A method and system for acoustic repulsion

Abstract

This application provides a method and system for acoustic wave deterrence. The method involves acquiring millimeter-wave radar point cloud data, dynamic compensation from AIS message parsing, and ship threat level parameters. Based on these data and parameters, a ship motion model is constructed to predict the threat area, and the three-dimensional coverage range of the acoustic wave is determined by combining the ship's length-to-beam ratio. Subsequently, the transducer array beam parameters are calculated to generate an acoustic wave deterrence waveform command. Through millimeter-wave radar, AIS message parsing, and algorithm processing, the acoustic wave coverage range and command are accurately determined, significantly improving deterrence accuracy and efficiency, and ensuring maritime traffic safety and unimpeded flow.

Description

Technical Field

[0001] This application pertains to the field of waterway clearance, and particularly relates to an acoustic clearance method and system. Background Technology

[0002] In the field of maritime traffic management and safety monitoring, timely identification and effective removal of potentially threatening vessels are crucial for ensuring unobstructed navigation and maritime safety. Traditional removal methods often rely on visual identification or simple radar detection, which suffer from insufficient accuracy, slow response speed, and susceptibility to environmental interference. With technological advancements, acoustic removal, as a non-contact safety method, has gained increasing attention. However, most existing acoustic removal systems lack accurate prediction of vessel movement and adaptability to dynamic environments, resulting in inaccurate acoustic coverage, energy waste, and poor removal effectiveness. Summary of the Invention

[0003] The purpose of this application is to overcome the deficiencies in the prior art and provide a method and system for acoustic wave expulsion.

[0004] This application provides an acoustic wave repulsion method, including:

[0005] Acquire millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters;

[0006] Based on the dynamic compensation parameters and millimeter-wave radar point cloud data, a ship motion model is constructed.

[0007] Based on the ship motion model and ship characteristic parameters in the AIS message, threat area prediction data is generated.

[0008] Based on the predicted threat area data and the ship's length-to-width ratio parameters, the three-dimensional coverage range of the acoustic wave effect is determined;

[0009] Based on the three-dimensional coverage area and the preset safety threshold, the transducer array beamforming parameters are calculated.

[0010] Based on the array beamforming parameters and the ship threat level parameters, generate an acoustic drive-away waveform command;

[0011] The target is driven away using sound waves according to the sound wave drive-away waveform command.

[0012] Optionally, the construction of the ship motion model includes:

[0013] A hybrid architecture combining a Kalman filter framework and a particle filter correction module is adopted.

[0014] When the ship's turning angular velocity is detected to exceed a preset threshold, particle filter correction is triggered to generate dynamic prediction parameters containing the motion state covariance matrix.

[0015] The particle filter correction module uses the Monte Carlo sampling method to generate motion state disturbance parameters from historical ship trajectory data.

[0016] Optionally, the method for determining the three-dimensional coverage area includes:

[0017] The acoustic wave domain is calculated based on a hyperboloid geometric model, where the major axis direction is consistent with the tangent direction of the ship's motion trajectory.

[0018] The length of the short shaft is dynamically adjusted according to the ship's length-to-beam ratio parameter and is positively correlated with the preset safety factor;

[0019] The height axis parameter is generated by fusing real-time marine environmental data and through a weighted calculation of wave height and draft.

[0020] Optionally, the method for parsing the dynamic compensation parameters includes:

[0021] Extract the draft compensation bit and heading differential correction bit from the extended fields of the AIS message;

[0022] The length of the compensation bit data is set to be less than a specific proportion of the message check bit length;

[0023] Differential coding technology is used to compress, store, and transmit compensation parameters.

[0024] Optionally, the method for generating the transducer array beamforming parameters includes:

[0025] A multi-layer ring array topology is adopted, and the spacing between adjacent transducer units is dynamically calculated based on the acoustic wavelength and a specific scaling factor.

[0026] The phase difference gradient is distributed along the normal direction of the target ship's trajectory;

[0027] When multiple targets are detected as threats, the multi-beam synchronization control module is activated to generate spatially separated independent sound field focusing areas.

[0028] This application also provides an acoustic repulsion system, comprising:

[0029] The acquisition module acquires millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters.

[0030] The module constructs a ship motion model based on the dynamic compensation parameters and millimeter-wave radar point cloud data.

[0031] The prediction module generates threat area prediction data based on the ship motion model and ship characteristic parameters in the AIS message;

[0032] The action module determines the three-dimensional coverage range of the acoustic wave action based on the threat area prediction data and the ship's length-to-width ratio parameters;

[0033] The parameter module calculates the transducer array beamforming parameters based on the three-dimensional coverage area and the preset safety threshold.

[0034] The command module generates an acoustic wave drive-away waveform command based on the array beamforming parameters and the ship threat level parameters.

[0035] The execution module performs acoustic wave expulsion on the target according to the acoustic wave expulsion waveform command.

[0036] Optionally, the building module constructs a ship motion model, including:

[0037] A hybrid architecture combining a Kalman filter framework and a particle filter correction module is adopted.

[0038] When the ship's turning angular velocity is detected to exceed a preset threshold, particle filter correction is triggered to generate dynamic prediction parameters containing the motion state covariance matrix.

[0039] The particle filter correction module uses the Monte Carlo sampling method to generate motion state disturbance parameters from historical ship trajectory data.

[0040] Optionally, the action module generates threat area prediction data, including:

[0041] The acoustic wave domain is calculated based on a hyperboloid geometric model, where the major axis direction is consistent with the tangent direction of the ship's motion trajectory.

[0042] The length of the short shaft is dynamically adjusted according to the ship's length-to-beam ratio parameter and is positively correlated with the preset safety factor;

[0043] The height axis parameter is generated by fusing real-time marine environmental data and through a weighted calculation of wave height and draft.

[0044] Optionally, the acquisition module parses the dynamic compensation parameters, including:

[0045] Extract the draft compensation bit and heading differential correction bit from the extended fields of the AIS message;

[0046] The length of the compensation bit data is set to be less than a specific proportion of the message check bit length;

[0047] Differential coding technology is used to compress, store, and transmit compensation parameters.

[0048] Optionally, the parameter module forms parameters including:

[0049] A multi-layer ring array topology is adopted, and the spacing between adjacent transducer units is dynamically calculated based on the acoustic wavelength and a specific scaling factor.

[0050] The phase difference gradient is distributed along the normal direction of the target ship's trajectory;

[0051] When multiple targets are detected as threats, the multi-beam synchronization control module is activated to generate spatially separated independent sound field focusing areas.

[0052] The beneficial effects of this application are:

[0053] Invention point:

[0054] 1. By combining high-precision point cloud data from millimeter-wave radar with dynamic compensation parameters provided by AIS, a more accurate ship motion model can be constructed.

[0055] 2. By detecting trajectory error data and generating closed-loop correction parameters based on the error data and sound field control command parameters, real-time feedback and adjustment of the ship's motion state are achieved.

[0056] 3. Ellipsoid Modeling

[0057] This application provides a method for acoustic repulsion, comprising: acquiring millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters; constructing a ship motion model based on the dynamic compensation parameters and millimeter-wave radar point cloud data; generating threat area prediction data based on the ship motion model and ship characteristic parameters in the AIS message; determining the three-dimensional coverage range of the acoustic wave action based on the threat area prediction data and the ship's length-to-width ratio parameter; calculating transducer array beamforming parameters based on the three-dimensional coverage range and a preset safety threshold; generating an acoustic repulsion waveform command based on the array beamforming parameters and the ship threat level parameter; and performing acoustic repulsion on the target according to the acoustic repulsion waveform command. This application significantly improves the accuracy and efficiency of repulsion by comprehensively utilizing millimeter-wave radar, AIS message parsing, and advanced algorithm processing to construct a ship motion model, and accurately determines the three-dimensional coverage range and waveform command of the acoustic wave action, thus ensuring the safety and smooth flow of maritime traffic. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the acoustic wave removal process in this application;

[0059] Figure 2 This is a schematic diagram of the acoustic wave decoy system in this application. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it will be understood that various forms of implementation of the present disclosure are possible and should not be limited to the embodiments set forth herein. Rather, the embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0061] S101. Acquire millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters.

[0062] The system acquires real-time point cloud data (range r, azimuth θ, elevation ϕ in polar coordinates) of the target vessel using millimeter-wave radar.

[0063]

[0064] And parse the dynamic compensation parameters in the AIS message extension field:

[0065] Draft compensation value: Δd = B13−16 × 0.1m (B13−16 is a 4-bit binary value);

[0066] Differential heading correction value: Δψ = B20 × 0.1° / s (B20 is an 8-bit binary value, ranging from −12.8 to +12.7° / s);

[0067] Differential coding compression: The difference between adjacent data is stored as 4 bits, improving the compression rate by 60%.

[0068] The draft compensation bit (4 bits) and heading differential correction bit (8 bits) in the AIS message are compressed and stored using differential coding technology. The data length of the compensation bit is 1 / 4 of the parity bit. For example, when the parity bit is 16 bits, the compensation bit only occupies 4 bits, which are restored to the actual physical quantity value by the protocol parsing module.

[0069] S102. Construct a ship motion model based on the dynamic compensation parameters and millimeter-wave radar point cloud data;

[0070] A hybrid architecture of Kalman filtering and particle filtering is used to construct a six-degree-of-freedom motion model of a ship.

[0071] Kalman filter state equation:

[0072] State vector definition:

[0073]

[0074] Among them, position (x, y, z), velocity (vx, vy, vz), and Euler angle (ψ, θ, ϕ).

[0075] State transition equation:

[0076]

[0077] Where F: state transition matrix (including ship hydrodynamic damping coefficient); B: control input matrix (wind and wave disturbance model). : Process noise, k is the time step.

[0078] When the ship's turning angular velocity exceeds 3° / s or its acceleration exceeds 3m / s², the particle filter correction module is triggered.

[0079] Particle filter correction:

[0080] Triggering conditions: |ψ˙| > 3° / s or |a| > 3m / s²

[0081] Disturbance generation: Position disturbance: Δx∼N(0,0.52)m; Velocity disturbance: Δv∼U(−2,+2)m / s.

[0082] Covariance matrix update: = +Threat level × diag[0.05, 0.05, 0.03]

[0083] Furthermore, 1000 perturbation particles are generated through Monte Carlo sampling to address prediction inaccuracies in sharp turn scenarios. The underlying state covariance matrix of the Kalman filter is dynamically adjusted according to the threat level.

[0084] S103. Based on the ship motion model and the ship characteristic parameters in the AIS message, generate threat area prediction data;

[0085] Predicting the trajectory of a ship within the next 5 seconds based on a ship motion model:

[0086] Trajectory prediction equation:

[0087]

[0088] Where Δt=5s: the prediction time window; The complex form represents the direction of the heading angle. Let the future time state vector be... This is the state vector at the current moment.

[0089] This application uses an integral form to fuse the ship's current speed (v) and acceleration (a) to accurately predict its trajectory within the next 5 seconds, solving the error problem of traditional linear extrapolation methods in scenarios involving speed changes or steering. (Complex form) By directly embedding changes in heading angle into the direction of motion, the ship's turning maneuvers are accurately reflected, improving the geometric fit of trajectory prediction. Acceleration term. The introduction of this method can effectively capture the variable speed motion of ships and avoid prediction biases caused by uniform speed models.

[0090] By combining the vessel's dimensions (length, beam) and type (cargo ship / speedboat) from the AIS message, a probability distribution map of the threat area is generated. Threat level parameters are calculated based on the vessel's speed, course deviation angle, and historical behavior patterns.

[0091] Threat level calculation:

[0092]

[0093] in, Maximum speed corresponding to the type of vessel (25m / s for cargo ships, 40m / s for speedboats). :captain.

[0094] This application normalizes the velocity using the tanh function to suppress sudden changes in threat values ​​from high-speed targets and smooth the response curve. The ratio of the course deviation angle (Δψ) to 30° quantifies the degree of deviation of the ship's course from the safe route, enabling timely identification of abnormal turning behavior.

[0095] Furthermore, the ratio of ship length to 200 meters reflects the potential threat posed by large vessels, enhancing sensitivity to high-risk targets such as cargo ships. Simultaneously, the tanh function maps speed to the [0,1] interval, avoiding excessive sensitivity of linear scaling to extreme speeds and balancing threat assessments across different ship types.

[0096] S104. Based on the threat area prediction data and the ship's length-to-width ratio parameters, determine the three-dimensional coverage range of the acoustic wave effect;

[0097] The three-dimensional coverage area of ​​the sound wave effect is calculated using a hyperboloid geometric model.

[0098] Hyperboloid model equations:

[0099]

[0100] Where α: heading angle; L=1.2 (Long axis); (Minor axis); (Height axis).

[0101] The hyperboloid model can more accurately match the dynamic motion trajectory of a ship. The major axis is aligned with the tangent of the ship's motion, ensuring that the sound wave coverage area closely matches the actual movement path of the ship. At the same time, the minor axis (S) is dynamically adjusted according to the ship's length-to-beam ratio and wave height, while the height axis (H) integrates draft and real-time ocean data to improve coverage accuracy in complex sea conditions.

[0102] When the threat level is ≥2:

[0103]

[0104] The major axis aligns with the tangent to the ship's trajectory, the minor axis length is dynamically adjusted based on the ship's length-to-beam ratio, and the height axis integrates real-time wave height and draft data. When the threat level is ≥2, the coverage area automatically expands by 20%.

[0105] When the threat level is ≥2, the coverage area is automatically expanded (20%) to enhance the ability to drive away high-risk targets.

[0106] S105 calculates the transducer array beamforming parameters based on the three-dimensional coverage area and the preset safety threshold;

[0107] The transducer adopts a three-layer ring array topology, with the inner ring unit spacing being half a wavelength (λ / 2) and the outer ring spacing dynamically adjusted according to the golden ratio (0.618λ).

[0108] Ring array design:

[0109] Calculation of unit spacing:

[0110]

[0111] Phase difference control equation:

[0112]

[0113] Where R: ring radius; N: number of single ring units; θ: target azimuth angle. Let be the phase difference of the nth transducer unit.

[0114] The phase difference gradient is distributed along the normal direction of the target's motion trajectory, supporting multi-beam synchronous control.

[0115] The golden ratio (0.618λ) reduces interference between adjacent units and improves the energy concentration of the main lobe of the beam.

[0116] The phase difference gradient is distributed along the target normal direction, which enhances beam directivity and reduces sidelobe leakage.

[0117] Dynamically adjustable spacing (λ is the wavelength) supports the efficient generation of sound waves of different frequencies.

[0118] Multi-beam synchronization control:

[0119] Number of beams:

[0120]

[0121] Energy allocation rules:

[0122]

[0123] in, and These are the acoustic energy allocated to the i-th target and the total acoustic energy available to the system, respectively.

[0124] It supports up to 12 independent beams (θ3dB=15°), which can simultaneously drive away multiple threatening vessels.

[0125] Allocate total energy according to threat level, optimize resource utilization, and prioritize the suppression of high-risk targets.

[0126] S106. Generate an acoustic wave drive-away waveform command based on the array beamforming parameters and the ship threat level parameters;

[0127] Generate graded acoustic signals based on threat level:

[0128] Level 1 alert: 10kHz continuous wave, sound pressure level 140dB;

[0129] Level 2 drive-off: 20kHz sweep pulse (±5kHz), duty cycle 30%, sound pressure level 150dB;

[0130] Level 3 Powerful: 40kHz shockwave, 10% duty cycle, 160dB sound pressure level.

[0131] Dynamic adjustment of sound pressure level:

[0132]

[0133] SPL stands for sound pressure level.

[0134] The higher the threat level, the greater the sound pressure level, achieving a balance between deterrence and security, avoiding excessive use of high energy, and reducing the impact on non-target areas.

[0135] Frequency modulation rules:

[0136]

[0137] The higher the threat level, the higher the center frequency, making high-frequency shockwaves more effective against high-threat targets. High-frequency sound waves (such as 40kHz) have less impact on humans and marine life, meeting the requirements for safe decoy.

[0138] S107. Drive away the target with sound waves according to the sound wave drive-away waveform command.

[0139] The system emits acoustic signals via a transducer array to monitor the target's response in real time. If the target's heading change rate exceeds 2° / s or the distance continues to decrease, the waveform parameters are dynamically adjusted and an AIS alarm is triggered. The system updates control commands every 200ms, forming a closed-loop control system.

[0140] Closed-loop correction equation

[0141]

[0142] in, is the scaling factor; E is the error energy of the target deviating from the predicted trajectory. and These are the corrected phase difference and the uncorrected phase difference, respectively.

[0143] This application improves beam tracking accuracy by dynamically correcting the phase difference using the error energy (E) gradient. A scaling factor (kp=0.1) balances response speed and stability while suppressing environmental noise interference.

[0144] AIS alarm message generation: Message format (compliant with ITU-R M.1371 standard extension)

[0145] This application also provides an acoustic repulsion system, comprising:

[0146] The acquisition module acquires millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters.

[0147] The module constructs a ship motion model based on the dynamic compensation parameters and millimeter-wave radar point cloud data.

[0148] The prediction module generates threat area prediction data based on the ship motion model and ship characteristic parameters in the AIS message;

[0149] The action module determines the three-dimensional coverage range of the acoustic wave action based on the threat area prediction data and the ship's length-to-width ratio parameters;

[0150] The parameter module calculates the transducer array beamforming parameters based on the three-dimensional coverage area and the preset safety threshold.

[0151] The command module generates an acoustic wave drive-away waveform command based on the array beamforming parameters and the ship threat level parameters.

[0152] The execution module performs acoustic wave expulsion on the target according to the acoustic wave expulsion waveform command.

[0153] Furthermore, the construction module constructs a ship motion model, including:

[0154] A hybrid architecture combining a Kalman filter framework and a particle filter correction module is adopted.

[0155] When the ship's turning angular velocity is detected to exceed a preset threshold, particle filter correction is triggered to generate dynamic prediction parameters containing the motion state covariance matrix.

[0156] The particle filter correction module uses the Monte Carlo sampling method to generate motion state disturbance parameters from historical ship trajectory data.

[0157] Furthermore, the action module generates threat area prediction data, including:

[0158] The acoustic wave domain is calculated based on a hyperboloid geometric model, where the major axis direction is consistent with the tangent direction of the ship's motion trajectory.

[0159] The length of the short shaft is dynamically adjusted according to the ship's length-to-beam ratio parameter and is positively correlated with the preset safety factor;

[0160] The height axis parameter is generated by fusing real-time marine environmental data and through a weighted calculation of wave height and draft.

[0161] Furthermore, the acquisition module parses the dynamic compensation parameters, including:

[0162] Extract the draft compensation bit and heading differential correction bit from the extended fields of the AIS message;

[0163] The length of the compensation bit data is set to be less than a specific proportion of the message check bit length;

[0164] Differential coding technology is used to compress, store, and transmit compensation parameters.

[0165] Furthermore, the parameters formed by the parameter module include:

[0166] A multi-layer ring array topology is adopted, and the spacing between adjacent transducer units is dynamically calculated based on the acoustic wavelength and a specific scaling factor.

[0167] The phase difference gradient is distributed along the normal direction of the target ship's trajectory;

[0168] When multiple targets are detected as threats, the multi-beam synchronization control module is activated to generate spatially separated independent sound field focusing areas.

Claims

1. A method for acoustic repulsion, characterized in that, include: Acquire millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters; Based on the dynamic compensation parameters and millimeter-wave radar point cloud data, a ship motion model is constructed. Based on the ship motion model and ship characteristic parameters in the AIS message, threat area prediction data is generated. Based on the predicted threat area data and the ship's length-to-width ratio parameters, the three-dimensional coverage range of the acoustic wave effect is determined; Based on the three-dimensional coverage area and the preset safety threshold, the transducer array beamforming parameters are calculated. Based on the array beamforming parameters and the ship threat level parameters, generate an acoustic drive-away waveform command; The target is driven away using sound waves according to the sound wave drive-away waveform command.

2. The acoustic wave expulsion method according to claim 1, characterized in that, The construction of the ship motion model includes: A hybrid architecture combining a Kalman filter framework and a particle filter correction module is adopted. When the ship's turning angular velocity is detected to exceed a preset threshold, particle filter correction is triggered to generate dynamic prediction parameters containing the motion state covariance matrix. The particle filter correction module uses the Monte Carlo sampling method to generate motion state disturbance parameters from historical ship trajectory data.

3. The acoustic wave expulsion method according to claim 1, characterized in that, The method for determining the three-dimensional coverage area includes: The acoustic wave domain is calculated based on a hyperboloid geometric model, where the major axis direction is consistent with the tangent direction of the ship's motion trajectory. The length of the short shaft is dynamically adjusted according to the ship's length-to-beam ratio parameter and is positively correlated with the preset safety factor; The height axis parameter is generated by fusing real-time marine environmental data and through a weighted calculation of wave height and draft.

4. The acoustic wave expulsion method according to claim 1, characterized in that, The method for analyzing the dynamic compensation parameters includes: Extract the draft compensation bit and heading differential correction bit from the extended fields of the AIS message; The length of the compensation bit data is set to be less than a specific proportion of the message check bit length; Differential coding technology is used to compress, store, and transmit compensation parameters.

5. The acoustic wave expulsion method according to claim 1, characterized in that, The method for generating the transducer array beamforming parameters includes: A multi-layer ring array topology is adopted, and the spacing between adjacent transducer units is dynamically calculated based on the acoustic wavelength and a specific scaling factor. The phase difference gradient is distributed along the normal direction of the target ship's trajectory; When multiple targets are detected as threats, the multi-beam synchronization control module is activated to generate spatially separated independent sound field focusing areas.

6. A sound wave repelling system, characterized in that, include: The acquisition module acquires millimeter-wave radar point cloud data, dynamic compensation parameters obtained from AIS message parsing, and ship threat level parameters. The module constructs a ship motion model based on the dynamic compensation parameters and millimeter-wave radar point cloud data. The prediction module generates threat area prediction data based on the ship motion model and ship characteristic parameters in the AIS message; The action module determines the three-dimensional coverage range of the acoustic wave action based on the threat area prediction data and the ship's length-to-width ratio parameters; The parameter module calculates the transducer array beamforming parameters based on the three-dimensional coverage area and the preset safety threshold. The command module generates an acoustic wave drive-away waveform command based on the array beamforming parameters and the ship threat level parameters. The execution module performs acoustic wave expulsion on the target according to the acoustic wave expulsion waveform command.

7. The acoustic wave decoy system according to claim 6, characterized in that, The building module constructs a ship motion model, including: A hybrid architecture combining a Kalman filter framework and a particle filter correction module is adopted. When the ship's turning angular velocity is detected to exceed a preset threshold, particle filter correction is triggered to generate dynamic prediction parameters containing the motion state covariance matrix. The particle filter correction module uses the Monte Carlo sampling method to generate motion state disturbance parameters from historical ship trajectory data.

8. The acoustic wave decoy system according to claim 6, characterized in that, The module generates threat area prediction data, including: The acoustic wave domain is calculated based on a hyperboloid geometric model, where the major axis direction is consistent with the tangent direction of the ship's motion trajectory. The length of the short shaft is dynamically adjusted according to the ship's length-to-beam ratio parameter and is positively correlated with the preset safety factor; The height axis parameter is generated by fusing real-time marine environmental data and through a weighted calculation of wave height and draft.

9. The acoustic wave decoy system according to claim 6, characterized in that, The module parses the dynamic compensation parameters, including: Extract the draft compensation bit and heading differential correction bit from the extended fields of the AIS message; The length of the compensation bit data is set to be less than a specific proportion of the message check bit length; Differential coding technology is used to compress, store, and transmit compensation parameters.

10. The acoustic wave decoy system according to claim 6, characterized in that, The parameters formed by the parameter module include: A multi-layer ring array topology is adopted, and the spacing between adjacent transducer units is dynamically calculated based on the acoustic wavelength and a specific scaling factor. The phase difference gradient is distributed along the normal direction of the target ship's trajectory; When multiple targets are detected as threats, the multi-beam synchronization control module is activated to generate spatially separated independent sound field focusing areas.

Images