improve the positioning robustness and reliability of the unmanned aerial vehicle inspection.

[0149] 2. The unmanned aerial vehicle carrying the Bluetooth and acoustic signal sensors is used to introduce the acoustic signal position estimation information in the single Bluetooth fingerprint positioning failure area, update the Bluetooth fingerprint database in real time, and effectively overcome the positioning precision decline caused by uneven distribution of Bluetooth fingerprint data.

[0150] 3. By fully utilizing the complementarity and synergistic effect of multi-source data, the present application significantly reduces the misjudgment rate and improves the positioning accuracy and reliability, economically and efficiently meets the needs of indoor unmanned aerial vehicle inspection. BRIEF DESCRIPTION OF DRAWINGS

[0151] Figure 1 is the flowchart of the method of the present application.

[0152] Figure 2 is the structural schematic diagram of the unmanned aerial vehicle positioning system based on acoustic signals and Bluetooth.

[0153] Figure 3 is the Bluetooth RSSI signal strength contour diagram. DETAILED DESCRIPTION

[0154] The present application is further described below in conjunction with the drawings.

[0155] As Figure 1 shown, a Bluetooth and acoustic signal fusion positioning method for substation indoor unmanned aerial vehicle inspection, the method comprising the following steps:

[0156] Step S1: deploy an unmanned aerial vehicle positioning system based on acoustic signals and Bluetooth, specifically:

[0157] As Figure 2 shown, the unmanned aerial vehicle positioning system includes an unmanned aerial vehicle terminal, a Bluetooth beacon, an acoustic signal base station, and a central positioning server. N acoustic signal base stations are deployed at the four corners of the ceiling edge to emit synchronous delayed predefined acoustic signals. Multiple Bluetooth beacons are placed on the ceiling above the area to be positioned, so that the Bluetooth messages emitted by them cover the entire target positioning area as much as possible. The unmanned aerial vehicle terminal is equipped with a microphone sensor and a Bluetooth module. The airborne microphone receives frequency-modulated acoustic signals from the acoustic signal base station, while the airborne Bluetooth module receives BLE messages with RSSI transmitted by the Bluetooth beacon. The Bluetooth beacon, acoustic base station, unmanned aerial vehicle terminal, and central positioning server for positioning communicate with each other.

[0158] In step S2, the acoustic signal is received, and the received acoustic signal is evaluated for acoustic signal quality with a reference signal, specifically:

[0159] After completing the deployment of the Bluetooth and acoustic signal combined unmanned aerial vehicle positioning system, the acoustic signal base station emits synchronous delayed frequency-modulated acoustic signals, and the microphone carried by the unmanned aerial vehicle receives these acoustic signals for preliminary positioning. Due to the influence of multipath effect and noise in the indoor area of the substation, the quality of the acoustic signals received at different positions is not the same. Therefore, in order to ensure that the quality of the received acoustic signal meets the high-precision positioning requirement, the acoustic signal quality is evaluated by comparing the difference between the received acoustic signal and the reference signal.

[0160] Step S2-1: signal strength evaluation:

[0161] Signal strength evaluation S is based on power calculation of the received signal, which is represented by the following formula:

[0162]

[0163] Where P r is the received power of the acoustic signal, and P ref is the received power of the reference signal.

[0164] Step S2-2: signal-to-noise ratio evaluation:

[0165] Signal-to-noise ratio is used to evaluate the relative strength of the received acoustic signal and noise, which is calculated by the following formula:

[0166]

[0167] where P noi is the power of the background noise;

[0168] Step S2-3: cross-correlation coefficient evaluation:

[0169] The cross-correlation coefficient evaluates the similarity between the received signal and the reference signal. The cross-correlation function R xy (τ) between the two signals is represented as:

[0170]

[0171] where x(t) is the reference signal, y(t) is the received signal, and τ is the signal time delay;

[0172] Step S2-4: constructing a unified sound signal quality evaluation standard, specifically:

[0173] From the three dimensions of signal intensity, signal-to-noise ratio, and correlation between signals, the quality of the sound signal received by the unmanned aerial vehicle terminal is evaluated to determine whether it meets the high-precision positioning signal quality standard, so as to perform position estimation on the signal that meets the sound signal quality evaluation standard. A unified sound signal quality evaluation standard is needed.

[0174] Step S2-4-1: setting the three signal quality evaluation indicators of signal intensity, signal-to-noise ratio, and correlation between signals as the criterion layer, and setting the comprehensive signal quality evaluation as the target layer;

[0175] The relative importance between these dimensions is evaluated by expert scoring and converted into a 1-9 scoring scale to construct a judgment matrix, where each element in the matrix represents the importance of the i-th dimension relative to the j-th dimension, specifically as follows:

[0176]

[0177] where p S,SNR represents the importance of signal intensity relative to signal-to-noise ratio, p S,R represents the importance of signal intensity relative to cross-correlation coefficient; and p SNR,R represents the importance of signal-to-noise ratio relative to cross-correlation coefficient;

[0178] The judgment matrix is normalized to obtain the weight of each signal evaluation dimension, i.e., the signal intensity weight w S , the signal-to-noise ratio weight w SNR , and the correlation between signals weight w R ;

[0179] Step S2-4-2: consistency check of the matrix, i.e., calculating the consistency ratio CR:

[0180] Calculate the maximum eigenvalue λ maxand calculate its corresponding consistency index CI:

[0181]

[0182] where z is the dimension of the matrix;

[0183] The random consistency index RI is introduced, and for a judgment matrix with a dimension of 3, RI is 0.58, at which time,

[0184]

[0185] If CR is less than a certain value, the consistency of the judgment matrix can be accepted;

[0186] Step S2-4-3: The comprehensive signal quality evaluation formula is constructed by the weights obtained in steps S2-4-1 to S2-4-2:

[0187] U = w S ·S norm +w SNR ·SNR norm +w ρ ·p norm +w CIR ·CIR norm ;

[0188] According to the comprehensive score U, a threshold U th is set to judge the quality of the received signal; if the comprehensive score U exceeds the threshold U th , it is considered that the signal quality meets the requirements and is suitable for position estimation; otherwise, the received signal is considered to be unsatisfactory.

[0189] In different indoor positioning scenarios, the importance of the four evaluation criteria is different; for example, in a noisy environment, the weight w SNR of the signal-to-noise ratio should be increased; and when the signal transmission distance is far, the weight w S of the signal strength should be appropriately increased. By dynamically adjusting the weights, the indoor environment and requirements of the substation can be more effectively adapted.

[0190] Step S2 completes the quality evaluation of the real-time received acoustic signals, and only acoustic signals that meet the quality evaluation criteria are calculated for the complete set of acoustic signal TDOA values and position estimation; step S3 calculates the time difference of arrival of the complete set of acoustic signals and performs position estimation, which is specifically:

[0191] Step S3-1: Calculation of the complete set of TDOA measurement values:

[0192] The unmanned aerial vehicle positioning system based on acoustic signals includes M acoustic signal base stations with known positions s i , i = 1, 2, …, M;

[0193] TDOA measurements r between each pair of acoustic signal base stations i and j ij defined as:

[0194] r ij = ||u - s i || - ||u - s j || + n ij + o ij + η ij ;

[0195] where u is the unknown position vector of the signal source; s i and s j are the position vectors of acoustic signal base stations i and j, respectively; n ij is the line-of-sight measurement noise, which is subject to a zero-mean Gaussian distribution; o ij is the outlier error caused by measurement noise or equipment error; η ij is the deviation of non-line-of-sight path, which is positive;

[0196] The TDOA measurements of all acoustic signal base station pairs are constructed into a matrix R:

[0197]

[0198] where r ij represents the TDOA value from sensor i to sensor j;

[0199] Step S3-2: Identify NLOS measurements based on geometric constraint-based cyclic redundancy detection method:

[0200] In order to identify and remove NLOS measurements, the geometric constraint characteristics of the full set TDOA matrix are utilized, and the sum of the cycles of the closed path is used to judge outliers. For any closed path of acoustic signal base stations, the TDOA measurements thereof satisfy the following cycle sum condition:

[0201] r 12 + r 23 + r 31 ≈ 0;

[0202] If the cycle sum of a certain path does not satisfy this condition, i.e., |r 12 + r 23 + r 31 | > ∈, it indicates that there is a non-line-of-sight signal in the path;

[0203] The full set TDOA matrix R should satisfy the rank constraint in an ideal case. When there is only a line-of-sight signal, the rank of the matrix should be 2. If the rank of R is greater than 2, it indicates that the matrix contains outliers or NLOS paths;

[0204] Step S3-3: NLOS detection and removal algorithm:

[0205] Based on the sum of cycles and rank constraints, a set of iterative steps is defined to identify and remove NLOS measurements, as follows:

[0206] An initialization loss function L is defined to evaluate the consistency of measurement data:

[0207] L = ∑ (i,j)∈S (r ij -(∥Γ-s i ∥-∥Γ-s j ∥)) 2 ;

[0208] Where S is the set of base station pairs for TDOA measurements, Γ is the estimate of source location, and the best estimate is obtained by minimizing the loss function; during the minimization of the loss function, each TDOA measurement of each base station pair is traversed one by one to check whether it satisfies the cycle sum condition:

[0209] |r ij +r jk +r ki |>∈;

[0210] For paths that do not satisfy this condition, the TDOA measurement with larger error is marked as a NLOS value and removed from the set S; after removing the NLOS measurement, the set S is updated, the source location estimate u is recalculated, and the process is iterated until all closed paths satisfy the cycle sum condition or the loss function no longer significantly decreases;

[0211] Step S3-4: Final position estimation:

[0212] After removing the NLOS measurement, the final source location is estimated by minimizing the updated loss function:

[0213]

[0214] Where S LOS is the set of line-of-sight (LOS) measurements after NLOS removal. Through the above steps, non-line-of-sight measurements can be identified and removed based on geometric characteristics, improving the accuracy and robustness of positioning.

[0215] Step S3 realizes real-time position estimation of indoor space drones through acoustic signals, and each measurement result may be different, Step S4 calculates the confidence space of the current positioning coordinates to evaluate the position quality, specifically:

[0216] Step S4-1: Calculate the mean of the current positioning sample:

[0217] The sample mean represents the central tendency of multiple measurement results, and is the optimal estimation value of the coordinate position, and is expressed as follows:

[0218]

[0219] wherein, and are the mean values of the position estimation under the current n measurements, x i and y i are the x and y coordinates of the i-th measurement, respectively.

[0220] Step S4-2: Calculate the current positioning sample standard deviation:

[0221] The sample standard deviation of the positioning coordinates measures the dispersion degree of multiple measurement results, and reflects the distribution of the positioning data around the mean value. The greater the standard deviation, the more dispersed the measurement results, and the greater the uncertainty of the positioning. The calculation process is as follows:

[0222]

[0223] wherein, s x and s y are the standard deviations of the x and y coordinates of the real-time updated n measurements, respectively.

[0224] Step S4-3: Calculate the current positioning point confidence interval:

[0225] According to the sample mean, the standard deviation, and the t value, the confidence interval of the current coordinate is calculated as follows:

[0226]

[0227] wherein, CI x and CI y are the confidence intervals of the x and y coordinates, respectively; and the t value is 2.262.

[0228] The confidence interval width of the position estimation represents the fluctuation degree of the two-dimensional positioning estimation at the position, and is as follows:

[0229]

[0230] wherein, d wx and d wy represent the confidence interval widths of the x and y coordinates under the current confidence level, respectively. The confidence space of the current estimated position is calculated through the above steps, and the confidence interval width threshold T is set according to the system accuracy requirement and actual experience. If the confidence interval width is greater than the threshold T, it indicates that the acoustic signal positioning fluctuation is large, and the acoustic positioning is inaccurate, and needs to be updated and repositioned; if the confidence interval width is less than the threshold, it is considered that the positioning quality meets the requirements.

[0231] Step S5: Bluetooth signal receiving and multilateral positioning;

[0232] Step S1 completes the deployment of the unmanned aerial vehicle positioning system based on Bluetooth and sound signals, wherein the Bluetooth module carried by the unmanned aerial vehicle receives the RSSI signal, i.e. the strength of the signal when it arrives, emitted by the Bluetooth beacon; by analyzing the RSSI value, the distance between the unmanned aerial vehicle and each Bluetooth beacon is estimated, and then the known coordinates of each beacon are used to realize the positioning of the unmanned aerial vehicle based on the Bluetooth signal through the multilateral measurement method; the specific steps are as follows:

[0233] Step S5-1: Bluetooth RSSI signal receiving and distance estimation:

[0234] In the unmanned aerial vehicle positioning system, the relationship between the RSSI value and the distance is represented by using the path loss model:

[0235] RSSI = -(10c log 10 (d) + A);

[0236] Wherein, RSSI is the received signal strength, the unit is dBm; d is the distance between the device and the Bluetooth beacon; A is the signal strength value measured at a reference distance of 1 meter; c is the path loss index;

[0237] Transforming RSSI = -(10n log 10 (d) + A), the estimated distance d between the device and the Bluetooth beacon is derived:

[0238]

[0239] Step S5-2: Multilateral measurement positioning of Bluetooth beacon:

[0240] Let the coordinates of the Bluetooth beacon i be (x i ,y i ), the coordinates of the unmanned aerial vehicle to be positioned be (x, y), and the distance between them and the beacon i be d i , then the distance relationship between the unmanned aerial vehicle and the beacon is:

[0241]

[0242] The distances d i between multiple beacons and the unmanned aerial vehicle in the space are used to construct multiple nonlinear equations, and by using the elimination method, the above multiple nonlinear equations are simplified into a linear equation group, i.e. the coordinates of the unmanned aerial vehicle position are solved.

[0243] Step S6: Apply Bluetooth linear positioning constraints;

[0244] In traditional Bluetooth-based drone positioning systems, environmental changes can lead to decreased or even failed Bluetooth RSSI positioning accuracy; therefore, a model independent of multiple calibration points is required. When positioning within specific geometric spaces such as corridors or cabinet passageways, applying linear constraints to the drone's position can improve positioning accuracy. Simultaneously, during fingerprint recognition, fingerprint data that does not conform to the constraints can be removed to optimize the drone positioning system performance. The specific steps of the constraints proposed in this invention are as follows:

[0245] The likelihood function derived from the RSSI distance model is used to calculate the distance error from the device to the beacon;

[0246] Create a circular density function around each beacon Its radius is determined based on the measurement data, and all measurements are summed, as shown in the following expression:

[0247]

[0248] in, To locate the two-dimensional position of the terminal, d is the two-dimensional position of the i-th beacon. i The distance measured for the i-th beacon; the distance d between the UAV terminal and the beacon is estimated by measuring the signal strength. i And find a target position x such that the actual distance from the drone to the beacon Distance d from the inference i Minimize the difference between them; by minimizing this likelihood function. This allows us to estimate the actual location of the drone;

[0249] To further optimize the positioning, the following linear constraints are used to limit the location estimation:

[0250]

[0251] Where A is a linear constraint matrix, representing the range of the region to be located;

[0252] Assuming the region to be located is a rectangle, in Ax≤b, A and vector b are represented as follows:

[0253]

[0254] Where, x min and x max Indicates the left and right boundaries of the room, y min and y max This indicates the upper and lower boundaries of the room.

[0255] Step S7: Multi-source data fusion for localization;

[0256] The sound signal positioning coverage is extensive and the positioning accuracy is high, but it is limited by multipath effect in the complex indoor environment of the substation, so it is suitable for deployment in open indoor scenes. In contrast, Bluetooth devices are low in cost and short in effective working distance, making them more suitable for narrow spaces. By adopting a multi-sensor fusion positioning method, combining sound signals and Bluetooth information, comprehensive indoor unmanned aerial vehicle positioning in substations is achieved. A fusion positioning algorithm based on extended Kalman filtering is used, and the specific steps are as follows:

[0257] Step S7-1: State vector definition

[0258] The state vector X of the unmanned aerial vehicle terminal centroid includes two parts of position p and velocity v:

[0259]

[0260] Step S7-2: System state transition equation setting, the nonlinear state transition equation f of the system is expressed as:

[0261] X k|k-1 =f(X k-1|k-1 ,u k ,q k );

[0262] Where u k is the control input vector, and q k is the process noise vector;

[0263] The state transition of the system follows the kinematic model, the position of the unmanned aerial vehicle terminal changes with the velocity, and the state transition equation describes the change of position and velocity, which can be expressed as:

[0264]

[0265] v k =v k-1 +a k-1 Δt;

[0266] Where a is the acceleration and Δt is the time step, and the motion model is substituted into the state transition function:

[0267]

[0268] The corresponding Jacobian matrix F k of the state transition function f is:

[0269]

[0270] Where is the partial derivative of the state transition function f with respect to the state vector X, indicating the degree of local linearization of f with respect to the state; I is the identity matrix, indicating the autocorrelation of the variable;

[0271] Step S7-3: Predicting the covariance matrix update, setting the state covariance matrix prediction equation as:

[0272]

[0273] where P is the covariance matrix of the state vector, Q is the covariance matrix of the process noise, L k is the Jacobian matrix of the noise;

[0274] Step S7-4: Observation model and update equation:

[0275] Considering the fusion of acoustic signals and Bluetooth fingerprint positioning information, the observation equation Z k is the combined information of the two sensors:

[0276] Z k = h(X k|k-1 ,r k );

[0277] where h(X) is the observation function, and the Bluetooth fingerprint and acoustic signal provide different position information, r k is the observation noise;

[0278] The Jacobian matrix H k of the observation equation is expressed as:

[0279]

[0280] Calculate the residual, compare the sensor observation value Z k and the system predicted state X k|k-1 , and calculate the observation residual Y k :

[0281] Y k = Z k -h(X k|k-1 ,r k );

[0282] where Z k represents the observation data of the sensor, and h(X) is the observation function, which represents the observation value estimated by the system from the state vector;

[0283] Calculate the innovation covariance matrix, combine the observation noise R k and the state covariance matrix P k|k-1 to calculate the innovation covariance matrix S k :

[0284]

[0285] Calculate the Kalman gain, and use the Kalman gain Kk Adjust the degree of trust of the system to the observation data:

[0286]

[0287] Combine the predicted state X k|k-1 and the observation residual Y k , using the Kalman gain K k to update the state vector X k|k of the intelligent terminal:

[0288] X k|k = X k|k-1 + K k Y k

[0289] As the observation information is continuously introduced, the value of the covariance matrix gradually decreases:

[0290] P k|k = (I - K k H k )P k|k-1

[0291] where H k is the Jacobian matrix of the observation equation.

[0292] Using the above fusion positioning algorithm, the sound signal and Bluetooth positioning data can be fused to realize accurate position estimation of the unmanned aerial vehicle. By fusing two different positioning data sources of Bluetooth and sound signals, the errors caused by the limitations of a single sensor (such as small Bluetooth coverage and sound signals susceptible to multipath interference) can be reduced, thereby realizing higher-precision unmanned aerial vehicle positioning in all scenarios. With each iteration, the system updates the position of the unmanned aerial vehicle according to the new observation data, gradually reducing noise and uncertainty, thereby tracking the unmanned aerial vehicle in real time and accurately in a complex indoor environment.

[0293] Step S8: Construct and update the Bluetooth fingerprint database;

[0294] The RSSI signal strength value received by the unmanned aerial vehicle through the Bluetooth module and the unmanned aerial vehicle position coordinates fused by Bluetooth and sound signals are stored in pairs (RSSI, (x, y, z)), and the Bluetooth fingerprint database is constructed. By collecting RSSI data of the system at multiple different positions in the substation indoor, the accuracy of the unmanned aerial vehicle positioning and the robustness of the system can be significantly improved, as shown in FIG. 4, which is an RSSI signal strength value contour plot in a certain substation indoor. Figure 3

[0295] ​​​The above merely describes the embodiments of the present application, but is not intended to limit the present application. The present application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A Bluetooth and acoustic signal fusion positioning method for indoor drone inspection of substations, characterized in that: The method includes the following steps: Step S1: Deploy a drone positioning system based on acoustic signals and Bluetooth; Step S2: Receive the acoustic signal and evaluate its quality by comparing the received acoustic signal with a reference signal; The acoustic signal is received, and the received acoustic signal is compared with a reference signal to evaluate the acoustic signal quality, specifically as follows: Step S2-1: Signal strength assessment; Step S2-2: Signal-to-noise ratio evaluation; Step S2-3: Cross-correlation coefficient evaluation; Step S2-4: Construct a unified sound signal quality assessment standard, specifically as follows: Step S2-4-1: Set the three signal quality evaluation indicators, namely signal strength, signal-to-noise ratio and correlation between signals, as the criterion layer, and set the comprehensive signal quality evaluation as the target layer; The relative importance of these dimensions is assessed through expert scoring, and the scores are converted into a 1-9 rating scale to construct a judgment matrix. Each element in the matrix represents the importance of the i-th dimension relative to the j-th dimension, as follows: Where, p S,SNR p represents the importance of signal strength relative to the signal-to-noise ratio. S,R This indicates the importance of signal strength relative to the cross-correlation coefficient; p SNR,R The importance of signal-to-noise ratio relative to cross-correlation coefficient; The judgment matrix is ​​normalized to obtain the weight of each signal evaluation dimension, i.e., the signal strength weight w. S Signal-to-noise ratio weight w SNR Correlation weights w between signals R ; Step S2-4-2: Perform a consistency check on the matrix, i.e., calculate the consistency ratio CR: Calculate the largest eigenvalue λ of the judgment matrix max And calculate its corresponding consistency index CI: Where z is the dimension of the matrix; Introducing the random consistency index RI, for a judgment matrix of dimension 3, the RI is 0.

58. At this point, If CR is less than a specific value, then the consistency of the judgment matrix is ​​acceptable; Step S2-4-3: Construct a comprehensive signal quality evaluation formula using the weights obtained in steps S2-4-1 to S2-4-2: U=w S ·S norm +w SNR ·SNR norm +w ρ ·ρ norm +w CIR ·CIR norm ; Based on the comprehensive score U, a threshold U is set. th Determine the quality of the received signal; if the overall score U exceeds the threshold U th If the signal quality meets the requirements, it is considered suitable for location estimation; otherwise, the received signal is considered unacceptable. Step S3: Calculate the arrival time difference of the entire acoustic signal set and perform location estimation, specifically as follows: Step S3-1: Calculation of the total set TDOA measurement values: The acoustic signal-based UAV positioning system includes M acoustic signal base stations with known locations, denoted as s. i , i = 1, 2, ..., M; For acoustic signal base station i and acoustic signal base station j, the TDOA measurement value r between each pair of them is... ij Defined as: r ij =∥us i ∥-∥us j ∥+n ij +o ij +n ij ; Where u is the unknown position vector of the signal source; s i and s j These are the position vectors of acoustic signal base station i and acoustic signal base station j, respectively; n ij The noise in the line-of-sight measurement follows a zero-mean Gaussian distribution; ij Abnormal data error, caused by measurement noise or equipment error; η ij This represents the deviation of the non-line-of-sight path, and is a positive value. Construct a matrix R from the TDOA measurements of all acoustic signal base station pairs: Where, r ij This represents the TDOA value from sensor i to sensor j; Step S3-2: Identify NLOS measurements using a geometrically constrained cyclic redundancy detection method. By utilizing the geometric constraint properties of the complete TDOA matrix, outliers are identified through the cyclic summation of closed paths. For any closed path of an acoustic signal base station, its TDOA measurement values ​​satisfy the following cyclic summation condition: r 12 +r 23 +r 31 ≈0； If the cyclic sum of a certain path does not satisfy this condition, i.e., |r 12 +r 23 +r 31 |>∈ indicates that there is a non-line-of-sight signal in the path; Ideally, the TDOA matrix R of the entire set should satisfy the rank constraint. When there are only LOS signals, the rank of the matrix should be 2. If the rank of R is greater than 2, it indicates that the matrix contains abnormal data or NLOS paths. Step S3-3: NLOS detection and removal algorithm: Based on the sum of cycles and rank constraints, a set of iterative steps is defined to identify and remove NLOS measurements, as follows: Define an initial loss function L to evaluate the consistency of the measurement data: Where S is the set of base station pairs measured by TDOA, Γ is the estimated source location, and the optimal estimate is obtained by minimizing the loss function. During the minimization of the loss function, the TDOA measurement values ​​of each base station pair are traversed one by one to check whether the loop condition is satisfied: |r ij +r jk +r ki |>∈； For paths that do not meet this condition, the TDOA measurement with a large error is marked as an NLOS value and removed from the set S; after removing the NLOS measurement, the set S is updated, the source location estimate u is recalculated, and the process is iterated until all closed paths satisfy the cycle and condition or the loss function no longer decreases significantly. Step S3-4: Final position estimation: After removing NLOS measurements, the final source location is estimated by minimizing the updated loss function: Among them, S LOS This is the set of sight distance measurements after NLOS rejection; Step S4: Evaluate the acoustic signal localization quality; Step S5: Bluetooth signal reception and multi-directional positioning; Step S6: Apply Bluetooth linear positioning constraints; Step S7: Multi-source data fusion for localization; Step S8: Build and update the Bluetooth fingerprint database.

2. The Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 1, characterized in that: The deployment of the drone positioning system based on acoustic signals and Bluetooth in step S1 specifically includes: The drone positioning system includes a drone terminal, a Bluetooth beacon, an acoustic signal base station, and a central positioning server. N acoustic signal base stations are deployed at the four corners of the ceiling edge to emit predefined acoustic signals with synchronous delay. The Bluetooth beacon is placed on the ceiling above the area to be located, so that the Bluetooth messages it emits cover the entire target location area. The drone terminal is equipped with a microphone sensor and a Bluetooth module. The onboard microphone receives FM sound signals from the sound signal base station, and the onboard Bluetooth module receives BLE messages with RSSI transmitted by Bluetooth beacons. The Bluetooth beacon, acoustic base station, drone terminal, and central positioning server that performs positioning communicate with each other.

3. The Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 1, characterized in that: In step S2, the acoustic signal is received, and the received acoustic signal is compared with a reference signal to evaluate the acoustic signal quality. Specifically: Step S2-1: Signal strength assessment: The signal strength assessment S is calculated based on the power of the received signal and is expressed by the following formula: Among them, P r P is the received power of the sound signal. ref The received power of the reference signal; Step S2-2: Signal-to-noise ratio evaluation: The signal-to-noise ratio (SNR) is used to evaluate the relative intensity of the received acoustic signal and noise, and is calculated using the following formula: Among them, P noi The power of the background noise; Step S2-3: Cross-correlation coefficient evaluation: The cross-correlation coefficient assesses the similarity between the received signal and the reference signal; the cross-correlation function R between the two signals is used to evaluate this similarity. xy (τ) is represented as: Where x(t) is the reference signal, y(t) is the received signal, and τ is the signal delay; Step S2-4: Construct a unified standard for evaluating the quality of acoustic signals.

4. The Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 1, characterized in that: In step S4, the acoustic signal localization quality is evaluated, specifically as follows: Step S4-1: Calculate the mean of the current location sample: The sample mean represents the central tendency of multiple measurements and indicates the best estimate of the coordinate position, expressed as follows: in, and The current values ​​are the mean of the position estimates from n measurements, x i and y i These are the x and y coordinates of the i-th measurement, respectively; Step S4-2: Calculate the standard deviation of the current location sample: The sample standard deviation of positioning coordinates measures the dispersion of multiple measurements, reflecting the distribution of positioning data around the mean. The larger the standard deviation, the more dispersed the measurement results and the greater the uncertainty in positioning. Its calculation process is as follows: Among them, s x and s y These are the standard deviations of the x and y coordinates from n measurements, updated in real time. Step S4-3: Calculate the confidence interval for the current location point: Calculate the confidence interval for the current coordinates based on the sample mean, standard deviation, and t-value: Among them, CI x and CI y These are the confidence intervals for the x and y coordinates, respectively; the t-value is 2.

262. The width of the confidence interval for location estimation represents the degree of fluctuation in the two-dimensional positioning estimate at that location, as shown in the following formula: Where, d wx and d wy These represent the widths of the confidence intervals at the x and y coordinates, respectively, at the current confidence level.

5. A Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 1, characterized in that: The Bluetooth signal reception and multi-directional positioning in step S5 are specifically as follows: Step S5-1: Bluetooth RSSI signal reception and distance estimation: In UAV positioning systems, the relationship between RSSI values ​​and distance is represented using a path loss model: RSSI=-(10clog 10 (d)+A); Where RSSI is the received signal strength in dBm; d is the distance between the device and the Bluetooth beacon; A is the signal strength value measured at a reference distance of 1 meter; and c is the path loss index. For RSSI = -(10clog) 10 By transforming (d)+A), the estimated distance d between the device and the Bluetooth beacon is derived: Step S5-2: Bluetooth beacon multi-directional positioning: Let the coordinates of Bluetooth beacon i be (x i ,y i The location coordinates of the UAV to be located are (x, y), and the distance between it and beacon i is d. i The distance relationship between the drone and the beacon is: Utilizing the distance d between multiple beacons in space and the drone i Multiple nonlinear equations are constructed, and the elimination method is used to simplify these multiple nonlinear equations into a system of linear equations, which is used to solve for the UAV's position coordinates.

6. The Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 1, characterized in that: The Bluetooth linear positioning constraint is applied in step S6 as follows: The likelihood function derived from the RSSI distance model is used to calculate the distance error from the device to the beacon; Create a circular density function around each beacon Its radius is determined based on the measurement data, and all measurements are summed, as shown in the following expression: in, To locate the two-dimensional position of the terminal, d is the two-dimensional position of the i-th beacon. i The distance measured for the i-th beacon; the distance d between the UAV terminal and the beacon is estimated by measuring the signal strength. i And find a target position x such that the actual distance from the drone to the beacon Distance d from the inference i Minimize the difference between them; by minimizing this likelihood function. This allows us to estimate the actual location of the drone; To further optimize the positioning, the following linear constraints are used to limit the location estimation: Where A is a linear constraint matrix, representing the range of the region to be located; Assuming the region to be located is a rectangle, in Ax≤b, A and vector b are represented as follows: Where, x min and x max Indicates the left and right boundaries of the room, y min and y max Indicates the upper and lower boundaries of the room.

7. A Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 1, characterized in that: The multi-source data fusion positioning in step S7 specifically involves: Step S7-1: Define the state vector The state vector X of the UAV terminal centroid consists of two parts: position p and velocity v. Step S7-2: Setting the system state transition equation. The nonlinear state transition equation f of the system is expressed as: X k|k-1 =f(X k-1|k-1 ,u k ,q k ); Among them, u k To control the input vector, q k This is the process noise vector; The system's state transition follows a kinematic model. The position of the UAV terminal changes with velocity, and the state transition equation describes the changes in position and velocity, which can be expressed as: v k =v k-1 +a k-1 Δt; Where a is the acceleration and Δt is the time step, the kinematic model is substituted into the state transition function: The corresponding state transition function f corresponds to the Jacobian matrix F. k for: in, Let f be the partial derivative of the state transition function f with respect to the state vector X, representing the degree of local linearization of f with respect to the state; I is the identity matrix, representing the autocorrelation of the variables; Step S7-3: Predict and update the covariance matrix. Set the prediction equation for the state covariance matrix as follows: Where P is the covariance matrix of the state vector, Q is the covariance matrix of the process noise, and L... k It is the Jacobian matrix of the noise; Step S7-4: Observation Model and Update Equations: Considering the fusion of acoustic signals and Bluetooth fingerprint positioning information, the observation equation Z is set. k It is a combination of information from two sensors: Z k =h(X k|k-1 ,r k ); Where h(X) is the observation function, and Bluetooth fingerprints and voice signals provide different location information, r k It is observation noise; The Jacobian matrix H of the observation equation k Represented as:

8. A Bluetooth and acoustic signal fusion positioning method for indoor UAV inspection of substations according to claim 7, characterized in that: The observation update steps include calculating the residuals, information covariance, Kalman gain, and updating the state vector, as detailed below: Calculate the residuals, and convert the sensor observations Z... k and the system's predicted state X k|k-1 Compare and calculate the observed residual Y k : Y k =Z k -h(X k|k-1 ,r k ); Among them, Z k h(X) represents the observation data from the sensor, and h(X) is the observation function, which represents the observation value estimated by the system from the state vector. Calculate the new information covariance matrix, and combine it with the observation noise R. k and state covariance matrix P k|k-1 To calculate the new information covariance matrix S k : Calculate the Kalman gain using the Kalman gain K. k Adjusting the system's level of trust in the observation data: Combined with predicted state X k|k-1 and observation residual Y k Using Kalman gain K k Update the state vector X of the smart terminal k|k : X k|k =X k|k-1 +K k Y k ; As observational information is continuously introduced, the value of the covariance matrix will gradually decrease: P k|k =(I-K k H k )P k|k-1 ; Among them, H k The Jacobian matrix of the observation equation.

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