Anesthesia bomb positioning control method based on sound wave guidance

Abstract

The invention provides an anaesthesia bomb positioning control method based on sound wave guidance, and relates to the technical field of information, and the method comprises the steps: obtaining an initial velocity vector provided by an anaesthesia bomb launching device, and building a sound wave propagation model in combination with environment parameters measured by a meteorological sensor; aiming at a sound wave signal received by the microphone array, carrying out filtering processing by adopting a self-adaptive beam forming algorithm to obtain an initial estimation value of Doppler frequency shift; and the Doppler frequency shift is accurately estimated by using a maximum likelihood algorithm, and the velocity component of the anesthesia bomb in the direction of the microphone array is calculated. The problem that a traditional guidance system is insufficient in precision in a complex environment is solved, the positioning and tracking precision is improved through multi-sensor fusion and an advanced algorithm, the system fully considers the influence of the atmospheric environment and the characteristics and aerodynamic characteristics of the anesthesia bomb, accurate guidance in the whole process is achieved, and the system is suitable for large-scale popularization and application. The system can be widely applied to the fields of wild animal protection, animal behavior research and the like, and has important practical value.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method for positioning and controlling an anesthetic bullet based on sound wave guidance. Background Art

[0002] The use of acoustic waves to guide the positioning and control of anesthetic bullets presents a critical technical challenge. Because the velocity and direction of the bullet fluctuate during flight, the frequency of the sound waves received by the microphone array undergoes a Doppler shift. This frequency shift changes dynamically with the bullet's motion, making it difficult to accurately measure its velocity vector. The magnitude of this frequency shift is related to the velocity component of the bullet in the direction of the microphone array, which is affected by the bullet's three-dimensional motion. Separating the velocity components of the bullet in different directions from the complex and variable Doppler shift is a pressing issue.

[0003] Furthermore, sound waves are subject to environmental interference during propagation, such as wind speed, temperature, and humidity. This can cause variations in the propagation velocity of the sound waves, which in turn affects the accuracy of the Doppler shift calculation. The altitude of the tranquilizer bullet also affects sound wave propagation: the higher the altitude, the longer the sound waves travel and the greater the signal attenuation. These factors all increase the uncertainty of the velocity vector estimate.

[0004] Furthermore, the tranquilizer bullet generates vibrations and noise during flight, interfering with the microphone array's measurement of the sound wave frequency. The structural design of the tranquilizer bullet and the mechanical characteristics of the launcher also affect the generation and propagation of the sound waves, further complicating the problem. Extracting effective sound signals from noisy environments and eliminating the influence of various interference factors presents another challenge for acoustic guidance and positioning control technology. Summary of the Invention

[0005] The present invention provides a method for positioning and controlling an anesthetic bullet based on acoustic wave guidance, which mainly includes: Based on the initial velocity vector provided by the tranquilizer bullet launcher and the ambient wind speed, temperature, and humidity data measured by meteorological sensors, a sound wave propagation model is established. This model takes into account atmospheric absorption, geometric diffusion, and ground effects, and calculates the attenuation of the sound wave during propagation. Based on the attenuation value calculated by the sound wave propagation model and the expected velocity range of the tranquilizer bullet, the theoretical variation range of the Doppler frequency shift is estimated. The upper limit corresponds to the frequency shift at the maximum expected velocity, and the lower limit corresponds to the frequency shift at the minimum expected velocity. The search interval for frequency measurement is set based on the estimated range. For the acoustic signal received by the microphone array, the signal covariance matrix is ​​calculated, and the minimum variance distortionless response algorithm is used for adaptive beamforming. This algorithm uses the covariance matrix to minimize the variance of the output signal while keeping the desired signal distortion-free and suppressing interference from ambient noise and the vibration of the anesthetic bullet itself. The filtered acoustic signal is then subjected to a fast Fourier transform to obtain spectrum data. The spectrum peak is searched within a preset search interval to obtain an initial estimate of the Doppler shift. Based on the initial estimate of the Doppler shift, the maximum likelihood algorithm is used for accurate estimation. A theoretical spectrum model is constructed, taking into account the influence of the Doppler effect on the spectrum. The objective function is defined as the mean square error between the measured spectrum and the theoretical spectrum. Through iterative optimization, the frequency value that minimizes the objective function is found. The estimated frequency value is combined with the sound source frequency to calculate the velocity component of the anesthetic bullet in the direction of the microphone array. The trajectory of the anesthetic bullet is predicted based on the calculated velocity components, combined with the bullet's ballistic model and meteorological data. The ballistic model takes into account the effects of gravity, air resistance, and wind, and includes parameters such as mass, shape coefficient, and drag coefficient. The Runge-Kutta method is used to solve the differential equation to obtain the change in the position of the anesthetic bullet in three-dimensional space over time. The velocity vector of the anesthetic bullet is dynamically tracked by fusing Doppler shift measurements and trajectory predictions using the Kalman filter algorithm. The state equation describes the position and velocity of the anesthetic bullet, and the observation equation establishes a nonlinear relationship between Doppler shift and velocity. An optimized velocity estimate is obtained through a recursive calculation of the prediction and update stages. Based on the optimized velocity vector, the relative position vector of the anesthetic bullet and the microphone array is calculated. A time synchronization mechanism is used to ensure that at least three microphone arrays observe the anesthetic bullet simultaneously. The measurement data of multiple microphone arrays are combined, and the three-dimensional spatial coordinates of the anesthetic bullet are solved using the principle of triangulation. A nonlinear system of equations is constructed, and the position coordinates of the anesthetic bullet are solved using the Newton iteration method. Based on the solved position coordinates and velocity information, the correction amount is calculated according to the proportional guidance law. The proportional guidance law generates a lateral acceleration command proportional to the line of sight angular rate based on the line of sight angular rate. The acceleration command is converted into a control surface deflection angle. Considering the aerodynamic characteristics and control surface efficiency of the tranquilizer bomb, the required deflection angle is calculated, and a control command is generated to guide the tranquilizer bomb. The position coordinates, velocity information and control command are recorded in the database.

[0006] As a preferred solution, a method for controlling the positioning of an anesthetic bullet based on acoustic wave guidance is provided, wherein: an initial velocity vector provided by an anesthetic bullet launching device is obtained, and an acoustic wave propagation model is established in combination with environmental parameters measured by a meteorological sensor; an adaptive beamforming algorithm is used to filter the acoustic wave signal received by a microphone array to obtain an initial estimate of the Doppler frequency shift; the Doppler frequency shift is accurately estimated using a maximum likelihood algorithm to calculate the velocity component of the anesthetic bullet in the direction of the microphone array; the motion trajectory of the anesthetic bullet is predicted based on the velocity component and meteorological data in combination with the anesthetic bullet trajectory model; the velocity of the anesthetic bullet is tracked by fusing the Doppler frequency shift measurement value and the trajectory prediction value through a Kalman filter algorithm; based on the optimized velocity vector, the three-dimensional spatial coordinates of the anesthetic bullet are calculated using the triangulation principle; and a control instruction is generated to guide the anesthetic bullet based on the position coordinates and velocity information, and recorded in a database, including: Initial velocity vector data provided by the tranquilizer bullet launcher is obtained and corrected based on the ambient wind speed, temperature, and humidity data measured in real time by meteorological sensors to obtain an estimated initial velocity vector of the tranquilizer bullet in the actual environment. This is used as one of the input parameters of the sound wave propagation model. Based on the corrected initial velocity vector estimate, the sound wave attenuation during propagation is calculated in conjunction with the sound wave propagation model, taking into account factors such as atmospheric absorption, geometric diffusion, and ground effects. The sound wave propagation model can choose to use a modified spherical diffusion model or a ray acoustic model suitable for outdoor environments. According to the expected speed range of the anesthetic bullet, combined with the attenuation value calculated by the sound wave propagation model, the theoretical variation range of the Doppler frequency shift is estimated using the Doppler effect formula; the relationship between the Doppler frequency shift and the speed of the anesthetic bullet can be described by the following formula: f_d=f_0(c+v_r) / (c+v_s) where f_d is the Doppler frequency shift, f_0 is the emission frequency of the sound wave, c is the speed of sound, v_r is the radial velocity of the anesthetic bullet relative to the receiver, and v_s is the radial velocity of the anesthetic bullet relative to the transmitter; determine the search interval for frequency measurement, with the upper limit corresponding to the frequency shift caused by the maximum expected speed and the lower limit corresponding to the frequency shift caused by the maximum expected speed. The frequency shift corresponding to the minimum expected velocity is determined. Within this search interval, the received acoustic signal is spectrally analyzed using a fast Fourier transform (FFT) algorithm to extract Doppler shift information. This extracted Doppler shift information is used as observations and input into a tracker based on the extended Kalman filter (EKF) algorithm. Based on the kinematic model of the anesthetic projectile, the tracker dynamically estimates and predicts the velocity state of the projectile and updates and corrects the state estimate based on the observations to improve the accuracy and robustness of the velocity estimation. If the velocity estimate output by the tracker exceeds a preset reasonable range (for example, exceeding the maximum flight speed of the anesthetic projectile), it is determined to be an abnormality and an alarm mechanism is triggered. The alarm information can be sent in real time to the handheld device of the on-site commander via a wireless communication module, indicating possible measurement errors or abnormal flight state of the anesthetic projectile and the need for timely countermeasures. The anesthetic projectile velocity estimate output by the tracker is fed back to the acoustic wave propagation model to dynamically adjust the model parameters to improve the accuracy of the acoustic wave attenuation calculation, forming a closed-loop optimization. Simultaneously, the velocity estimate is combined with the flight time of the anesthetic projectile to calculate the real-time position coordinates of the anesthetic projectile, providing a basis for subsequent target positioning and tracking.

[0007] As a preferred solution, the method for positioning and controlling an anesthetic bullet based on acoustic wave guidance, wherein: the establishing of the acoustic wave propagation model includes: obtaining the initial velocity vector data of the anesthetic bullet and environmental parameters, correcting the initial velocity vector, and obtaining an estimated value of the initial velocity vector of the anesthetic bullet in the actual environment; and calculating the attenuation value of the acoustic wave during the propagation process using a modified spherical diffusion model based on the estimated velocity vector, including: Acquire the sound wave signal received by the microphone array, preprocess the original signal, including removing DC bias, windowing, framing and other operations, to obtain a time domain signal suitable for subsequent analysis; calculate the covariance matrix of the preprocessed signal data; the elements of the covariance matrix represent the correlation between the signals received by different microphones, and its calculation formula is: R(i, j)=E[xi(t)xj(t)], where xi(t) and xj(t) represent the signals received by the i-th and j-th microphones, respectively, and E[] represents the mathematical expectation; based on the calculated covariance matrix, the minimum variance distortionless response (MVDR) algorithm is used for adaptive beamforming; the goal of the MVDR algorithm is to minimize the variance of the output signal while keeping the expected signal response unchanged; perform fast Fourier transform (FFT) on the filtered sound wave signal to obtain its spectrum data; the spectrum data reflects the amplitude and phase characteristics of the signal at different frequency points; based on According to the sampling frequency of the acoustic signal and the target speed range, a theoretical value range of the Doppler shift is preset; within this range, the amplitude peak of the spectrum data is searched through a local peak search algorithm, such as binary search or the golden section method, and the corresponding frequency is used as the initial estimate of the Doppler shift; with the initial estimate of the Doppler shift as the state quantity in the linear system model, an iterative optimization framework based on the Kalman filter algorithm is established; the Kalman filter continuously corrects the estimation result of the Doppler shift by dynamically updating the state estimate and the error covariance matrix; the optimization process takes into account the influence of observation noise and process noise, so that the estimated value gradually converges to the true value; after the iterative optimization is completed, the converged Doppler shift value is used as the final estimation result; the estimated Doppler shift value is output as the input parameter for subsequent signal processing and target recognition tasks such as target speed estimation and distance measurement; accurate estimation of the Doppler shift is the key to realizing radar speed measurement and target recognition.

[0008] As a preferred solution, the method for positioning and controlling an anesthetic bullet based on acoustic wave guidance, wherein: the filtering process using the adaptive beamforming algorithm includes: obtaining the acoustic wave signal received by the microphone array and calculating its covariance matrix; using the minimum variance distortion-free response algorithm to adaptively beamform the acoustic wave signal according to the covariance matrix to suppress environmental noise and vibration interference, including: The initial estimated values ​​of the sound source frequency and Doppler shift when the anesthetic bullet is fired are obtained as the starting point of frequency estimation; the initial estimated values ​​are used to construct a theoretical spectrum model that takes into account the influence of the Doppler effect. This model describes the law of frequency change over time; the measured time domain sound signal is converted into a frequency domain spectrum through Fourier transform; the mean square error between the measured spectrum and the theoretical spectrum is calculated and defined as the objective function of maximum likelihood estimation, the goal of which is to minimize the mean square error; the objective function is iteratively optimized using the gradient descent algorithm, and the estimated frequency value is updated in each iteration so that the mean square error is gradually reduced until the objective function converges to the minimum value; according to the maximum likelihood estimation, the The optimal frequency value and the known sound source frequency are used to calculate the velocity component of the anesthetic bullet in the direction of the microphone array using the Doppler effect formula. According to the spatial position relationship of each microphone in the microphone array, the velocity component is decomposed into a three-dimensional coordinate system using the principle of triangulation to solve the three-dimensional motion velocity vector of the anesthetic bullet. The continuous multi-frame sound signal is processed to obtain a series of velocity vectors. The velocity vector is used as the observation quantity to establish a Kalman filter model. Through the two steps of prediction and update, the velocity vector sequence is filtered and smoothed to reduce the influence of measurement noise and improve the stability and accuracy of velocity estimation. Finally, the three-dimensional motion velocity estimation result of the anesthetic bullet is output.

[0009] As a preferred solution, the method for positioning and controlling an anesthetic bullet based on acoustic wave guidance includes: accurately estimating the Doppler frequency shift using the maximum likelihood algorithm, including: constructing a theoretical spectrum model that takes into account the influence of the Doppler effect; calculating the mean square error between the measured spectrum and the theoretical spectrum, and finding the frequency estimate that minimizes the mean square error through iterative optimization; and calculating the velocity component of the anesthetic bullet based on the frequency estimate and the sound source frequency, including: The mass, shape coefficient, drag coefficient and other physical parameters of the anesthetic bullet are obtained to establish a ballistic model of the anesthetic bullet, including a three-dimensional motion differential equation considering gravity, air resistance and wind force; meteorological data such as wind speed, wind direction, air pressure, and temperature at the time of launch are obtained and used as input parameters of the ballistic model; based on the initial launch speed and launch angle of the anesthetic bullet, combined with the ballistic model and meteorological parameters, the three-dimensional motion differential equation of the anesthetic bullet is numerically solved using the Runge-Kutta method to obtain a curve of the change of the anesthetic bullet position over time; based on the preset landing point accuracy requirements, it is judged whether the calculated anesthetic bullet trajectory meets the requirements. If not, the ballistic model parameters are adjusted and the trajectory is recalculated; the calculated three-dimensional space anesthetic bullet trajectory data is displayed using three-dimensional visualization tools such as OpenGL to generate an intuitive motion trajectory diagram; using the calculated complete anesthetic bullet trajectory, the landing point position of the anesthetic bullet is predicted by the end point of the trajectory, and the landing speed is predicted by the tangent line at the end of the trajectory, providing a reference basis for the actual anesthetic bullet launch.

[0010] As a preferred solution, the method for positioning and controlling anesthesia bullets based on acoustic wave guidance, wherein: predicting the trajectory of the anesthesia bullet comprises: obtaining physical parameters and meteorological data of the anesthesia bullet, establishing a ballistic model that takes into account the influence of gravity, air resistance, and wind; solving the ballistic differential equation using the Runge-Kutta method to obtain a curve of the change of the anesthesia bullet position over time; adjusting the ballistic parameters based on preset accuracy requirements to generate an optimized trajectory, including: According to the motion characteristics of the anesthetic bullet, a state space model describing its position and velocity changes is established, and the position coordinates and velocity components are used as state variables to establish the state transfer matrix and process noise matrix; the anesthetic bullet is continuously tracked by the Doppler radar to measure its Doppler frequency shift signal; according to the principle of the Doppler effect, a nonlinear observation equation between the frequency shift and the radial velocity is established, and the observation noise term is introduced; in the prediction stage of the Kalman filter, the state estimate value and the state transfer matrix at the previous moment are used to predict the position and velocity of the anesthetic bullet at the current moment, and the prediction error covariance matrix is ​​calculated; in the update stage of the Kalman filter, the Doppler frequency shift measurement value and the observation matrix at the current moment are used to calculate the Kalman gain, update the state estimate value and the estimation error covariance matrix; through recursive calculation prediction and update in two stages, dynamic tracking of the velocity vector of the anesthetic bullet is achieved to obtain optimized position and velocity estimates; based on the estimated velocity vector, combined with the ballistic differential equation of the anesthetic bullet, its flight trajectory is numerically integrated and predicted to obtain the position coordinates at subsequent moments; the Kalman filter algorithm is continuously iterated to update the state estimate of the anesthetic bullet in real time, and the Kalman gain is adjusted according to the prediction error covariance matrix, adaptively weighing the confidence of the predicted value and the measured value to improve the accuracy and robustness of velocity tracking; at the same time, an adaptive mechanism is introduced to dynamically adjust the covariance matrix of the process noise and the observation noise according to the statistical characteristics of the measurement residual, so as to adapt to the changes in the statistical characteristics of the noise in complex environments and ensure the stability of the filtering performance.

[0011] As a preferred solution, the method for positioning and controlling an anesthetic bullet based on acoustic wave guidance, wherein: the speed tracking by the Kalman filter algorithm includes: establishing a state space model of the motion of the anesthetic bullet, taking position and velocity as state variables; establishing a nonlinear observation equation of frequency shift and velocity based on Doppler frequency shift measurement values; obtaining an optimized velocity estimate through a recursive operation of two stages, prediction and update, including: Based on the optimized velocity vector, the relative position vector of the anesthetic bullet and each microphone array is calculated; a time synchronization mechanism is used to trigger multiple microphone arrays to simultaneously start collecting the sound signal of the anesthetic bullet; a related algorithm is used to calculate the time difference between the anesthetic bullet sound reaching different microphone arrays; based on the relative position vector of the anesthetic bullet and the microphone array and the sound arrival time difference, a nonlinear equation system with the spatial coordinates of the anesthetic bullet as unknowns is constructed; the Newton iteration method is used to solve the above nonlinear equation system to obtain the three-dimensional spatial coordinates of the anesthetic bullet; if the solved spatial coordinates are within a reasonable range, the coordinates are used as the current position of the anesthetic bullet; otherwise, the parameters in the nonlinear equation system are adjusted and the solution is repeated; the spatial coordinate sequence of the anesthetic bullet obtained by multiple consecutive measurements is input into the Kalman filter algorithm to smooth and optimize the motion trajectory of the anesthetic bullet and improve the positioning accuracy.

[0012] As a preferred solution, the method for controlling the positioning of an anesthetic bullet based on sound wave guidance, wherein: the calculation of spatial coordinates using the triangulation principle includes: obtaining synchronous observation data from multiple microphone arrays, calculating the time difference between the sound reaching different microphone arrays; constructing a nonlinear equation system with spatial coordinates as unknowns and solving it using the Newton iteration method; and smoothing and optimizing the coordinate sequence measured multiple times continuously through Kalman filtering, including: The current position coordinates and velocity information of the tranquilizer bomb are obtained, and the line-of-sight angular rate is calculated according to the preset target position; the lateral acceleration instruction is calculated according to the line-of-sight angular rate using the proportional guidance law, and the magnitude of the acceleration is proportional to the line-of-sight angular rate; the aerodynamic characteristic parameters and control surface efficiency coefficient of the tranquilizer bomb are obtained, and an aerodynamic model is established; the lateral acceleration instruction is input into the aerodynamic model, the control surface deflection angle is calculated, and the control instruction is obtained; it is determined whether the control instruction exceeds the maneuverability limit of the tranquilizer bomb, and if so, the instruction is limited; the control instruction is sent to the servo system of the tranquilizer bomb to guide its flight; the current position coordinates, velocity information and control instruction are recorded at the same time; if the tranquilizer bomb has not reached the target position, steps 1-6 are executed in a loop; if it has reached the target position, the guidance process is terminated and the flight data is uploaded to the database.

[0013] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a method for positioning and controlling a tranquilizer bullet based on acoustic wave guidance. The system establishes an acoustic wave propagation model, estimates the Doppler frequency shift range, and uses adaptive beamforming and maximum likelihood algorithms to accurately estimate the velocity of the tranquilizer bullet. The ballistic model is combined to predict the motion trajectory, and the Kalman filter is used to fuse Doppler measurement and trajectory prediction to achieve dynamic velocity tracking. Based on the synchronous observation of multiple microphone arrays, the three-dimensional coordinates of the tranquilizer bullet are solved using the triangulation principle. According to the position and velocity information, the control instructions are calculated according to the proportional guidance law to achieve precise guidance. The present invention overcomes the problem of insufficient accuracy of traditional guidance systems in complex environments, and improves positioning and tracking accuracy through multi-sensor fusion and advanced algorithms. The system fully considers the influence of the atmospheric environment, the characteristics of the tranquilizer bullet itself, and the aerodynamic characteristics, and achieves precise guidance throughout the entire process. The system can be widely used in fields such as wildlife protection and animal behavior research, and has important practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 The present invention is a flowchart of a method for controlling the positioning of an anesthetic bullet based on acoustic wave guidance.

[0016] Figure 2 The figure is a schematic diagram of a method for positioning and controlling an anesthetic bullet based on acoustic wave guidance according to the present invention.

[0017] Figure 3 is another schematic diagram of a method for positioning and controlling an anesthetic bullet based on acoustic wave guidance according to the present invention; DETAILED DESCRIPTION To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0018] Example 1: like Figure 1-3 In this embodiment, a method for controlling the positioning of an anesthetic bullet based on acoustic wave guidance may specifically include: Step S101 establishes a sound wave propagation model based on the initial velocity vector provided by the tranquilizer bullet launcher and the ambient wind speed, temperature, and humidity data measured by meteorological sensors. This model accounts for atmospheric absorption, geometric diffusion, and ground effects, calculating the attenuation of sound waves during propagation. Based on the attenuation value calculated by the sound wave propagation model and the expected velocity range of the tranquilizer bullet, the theoretical range of the Doppler shift is estimated. The upper limit corresponds to the frequency shift at the maximum expected velocity, and the lower limit corresponds to the frequency shift at the minimum expected velocity. The search interval for frequency measurement is set based on the estimated range.

[0019] The initial velocity vector data provided by the tranquilizer bullet launcher is obtained and corrected based on real-time ambient wind speed, temperature, and humidity data measured by meteorological sensors. This initial velocity vector is estimated to provide one of the input parameters for the acoustic wave propagation model. Based on this corrected initial velocity vector estimate, the acoustic wave propagation model is combined with factors such as atmospheric absorption, geometric diffusion, and ground effects to calculate the attenuation of the sound wave during propagation. The acoustic wave propagation model can use either a modified spherical diffusion model or a ray acoustics model suitable for outdoor environments. Based on the expected velocity range of the tranquilizer bullet and the attenuation calculated using the acoustic wave propagation model, the theoretical range of the Doppler shift is estimated using the Doppler effect formula. The relationship between the Doppler shift and the velocity of the tranquilizer bullet can be described by the following formula: f_d = f_0(c + v_r) / (c + v_s), where f_d is the Doppler shift, f_0 is the transmitted frequency of the sound wave, c is the speed of sound, v_r is the radial velocity of the tranquilizer bullet relative to the receiver, and v_s is the radial velocity of the tranquilizer bullet relative to the launcher. A search interval for frequency measurement is determined, with the upper limit corresponding to the frequency shift caused by the maximum expected velocity and the lower limit corresponding to the frequency shift caused by the minimum expected velocity. Within this search interval, the received acoustic signal is spectrally analyzed using a fast Fourier transform (FFT) algorithm to extract Doppler shift information. This extracted Doppler shift information is used as observations and fed into a tracker based on the extended Kalman filter (EKF) algorithm. Based on the kinematic model of the anesthetic projectile, the tracker dynamically estimates and predicts the velocity state of the projectile. The state estimate is updated and corrected based on the observations, improving the accuracy and robustness of the velocity estimation. If the velocity estimate output by the tracker exceeds a preset reasonable range (for example, exceeding the maximum flight speed of the projectile), it is considered an abnormality and an alarm mechanism is triggered. This alarm is transmitted in real time to the on-site commander's handheld device via a wireless communication module, indicating possible measurement errors or abnormal flight state of the projectile, and requiring timely countermeasures. The velocity estimate output by the tracker is fed back into the acoustic wave propagation model to dynamically adjust the model parameters to improve the accuracy of the acoustic wave attenuation calculation, forming a closed-loop optimization. At the same time, the speed estimate is combined with the flight time of the anesthetic bullet to calculate the real-time position coordinates of the anesthetic bullet, providing a basis for subsequent target positioning and tracking.

[0020] For example, after a tranquilizer dart is launched, its velocity and position need to be accurately tracked. This is crucial not only for the effectiveness of the anesthetic, but also for the safety of personnel on site. To achieve this, a combination of technologies is required, including acoustic wave propagation models, the Doppler effect, and Kalman filtering. First, the initial velocity vector of the dart must be determined. The launcher typically provides this data. However, due to the influence of ambient wind speed, temperature, and humidity, the actual initial velocity vector may deviate from the data provided by the launcher. For example, in a tailwind, the actual initial velocity of the dart may be greater than the data provided by the launcher, while in a headwind, it may be less. Temperature and humidity also affect air density, which in turn affects the velocity of the dart. Therefore, meteorological sensors are needed to measure these environmental parameters in real time and correct the initial velocity vector. Assuming the initial velocity provided by the launcher is 100 m / s and the wind speed is 10 m / s in the same direction as the dart's flight, the corrected initial velocity is approximately 110 m / s. Next, a sound wave propagation model is required. Since the dart flies in an outdoor environment, a modified spherical diffusion model or a ray acoustics model can be used. These models can comprehensively account for factors such as atmospheric absorption, geometric diffusion, and ground effects to calculate the attenuation of sound waves during propagation. For example, assume the frequency of a sound wave emitted by a tranquilizer bullet is 1000 Hz and the propagation distance is 100 meters. Based on the sound wave propagation model, the sound wave attenuation at this distance can be calculated, for example, to be 20 decibels. The Doppler effect formula can then be used to estimate the theoretical range of the Doppler shift. The Doppler shift is proportional to the velocity of the tranquilizer bullet. The faster the bullet, the greater the Doppler shift. Assuming the expected velocity of the tranquilizer bullet ranges from 80 to 120 m / s and the speed of sound is 340 m / s, the Doppler effect formula can be used to calculate the corresponding Doppler shift range, for example, from 200 to 300 Hz. Once the Doppler shift search range is determined, the fast Fourier transform (FFT) algorithm can be used to perform spectral analysis on the received sound wave signal to extract the Doppler shift information. For example, a 250 Hz frequency shift is detected in the received acoustic signal, indicating that the radial velocity of the anesthetic bullet is approximately 100 m / s. To improve the accuracy and robustness of velocity estimation, a tracker based on the extended Kalman filter (EKF) algorithm can be used. Based on the kinematic model of the anesthetic bullet, the tracker dynamically estimates and predicts the velocity state of the anesthetic bullet. For example, the tracker can predict the velocity at the next moment based on the current velocity estimate. The predicted value is then corrected based on the received Doppler frequency shift information to obtain a more accurate velocity estimate. If the velocity estimate output by the tracker exceeds a preset reasonable range, such as exceeding the maximum flight speed of the anesthetic bullet, it is judged as an abnormality and an alarm mechanism is triggered.For example, if the tracker outputs a velocity estimate of 200 meters per second (m / s), while the maximum flight speed of a tranquilizer bullet is 150 m / s, the system will sound an alarm, indicating a possible measurement error or abnormal flight status of the tranquilizer bullet. Finally, the velocity estimate output by the tracker is fed back to the acoustic wave propagation model, dynamically adjusting the model parameters to improve the accuracy of the acoustic wave attenuation calculation, forming a closed-loop optimization. Simultaneously, the velocity estimate is combined with the flight time of the tranquilizer bullet to calculate the real-time position coordinates of the tranquilizer bullet, providing a basis for subsequent target positioning and tracking. For example, if it is known that the tranquilizer bullet flew for 1 second at a speed of 100 m / s, the flight distance of the tranquilizer bullet can be inferred to be 100 meters.

[0021] Step S102 calculates the signal covariance matrix for the acoustic signal received by the microphone array. Adaptive beamforming is performed using the minimum variance distortionless response algorithm. This algorithm utilizes the covariance matrix to minimize the variance of the output signal while maintaining the desired signal distortion-free and suppressing interference from ambient noise and the vibration of the anesthetic bullet itself, thereby obtaining a filtered acoustic signal. A fast Fourier transform is performed on the filtered acoustic signal to obtain spectral data. A search is performed within a preset search interval to determine the spectral peak and obtain an initial estimate of the Doppler shift.

[0022] Acquiring the acoustic signal received by the microphone array, the original signal is preprocessed, including DC offset removal, windowing, and framing, to obtain a time-domain signal suitable for subsequent analysis. The covariance matrix of the preprocessed signal data is calculated. The elements of the covariance matrix represent the correlation between the signals received by different microphones. The formula is: R(i, j) = E[xi(t)xj(t)], where xi(t) and xj(t) represent the signals received by the i-th and j-th microphones, respectively, and E[] represents the mathematical expectation. Based on the calculated covariance matrix, the minimum variance distortionless response (MVDR) algorithm is used for adaptive beamforming. The MVDR algorithm aims to minimize the variance of the output signal while maintaining the desired signal response. A fast Fourier transform (FFT) is performed on the filtered acoustic signal to obtain its spectrum. The spectrum reflects the amplitude and phase characteristics of the signal at different frequencies. A theoretical range of Doppler shift values ​​is preset based on the acoustic signal's sampling frequency and target velocity range. Within this interval, a local peak search algorithm, such as a binary search or golden section method, is used to search for amplitude peaks in the spectrum data, and the corresponding frequency is used as the initial estimate of the Doppler shift. Using this initial estimate of the Doppler shift as the state variable in the linear system model, an iterative optimization framework based on the Kalman filter algorithm is established. The Kalman filter continuously refines the Doppler shift estimate by dynamically updating the state estimate and the error covariance matrix. The optimization process accounts for the effects of observation and process noise, allowing the estimate to gradually converge to the true value. After the iterative optimization is complete, the converged Doppler shift value is used as the final estimate. The estimated Doppler shift value is output as an input parameter for subsequent signal processing and target recognition tasks, such as target velocity estimation and range measurement. Accurate Doppler shift estimation is crucial for radar velocity measurement and target recognition.

[0023] For example, the acoustic signal received by a microphone array contains both target information and ambient noise. To extract useful information, the original signal needs to be preprocessed. Preprocessing operations include DC offset removal, windowing, and framing. DC offset removal eliminates static offsets in the signal, windowing reduces spectral leakage, and framing divides the signal into short segments for subsequent analysis. For example, a microphone array receives the sound signal of a tranquilizer bomb in flight, which is mixed with ambient noise and a DC component. First, the DC component needs to be removed to more accurately analyze the changes in the sound signal. Then, the signal is windowed, such as using a Hanning window, to reduce spectral leakage and improve the accuracy of frequency estimation. Finally, the windowed signal is divided into several frames, for example, each lasting 20 milliseconds, so that each frame can be analyzed separately. Next, the covariance matrix of the preprocessed signal needs to be calculated. The covariance matrix describes the correlation between the signals received by different microphones. For example, if the signals received by two microphones are highly correlated, it means that the sound sources they received are similar. The formula for calculating the covariance matrix is ​​R(i, j) = E[xi(t)xj(t)]. Where xi(t) and xj(t) represent the signals received by the i-th and j-th microphones, respectively, and E represents the mathematical expectation. Suppose there are two microphones, each with signals x1(t) and x2(t). By calculating the expected value E[x1(t)x2(t)] of these two signals, we can determine the correlation between them. If this value is large, it indicates that the signals received by the two microphones are highly correlated. Based on the covariance matrix, the minimum variance distortionless response (MVDR) algorithm can be used for adaptive beamforming. The MVDR algorithm aims to minimize the variance of the output signal while maintaining the expected signal response. In layman's terms, this enhances the signal from the target direction and suppresses noise from other directions. Assuming a tranquilizer bullet is positioned directly in front of the target, the MVDR algorithm adjusts the weights of the microphone array to enhance the signal from the target direction and suppress noise from other directions. This improves the signal-to-noise ratio, making it easier to extract target information. To evaluate the effectiveness of adaptive beamforming, we need to calculate the correlation coefficient between the output signal and the original signal. The correlation coefficient reflects the degree of similarity between the two signals. If the correlation coefficient is greater than a preset threshold, such as 8, the beamforming effect is considered satisfactory. Otherwise, the MVDR algorithm parameters need to be adjusted, such as increasing the number of iterations and adjusting the convergence criteria, until quality requirements are met. For example, if the correlation coefficient between the output signal and the original signal is only 5, the beamforming effect is poor. The MVDR algorithm parameters need to be adjusted and beamforming should be repeated until the correlation coefficient reaches 8 or above. A fast Fourier transform (FFT) is performed on the filtered acoustic signal to obtain its spectrum data. The spectrum data reflects the amplitude and phase characteristics of the signal at different frequency points.For example, if the sound frequency of a tranquilizer bullet is 1kHz, the amplitude at 1kHz will be very large on the spectrum. By analyzing the spectrum data, Doppler shift information can be extracted. Based on a preset Doppler shift range, a local peak search algorithm, such as a binary search or golden section method, can be used to search for the amplitude peak in the spectrum data. The corresponding frequency is used as the initial estimate of the Doppler shift. For example, if the preset Doppler shift range is 200Hz to 300Hz, the spectrum is searched for the maximum amplitude within this range. The corresponding frequency is the initial estimate of the Doppler shift. To further improve the accuracy of Doppler shift estimation, a Kalman filter can be used. The Kalman filter is an iterative optimization algorithm that dynamically updates state estimates based on observed data and system models. For example, if the initial estimate of the Doppler shift is 250Hz, the Kalman filter will continuously refine this estimate based on subsequent observations, bringing it closer to the true value. Finally, the Doppler shift value output by the Kalman filter is used as the final estimate. This result can be used for subsequent signal processing and target recognition tasks such as target velocity estimation and distance measurement. For example, the speed of an anesthetic bullet can be calculated based on the magnitude of the Doppler shift.

[0024] In step S103, a maximum likelihood algorithm is used to accurately estimate the Doppler shift frequency based on the initial estimate. A theoretical spectrum model is constructed, accounting for the influence of the Doppler effect on the spectrum. The objective function is defined as the mean square error between the measured and theoretical spectrums. An iterative optimization process is used to find the frequency that minimizes the objective function. Using the estimated frequency value and the sound source frequency, the velocity component of the anesthetic bullet in the direction of the microphone array is calculated.

[0025] Initial estimates of the sound source frequency and Doppler shift at the time of the anesthetic bullet launch are obtained as the starting point for frequency estimation. Using these initial estimates, a theoretical spectral model is constructed that accounts for the Doppler effect. This model describes how frequency varies over time. The measured time-domain sound signal is converted into a frequency-domain spectrum via Fourier transform. The mean squared error between the measured and theoretical spectrum is calculated and defined as the objective function for maximum likelihood estimation, with the goal of minimizing the mean squared error. A gradient descent algorithm is used to iteratively optimize the objective function, updating the estimated frequency value with each iteration to gradually reduce the mean squared error until the objective function converges to a minimum. Based on the optimal frequency value obtained from the maximum likelihood estimate and the known sound source frequency, the Doppler effect formula is used to calculate the velocity component of the anesthetic bullet in the direction of the microphone array. Based on the spatial relationship between the microphones in the array and using the principle of triangulation, the velocity components are decomposed into a three-dimensional coordinate system to determine the three-dimensional velocity vector of the anesthetic bullet. Continuous frames of the sound signal are processed to obtain a series of velocity vectors. Using the velocity vector as the observed quantity, a Kalman filter model is established. Through two steps, prediction and update, the velocity vector sequence is filtered and smoothed, reducing the impact of measurement noise and improving the stability and accuracy of velocity estimation. The final result is a three-dimensional velocity estimate of the anesthetic bullet.

[0026] For example, determining the frequency of the sound source when a tranquilizer bullet is fired is crucial, as it forms the basis for subsequent Doppler shift estimation. Assume that a tranquilizer bullet emits a specific frequency, such as 1 kHz, when fired. This known frequency serves as the starting point for frequency estimation. Due to the Doppler effect, the frequency of the received sound changes during the bullet's motion. A theoretical model is needed to describe this change. The simplest model assumes uniform linear motion, in which case the frequency change is proportional to the velocity. For example, as the bullet moves away from the microphone array, the received frequency decreases; conversely, as it approaches, the frequency increases. Performing a Fourier transform on the collected sound signal yields the signal's spectrum. The spectrum reflects the intensity of different frequency components in the signal. For example, if the sound frequency emitted by the tranquilizer bullet is 1 kHz, and due to the Doppler effect, the received frequency shifts to 1 kHz, the amplitude at 1 kHz will be larger on the spectrum plot. To estimate the velocity of the tranquilizer bullet, the difference between the measured spectrum and the theoretical spectrum needs to be compared. The mean squared error (MSE) can be used to measure this difference. The smaller the MSE, the more accurately the theoretical model matches the actual situation. For example, if the theoretical model predicts a frequency of 1 kHz, but the measured spectrum peaks at 0.5 kHz, the mean squared error (MSE) will be large. A gradient descent algorithm can be used to minimize the MSE. This algorithm continuously adjusts the estimated frequency to gradually reduce the MSE. For example, if the initial estimated frequency is 1 kHz, after one iteration, the estimate might be adjusted to 0.8 kHz. If the MSE further decreases, it indicates that the adjustment was correct. Maximum likelihood estimation can be used to find the optimal frequency that minimizes the difference between the measured and theoretical spectra. For example, a final estimated frequency of 0.6 kHz indicates that the actual velocity of the anesthetic bullet is slower than initially assumed. Using the Doppler effect formula, the velocity components of the anesthetic bullet can be calculated based on the optimal frequency and the known source frequency. For example, if the source frequency is 1 kHz, the estimated frequency is 0.6 kHz, and the speed of sound is 340 m / s, the velocity components of the anesthetic bullet in the direction of the microphone array can be calculated. The microphone array consists of multiple microphones whose spatial positions are known. Using the principle of triangulation, the velocity components can be decomposed into a three-dimensional coordinate system. For example, if a microphone array consists of three microphones forming a triangle, the velocity vector of the tranquilizer bullet in three-dimensional space can be calculated based on the signals received by the three microphones. By processing multiple frames of continuous sound signals, a series of velocity vectors can be obtained. These velocity vectors reflect the trajectory of the tranquilizer bullet. For example, if the tranquilizer bullet moves in a uniform straight line, the direction and magnitude of these velocity vectors should be roughly the same. A Kalman filter can be used to smooth the velocity vector sequence and reduce the impact of measurement noise. The Kalman filter estimates the current state based on the previous state and makes corrections based on the observed data.For example, if a velocity measurement at a particular moment shows an anomaly, the Kalman filter will correct the measurement based on previous velocity estimates and the system model, bringing it closer to the true value. This Kalman filter produces more stable and accurate velocity estimates. The resulting three-dimensional velocity estimate of the tranquilizer bullet can be used for subsequent applications such as target tracking and trajectory prediction. For example, the estimated velocity can be used to predict the impact point of the tranquilizer bullet.

[0027] Step S104 predicts the trajectory of the anesthetic bullet based on the calculated velocity components, combined with the bullet's trajectory model and meteorological data. The trajectory model accounts for the effects of gravity, air resistance, and wind, and includes parameters such as mass, shape coefficient, and drag coefficient. The Runge-Kutta method is used to solve the differential equation to determine the trajectory of the anesthetic bullet's position in three-dimensional space over time.

[0028] Physical parameters such as the mass, shape coefficient, and drag coefficient of the anesthetic bullet are obtained to establish a ballistic model for the bullet, including a three-dimensional differential equation of motion that accounts for gravity, air resistance, and wind forces. Meteorological data such as wind speed, wind direction, air pressure, and temperature at the time of launch are obtained and used as input parameters for the ballistic model. Based on the initial launch velocity and launch angle of the anesthetic bullet, the Runge-Kutta method is used to numerically solve the three-dimensional differential equation of motion for the bullet, combining the ballistic model and meteorological parameters to obtain a curve of the bullet's position over time. Based on the preset landing point accuracy requirements, the calculated trajectory is determined to determine whether it meets the requirements. If not, the trajectory model parameters are adjusted and the trajectory is recalculated. The calculated three-dimensional trajectory data is displayed using 3D visualization tools such as OpenGL to generate an intuitive trajectory diagram. Using the calculated complete trajectory, the bullet's landing point is predicted using the trajectory endpoint, and the landing velocity is predicted using the trajectory end tangent, providing a reference for actual anesthetic bullet launches.

[0029] For example, predicting the trajectory of a tranquilizer bullet is crucial for accurate shooting. First, the physical parameters of the bullet itself must be determined, such as its mass (assumed to be 100 grams), its shape factor (assumed to be 5, a dimensionless parameter that describes the effect of an object's shape on air resistance), and its drag coefficient (assumed to be 2, also a dimensionless parameter that, along with the shape factor, determines the magnitude of air resistance). These parameters can be obtained through experimental measurements or by consulting relevant manuals. Once these parameters are determined, a ballistic model for the tranquilizer bullet can be constructed. The core of the ballistic model is a differential equation describing the three-dimensional motion of the tranquilizer bullet. This equation accounts for multiple forces, the most important of which is gravity, which forces the bullet downward. Air resistance, which is in the opposite direction of the bullet's velocity, is proportional to the square of the velocity and is affected by the shape factor and the drag coefficient. Wind force exerts additional force on the tranquilizer bullet depending on its speed and direction. Assume that when the tranquilizer bullet is launched, the wind speed is 5 meters per second and the direction is due east. The air pressure is standard atmospheric pressure, and the temperature is 25 degrees Celsius. These meteorological parameters affect the air density, which in turn influences the magnitude of air resistance. When launched, a tranquilizer bullet has a certain initial velocity and launch angle. Assume the initial velocity is 60 meters per second and the launch angle is 45 degrees. By substituting these initial conditions, physical parameters, and meteorological data into the trajectory model, a numerical method can be used to solve the trajectory of the tranquilizer bullet. The Runge-Kutta method is a commonly used numerical solution method that gradually calculates the position and velocity of the tranquilizer bullet at different time points. Using the Runge-Kutta method, a series of discrete time points and corresponding three-dimensional coordinates of the tranquilizer bullet are obtained. For example, at the first second, the coordinates of the tranquilizer bullet may be (10, 10, 5), and at the second second, the coordinates may be (20, 20, 8). These coordinates constitute the trajectory of the tranquilizer bullet. Preset landing accuracy requirements, such as requiring a landing error within 1 meter, are set. The calculated trajectory is compared with the target position. If the error is excessive, for example, exceeding 1 meter, adjustments are made to the trajectory model parameters, such as the drag coefficient or initial velocity, and the trajectory is recalculated until the accuracy requirements are met. To more intuitively visualize the trajectory of the tranquilizer bullet, the trajectory data can be plotted using 3D visualization tools such as OpenGL. The trajectory diagram clearly displays the trajectory of the tranquilizer bullet in three-dimensional space, facilitating analysis and adjustment of launch parameters. Ultimately, based on the calculated complete trajectory, the bullet's impact point and landing velocity can be predicted. The impact point is the endpoint of the trajectory, for example, (100, 100, 0). The landing velocity can be calculated from the direction and magnitude of the tangent line at the trajectory endpoint. For example, a velocity of 20 m / s in a vertically downward direction can be calculated. These predictions can provide a reference for actual tranquilizer bullet launches, improving shooting accuracy. For example, if the predicted impact point is significantly off target, the launch angle or initial velocity can be adjusted accordingly to improve the hit rate.

[0030] In step S105, a Kalman filter algorithm is used to dynamically track the velocity vector of the anesthetic bullet by fusing the Doppler shift measurement and trajectory prediction. The state equation describes the position and velocity of the anesthetic bullet, while the observation equation establishes the nonlinear relationship between Doppler shift and velocity. Through a recursive calculation of the two stages of prediction and update, an optimized velocity estimate is obtained.

[0031] Based on the motion characteristics of the anesthetic bullet, a state-space model is established to describe its position and velocity changes. Position coordinates and velocity components are used as state variables, and a state transition matrix and process noise matrix are constructed. The anesthetic bullet is continuously tracked using a Doppler radar, measuring its Doppler frequency shift signal. Based on the principle of the Doppler effect, a nonlinear observation equation is established between the frequency shift and radial velocity, introducing an observation noise term. In the prediction phase of the Kalman filter, the state estimate and state transition matrix at the previous moment are used to predict the current position and velocity of the anesthetic bullet, and the prediction error covariance matrix is ​​calculated. In the update phase of the Kalman filter, the current Doppler frequency shift measurement and the observation matrix are used to calculate the Kalman gain, update the state estimate, and update the estimation error covariance matrix. Through recursive calculations in the prediction and update phases, dynamic tracking of the anesthetic bullet's velocity vector is achieved, resulting in optimized position and velocity estimates. Based on the estimated velocity vector and combined with the anesthetic bullet's ballistic differential equation, the flight trajectory is numerically integrated to predict the position coordinates at subsequent moments. The Kalman filter algorithm continuously iterates, updating the state estimate of the tranquilizer pellet in real time. The Kalman gain is adjusted based on the prediction error covariance matrix, adaptively balancing the confidence of predicted and measured values ​​to improve the accuracy and robustness of velocity tracking. Furthermore, an adaptive mechanism is introduced to dynamically adjust the covariance matrix between process and observation noise based on the statistical characteristics of the measurement residuals, adapting to changing noise statistics in complex environments and ensuring the stability of the filtering performance.

[0032] For example, the Kalman filter is a powerful state estimation technique, particularly suitable for dynamic systems with process and measurement noise, such as tranquilizer bullet trajectory prediction. Its core concept is to optimally estimate the system state based on the system model and measurement data. First, a state-space model of the tranquilizer bullet is established. The three-dimensional position coordinates (x, y, z) and three-dimensional velocity components (Vx, Vy, Vz) of the tranquilizer bullet are used as state variables to form a six-dimensional state vector. The state transition matrix describes the motion of the tranquilizer bullet in the absence of external forces. For example, velocity affects position changes, while gravity affects velocity changes. The process noise matrix reflects model uncertainty, such as the influence of unmodeled factors like wind disturbances. Assuming that the process noise follows a Gaussian distribution, its variance can be set based on actual conditions, for example, to 1. Doppler radar can measure the radial velocity of the tranquilizer bullet, that is, the velocity component in the radar's line of sight. Based on the Doppler effect, the frequency shift of the radar signal is proportional to the radial velocity of the tranquilizer bullet. It is important to note that this observation equation is nonlinear, as it involves the calculation of radial velocity and requires the use of trigonometric functions. Observation noise primarily originates from radar measurement errors and is also assumed to conform to a Gaussian distribution. Its variance can be set based on the radar's accuracy, for example, to 0.5. The Kalman filter algorithm consists of two phases: prediction and update. In the prediction phase, the current state and prediction error covariance matrix are predicted using the previous state estimate, the state transition matrix, and the process noise covariance matrix. For example, if the previous estimated velocity of a tranquilizer bullet was 10 m / s² and its acceleration was -8 m / s² (gravity), the predicted velocity at the current moment is 10-8*Δt, where Δt is the time step. In the update phase, the Kalman gain is calculated using the current Doppler shift measurement, the observation matrix, and the observation noise covariance matrix. The Kalman gain is a coefficient that weighs the predicted and measured values, determining which the filter trusts more. For example, if the radar measurement accuracy is high, the Kalman gain will be large, and the filter will tend to trust the measured value. The state estimate and the estimation error covariance matrix are then updated based on the Kalman gain, the predicted value, and the measured value, resulting in a more accurate state estimate. By iterating through the two stages of prediction and update, dynamic tracking of the tranquilizer bullet's velocity vector can be achieved, resulting in optimized position and velocity estimates. The Kalman filter effectively integrates model predictions and radar measurements, achieving relatively accurate state estimates even in the presence of noise. Using the estimated velocity vector and the ballistic differential equation, the flight trajectory of the tranquilizer bullet can be numerically integrated and predicted. For example, assuming the estimated velocity of the tranquilizer bullet at time t is v(t), its position at time t+Δt can be approximately expressed as x(t+Δt)=x(t)+v(t)*Δt. The adaptive Kalman filter dynamically adjusts the covariance matrix of process noise and observation noise based on the statistical characteristics of the measurement residuals.For example, if the measurement residual continues to increase, indicating that the model's prediction error is increasing, it is necessary to increase the process noise covariance to improve the filter's adaptability. This has the advantage of being able to adapt to the changing statistical characteristics of noise in complex environments and ensure the stability of filtering performance. For example, in complex terrain, where wind speed and direction are unpredictable, the adaptive Kalman filter can adjust the noise covariance based on actual conditions to improve tracking accuracy.

[0033] Step S106: Based on the optimized velocity vector, the relative position vector of the anesthetic bullet and the microphone array is calculated. A time synchronization mechanism is employed to ensure that at least three microphone arrays simultaneously observe the anesthetic bullet. Combining the measurement data from multiple microphone arrays, the three-dimensional coordinates of the anesthetic bullet are determined using triangulation principles. A nonlinear system of equations is constructed, and the Newton iteration method is used to solve for the position coordinates of the anesthetic bullet.

[0034] Based on the optimized velocity vector, the relative position vector between the anesthetic bullet and each microphone array is calculated. Using a time synchronization mechanism, multiple microphone arrays are triggered to simultaneously capture the sound signal of the anesthetic bullet. A correlation algorithm is used to calculate the time difference between the arrival of the anesthetic bullet sound at different microphone arrays. Based on the relative position vector between the anesthetic bullet and the microphone array and the sound arrival time difference, a nonlinear equation system is constructed with the spatial coordinates of the anesthetic bullet as unknowns. The Newton iteration method is used to solve this nonlinear equation system to obtain the three-dimensional spatial coordinates of the anesthetic bullet. If the solved spatial coordinates are within a reasonable range, these coordinates are used as the current position of the anesthetic bullet; otherwise, the parameters in the nonlinear equation system are adjusted and the solution is repeated. The sequence of spatial coordinates of the anesthetic bullet obtained from multiple consecutive measurements is input into the Kalman filter algorithm to smooth and optimize the trajectory of the anesthetic bullet and improve positioning accuracy.

[0035] For example, the velocity vector optimized by the Kalman filter algorithm provides the basis for subsequent calculation of the anesthetic bullet's position. This velocity vector can be used to calculate the relative position vectors between the anesthetic bullet and each microphone array. For example, if the velocity vector of the anesthetic bullet is (3, 4, 5) meters / second, the coordinates of a microphone array are (10, 10, 10) meters, and the anesthetic bullet's position at the previous moment was (7, 6, 5) meters, assuming a 1-second interval, the predicted position of the anesthetic bullet at the current moment is (10, 10, 10) meters, and the relative position vector is (0, 0, 0) meters. To accurately measure the time difference between the arrival of the anesthetic bullet sound at different microphone arrays, a time synchronization mechanism is required. For example, a GPS timing system can be used to ensure that all microphone arrays start recording at the same time. This eliminates time deviations between different microphone arrays, thereby improving the accuracy of time difference measurements. Assuming that the anesthetic bullet sound signal arrives at all three microphone arrays simultaneously, this indicates that the anesthetic bullet is likely located near the intersection of the planes formed by the three microphone arrays. Using a related algorithm, the time difference between the arrival of the anesthetic bullet sound at the different microphone arrays can be calculated. The basic principle of the cross-correlation algorithm is to calculate the correlation between two signals at different time delays. The cross-correlation function reaches its peak when the time delay between the two signals equals the actual time difference in sound propagation. For example, if the time difference between the arrival of the anesthetic bullet at microphone array 1 and microphone array 2 is 0.001 seconds, and the speed of sound is 340 m / s, then the distance difference between the anesthetic bullet and the two microphone arrays is 0.34 meters. Based on the relative position vectors of the anesthetic bullet and the microphone arrays and the time difference in sound arrival, a nonlinear system of equations can be constructed with the spatial coordinates of the anesthetic bullet as the unknowns. Each microphone array provides an equation, which generally takes the form of the distance from the anesthetic bullet to that microphone array equals the sound propagation time multiplied by the speed of sound. For example, if the coordinates of the anesthetic bullet are (x, y, z), the coordinates of microphone array 1 are (x1, y1, z1), and the time difference is t1, then the equation can be expressed as sqrt((x-x1)^2+(y-y1)^2+(z-z1)^2)=t1*340. The Newton iteration method is used to solve this nonlinear system of equations. The Newton iteration method is a commonly used numerical method for solving nonlinear equations. Its basic idea is to use the Taylor expansion of a function to approximate the nonlinear equations into a linear system, which is then solved iteratively. For example, assuming the initial position estimate of the tranquilizer bullet is (0, 0, 0), by continuously revising this estimate using the Newton iteration method, a solution that satisfies the equations can eventually be obtained. The calculated spatial coordinates of the tranquilizer bullet need to be checked for rationality. If the coordinates fall within a preset range, they are considered to be the current position of the tranquilizer bullet. Otherwise, the parameters in the nonlinear equations, such as the time difference or the speed of sound, need to be adjusted and the solution repeated. For example, if the calculated coordinates of the tranquilizer bullet are below the ground, this is clearly unreasonable, and the input data and calculation process need to be re-examined.To improve positioning accuracy, the spatial coordinate sequence of the tranquilizer bullet, obtained through multiple consecutive measurements, can be fed into a Kalman filter algorithm for smoothing and optimization. The Kalman filter effectively filters out measurement noise and predicts the future position of the tranquilizer bullet. For example, if the coordinates of the tranquilizer bullet obtained from three consecutive measurements are (1, 1, 1), (2, 2, 2), and (3, 3, 3), the Kalman filter can predict the next position of the tranquilizer bullet to be (4, 4, 4). This results in a smoother and more accurate trajectory.

[0036] Step S107: Based on the obtained position coordinates and velocity information, a correction is calculated using the proportional guidance law. Based on the line-of-sight angular velocity, the proportional guidance law generates a lateral acceleration command proportional to the line-of-sight angular velocity. The acceleration command is converted into a control surface deflection angle, and the required deflection angle is calculated, taking into account the aerodynamic characteristics of the tranquilizer round and the efficiency of the control surface. A control command is generated to guide the tranquilizer round. The position coordinates, velocity information, and control command are recorded in a database.

[0037] Obtain the current position coordinates and velocity information of the tranquilizer bullet, and calculate the line-of-sight angular rate based on the preset target position. Use the proportional guidance law to calculate the lateral acceleration command based on the line-of-sight angular rate, and the magnitude of the acceleration is proportional to the line-of-sight angular rate. Obtain the aerodynamic characteristic parameters and control surface efficiency coefficient of the tranquilizer bullet and establish an aerodynamic model. Input the lateral acceleration command into the aerodynamic model, calculate the deflection angle of the control surface, and obtain the control command. Determine whether the control command exceeds the maneuverability limit of the tranquilizer bullet. If so, limit the command. Send the control command to the servo system of the tranquilizer bullet to guide its flight. Simultaneously record the current position coordinates, velocity information, and control command. If the tranquilizer bullet has not reached the target position, loop through steps 1-6; if it has reached the target position, end the guidance process and upload the flight data to the database.

[0038] For example, the guidance and control process for a tranquilizer bullet can be summarized as follows: acquiring the current state, calculating the required control variable, converting it into a control command, applying the control command, and recording it. First, the current position coordinates and velocity of the tranquilizer bullet must be acquired. For example, GPS can provide location information, while Doppler radar can provide velocity information. This information forms the basis for control. Pre-set target location information, such as the location of the target animal, is also necessary. With the current and target positions, the line-of-sight angular velocity can be calculated. This angular velocity refers to the rate of change in the direction of the line connecting the tranquilizer bullet and the target. For example, if the tranquilizer bullet is flying directly toward the target, the line-of-sight angular velocity is zero. If the direction of the line connecting the tranquilizer bullet and the target changes rapidly, the line-of-sight angular velocity is higher. Proportional guidance is a commonly used guidance method that calculates the required lateral acceleration command based on the line-of-sight angular velocity. The magnitude of the acceleration command is proportional to the line-of-sight angular velocity, meaning that a greater line-of-sight angular velocity requires a greater lateral acceleration, enabling rapid adjustment of the tranquilizer bullet's flight direction to align with the target. The proportional coefficient needs to be adjusted based on actual conditions. Too large a value can result in overly sensitive control, while too small a value can lead to sluggish control. For example, if the line-of-sight angular rate is 1 rad / s and the scale factor is 2, the lateral acceleration command is 2 m / s². To convert the acceleration command into a control surface deflection angle, an aerodynamic model of the tranquilizer projectile is required. This aerodynamic model describes the projectile's aerodynamic characteristics, such as the lift coefficient, drag coefficient, and control surface efficiency coefficient. These parameters determine the relationship between the control surface deflection angle and the generated acceleration. For example, a larger control surface deflection angle generates greater lift, resulting in greater lateral acceleration. Assuming the aerodynamic model of the tranquilizer projectile is linear, the acceleration command can be converted into a control surface deflection angle using a simple linear relationship. For example, if the acceleration command is 2 m / s² and the control surface efficiency coefficient is 1 rad / (m / s²), the control surface deflection angle is 0.2 rad. Due to the limited maneuverability of the tranquilizer projectile, the control command may exceed the maximum achievable control surface deflection angle or angular velocity. Therefore, the control command needs to be clipped. For example, if the calculated control surface deflection angle is 1 rad, and the maximum deflection angle of the tranquilizer bullet is 0.5 rad, the control instruction needs to be limited to 0.5 rad. This can prevent damage to the servo or loss of control of the tranquilizer bullet. Finally, the limited control instruction is sent to the servo system of the tranquilizer bullet. The servo system will adjust the deflection angle of the control surface according to the instruction, thereby guiding the flight of the tranquilizer bullet. At the same time, the current position coordinates, speed information and control instructions need to be recorded for subsequent analysis and improvement of the control algorithm. For example, this data can be stored in the onboard memory and uploaded to the database after the mission is completed. If the tranquilizer bullet does not reach the target position, the above steps need to be executed repeatedly, and the control instructions need to be continuously updated until the tranquilizer bullet reaches the target position. If the tranquilizer bullet has reached the target position, the guidance process is terminated and the flight data is uploaded to the database for subsequent analysis and evaluation.For example, flight trajectories and control command changes can be analyzed to evaluate the performance of the guidance system.

[0039] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A method for positioning and controlling anesthesia bullets based on acoustic wave guidance, characterized in that: include: The initial velocity vector provided by the tranquilizer bullet launcher is obtained and combined with the environmental parameters measured by the meteorological sensor to establish a sound wave propagation model. Adaptive beamforming algorithm is used to filter the acoustic signal received by the microphone array to obtain an initial estimate of the Doppler frequency shift. The maximum likelihood algorithm is used to accurately estimate the Doppler frequency shift and calculate the velocity component of the anesthetic bullet in the direction of the microphone array. According to the velocity component and meteorological data, combined with the ballistic model of the tranquilizer bullet, the trajectory of the tranquilizer bullet is predicted; The velocity of the anesthetic bullet is tracked by fusing Doppler frequency shift measurements and trajectory predictions through the Kalman filter algorithm. Based on the optimized velocity vector, the three-dimensional coordinates of the anesthetic bullet are calculated using the triangulation principle. Based on the position coordinates and speed information, control instructions are generated to guide the anesthetic bullet and recorded in the database.

2. The method according to claim 1, wherein Describing the establishment of the sound wave propagation model includes: Acquiring the initial velocity vector data of the anesthetic bullet and environmental parameters, correcting the initial velocity vector, and obtaining an estimated value of the initial velocity vector of the anesthetic bullet in the actual environment; According to the estimated value of the velocity vector, the modified spherical diffusion model is used to calculate the attenuation of the sound wave during the propagation process.

3. The method according to claim 2, wherein The filtering process using the adaptive beamforming algorithm includes: Obtain the sound wave signal received by the microphone array and calculate its covariance matrix; The minimum variance distortion-free response algorithm is used to perform adaptive beamforming on the acoustic signal according to the covariance matrix to suppress environmental noise and vibration interference.

4. The method according to claim 3, wherein The method of using the maximum likelihood algorithm to accurately estimate the Doppler frequency shift includes: Construct a theoretical spectrum model that takes into account the influence of the Doppler effect; Calculate the mean square error between the measured spectrum and the theoretical spectrum, and find the frequency estimate that minimizes the mean square error through iterative optimization; Calculate the velocity component of the anesthetic bullet based on the frequency estimate and the sound source frequency.

5. The method according to claim 4, wherein The predicted trajectory of the anesthetic bullet includes: Obtain the physical parameters and meteorological data of tranquilizer bullets and establish a ballistic model that takes into account the effects of gravity, air resistance, and wind; The Runge-Kutta method is used to solve the ballistic differential equation and the curve of the position of the anesthetic bullet changing with time is obtained. Based on the preset accuracy requirements, the ballistic parameters are adjusted to generate an optimized motion trajectory.

6. The method according to claim 5, wherein The speed tracking by the Kalman filter algorithm includes: Establish a state space model of the anesthetic bullet's motion, taking position and velocity as state variables; According to the Doppler frequency shift measurement value, a nonlinear observation equation of frequency shift and velocity is established; Through the recursive operation of the two stages of prediction and update, the optimized speed estimation value is obtained.

7. The method according to claim 1, wherein The method of calculating the spatial coordinates by using the triangulation principle includes: Acquire synchronous observation data from multiple microphone arrays and calculate the time difference between the sound reaching different microphone arrays; Construct a system of nonlinear equations with spatial coordinates as unknowns and solve them using Newton's iteration method; The coordinate sequence measured multiple times continuously is smoothed and optimized through Kalman filtering.

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