High-robustness attitude calibration system and method for self-contained vector sensor in uncertain environment

Abstract

The invention belongs to the technical field of underwater acoustic engineering, and particularly relates to a high-robustness attitude calibration system and method for a self-contained vector sensor in an uncertain environment. The azimuth reference self-correction module is integrated in a circuit of a magnetic compass in the vector sensor; the azimuth reference self-correction module is used for processing a GPS azimuth modulation signal transmitted by a cooperative target in an autonomous calibration stage; and the self-capacitance module is used for completing data self-capacitance storage. The invention aims to solve the problem of performance limitation caused by dependence on a magnetic compass in the prior art, and is used for reducing the situation of deviation or inaccurate correction of the magnetic compass in an underwater complex environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater acoustic engineering, and in particular relates to a highly robust attitude calibration system and method for a self-contained vector sensor in an uncertain environment. Background Art

[0002] Vector sensors can not only simultaneously detect sound pressure and particle velocity in the acoustic field, but also maintain dipole directivity at low frequencies, suppressing isotropic noise. This makes them a significant advantage over conventional acoustic pressure arrays in low-frequency detection. Self-contained vector sensors have garnered widespread attention for their compact size, low energy consumption, and ability to independently perform data storage and signal processing. A self-contained vector sensor system consists of a vector sensor, a self-contained module, a magnetic compass, a pressure-resistant cabin, and a suspension frame. The vector sensor is a piezoelectric accelerometer, comprised of a highly sensitive three-dimensional accelerometer and a hydrophone. The self-contained module comprises a low-noise conditioning section, a high-precision acquisition section, and a data storage section. The magnetic compass characterizes the three-dimensional attitude information of the vector sensor system, providing a basis for the self-contained vector sensor's underwater acoustic field detection.

[0003] Self-contained vector sensors offer advantages in underwater acoustic field detection and orientation. However, their built-in magnetic compasses are susceptible to interference from ferromagnetic environments (such as ship hulls), leading to measurement deviations or irreversible failure. Existing alternatives, such as fiber optic gyroscopes, are bulky, power-hungry, and require regular calibration, making them difficult to meet the long-term underwater operation requirements of miniaturized equipment. If the magnetic compass is permanently mounted on the ship's hull, the constant interference from the ship's magnetic field may cause a directional reference deviation, leading to compass inaccuracy after the sensor enters the water, severely limiting its high-precision positioning and orientation capabilities. Currently, there is no effective solution for underwater calibration of magnetic compass deviations. Due to platform limitations, underwater acoustic calibration requires high sealing performance for sensor system connectors using wired data transmission, and calibration cannot be achieved using an integrated sound source within the sensor system. Post-processing and offline calibration of self-contained vector sensor system data cannot meet the real-time requirements of dynamic positioning scenarios. Summary of the Invention

[0004] The present invention provides a highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment, which addresses the performance limitation problem of the prior art caused by reliance on a magnetic compass.

[0005] The present invention provides a highly robust attitude calibration method for a self-contained vector sensor in an uncertain environment, which is used to reduce deviation or miscalibration of a magnetic compass in a complex underwater environment.

[0006] The present invention is achieved through the following technical solutions: A highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment, comprising a self-contained vector sensor; the system includes a magnetic compass, a self-contained module, and an azimuth reference self-correction module; The azimuth reference self-correction module is integrated into the circuit of the magnetic compass in the vector sensor; The azimuth reference self-correction module is used to process the GPS azimuth modulation signal transmitted by the cooperative target during the autonomous calibration phase; The self-contained module is used to complete self-contained data storage.

[0007] Furthermore, the azimuth reference self-calibration module receives a signal obtained by encoding and modulating the initial characteristic signal, the GPS coordinates of the sensor entering the water, and the GPS coordinates of the cooperative target after the sensor system enters the water to a predetermined depth during the autonomous calibration phase; After receiving the characteristic signal and detecting the synchronization header, the sensor system demodulates and decodes the signal containing the sensor's submerged GPS and the cooperative target's GPS information to calculate the cooperative target's true position α. The vector sensor estimates the direction of the cooperative target and obtains the estimated angle β of the cooperative target; The GPS true azimuth angle α of the cooperative target and the estimated angle β of the cooperative target are dynamically estimated by Kalman filtering and the magnetic compass deviation angle γ is compensated.

[0008] Furthermore, after the calibration is completed, the azimuth reference self-correction module transmits the information of the azimuth result with the dynamic correction amount to the self-contained module for storage; The azimuth reference self-correction module transmits the dynamically corrected azimuth estimation result to the self-contained module in real time.

[0009] Furthermore, the self-contained module includes a low-noise conditioning part, a high-precision acquisition part and a data storage part; it is used to store multi-channel data of the vector sensor and magnetic compass angle information within a predetermined time, and receive and store the azimuth estimation result of the azimuth reference self-correction module.

[0010] A calibration method for a highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment is disclosed. The calibration method uses the highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment. The calibration method comprises the following steps: Step 1: Set conditions; Step 2: Establish a Kalman filter model based on the conditions set in step 1; Step 3: Based on the model established in step 2, perform Kalman filter estimation; Step 4: Based on the Kalman filter estimation in step 3, the calibration process is continuously optimized in real time to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0011] Furthermore, the step one is specifically as follows: for a relatively stable sea condition, the true angle of the cooperative target remains unchanged in continuous sampling snapshots; the state is directly set to the angle of the cooperative target, and the Kalman filter method is used to estimate the deviation of the DOA, thereby obtaining the estimated value of the compass deviation angle in this case.

[0012] Furthermore, the step 2 specifically includes the following steps: Step 21: Give the state equation based on prior knowledge; Step 22: Measurement equation based on the equation of state; Step 2 and 3: Perform Kalman filtering on the measurement equation.

[0013] Furthermore, the step three is specifically as follows: Initial estimate: Get a preliminary deviation angle estimate based on the initial GPS angle and the DOA measurement angle difference; Filtering process: Each time new data arrives, the Kalman filter corrects the estimated value of the current deviation angle based on the previous estimate, the current measurement value, and the measurement noise; Update and Correction: As measurement data accumulates, the Kalman filter continuously adjusts the estimate of the deviation angle.

[0014] Furthermore, the step 4 specifically comprises continuously optimizing the calibration by comparing the deviation between the estimated deviation angle and the true angle, thereby ensuring that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0015] A highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment is applied to a complex underwater environment, reducing the influence of noise and external interference on the results.

[0016] The beneficial effects of the present invention are: The error between the compass deviation angle estimated by the present invention and the actual deviation angle is within 5°. Compared with the traditional uncalibrated method, the measurement accuracy of the hydrophone for the target direction is significantly improved through the optimization of Kalman filtering, thereby improving the direction-finding accuracy and environmental adaptability of the system.

[0017] The present invention can efficiently and robustly calibrate the internal magnetic compass deviation angle of a self-contained vector sensor system in an underwater dynamic and unknown magnetic field environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the azimuth estimation deviation of the self-capacitive vector sensor of the present invention.

[0019] Figure 2 It is a schematic diagram of system modularization of the present invention.

[0020] Figure 3This is a block diagram of the self-capacitive vector sensor of the present invention.

[0021] Figure 4 It is a schematic diagram of the deviation value γ between the actual GPS azimuth angle and the estimated azimuth angle of the present invention.

[0022] Figure 5 It is a schematic diagram of the estimated value of the magnetic declination angle γ at point A of the present invention.

[0023] Figure 6 It is a schematic diagram of the estimated value of the magnetic declination angle γ at point B of the present invention. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0025] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0026] It should also be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] The following is a clear and complete description of the technical solutions in the embodiments of this application in conjunction with the drawings in the specification of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0029] Implementation Method 1 This embodiment provides a highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment. The schematic diagram of the azimuth estimation deviation when the vector sensing system processes the target signal in an unknown magnetic field environment is shown in FIG. Figure 1 Given, such as Figure 3 As shown, its composition includes a self-capacitive vector sensor; the system includes a magnetic compass, a self-capacitive module and an azimuth reference self-correction module; The azimuth reference self-correction module is integrated into the circuit of the magnetic compass in the vector sensor; The azimuth reference self-correction module is used to process the GPS azimuth modulation signal transmitted by the cooperative target during the autonomous calibration phase to reduce the deviation or miscalibration of the magnetic compass in complex underwater environments; The self-contained module is an existing module of the vector sensor, and is used to complete the self-contained storage of data.

[0030] Furthermore, the azimuth reference self-calibration module receives a signal obtained by encoding and modulating the initial characteristic signal, the GPS coordinates of the sensor entering the water, and the GPS coordinates of the cooperative target after the sensor system enters the water to a predetermined depth during the autonomous calibration phase; After receiving the characteristic signal and detecting the synchronization header, the sensor system demodulates and decodes the signal containing the sensor's submerged GPS and the cooperative target's GPS information to calculate the cooperative target's true position α. At the same time, the vector sensor estimates the direction of the cooperative target and obtains the estimated angle β of the cooperative target; Then, the actual GPS position α of the cooperative target and the estimated angle β of the cooperative target are dynamically estimated by Kalman filtering and the magnetic compass deviation angle γ is compensated.

[0031] Furthermore, after the calibration is completed, the azimuth reference self-correction module transmits the azimuth result and other information with the dynamic correction amount to the self-contained module for storage; During the experimental phase, the azimuth reference self-correction module transmits the dynamically corrected azimuth estimation results to the self-contained module in real time.

[0032] Furthermore, the self-contained module includes a low-noise conditioning part, a high-precision acquisition part and a data storage part; it is used to store multi-channel data of the vector sensor and magnetic compass angle information within a predetermined time, and receive and store the azimuth estimation result of the azimuth reference self-correction module.

[0033] The self-contained sensor system designed in this invention adds an azimuth reference self-calibration module. This module integrates algorithms for signal preprocessing, vector acoustic signal processing, and autonomous dynamic calibration, along with demodulation and decoding algorithms. During the autonomous calibration phase, it processes the GPS azimuth modulated signal transmitted by the cooperating target.

[0034] The error between the estimated compass deviation angle and the actual deviation angle is within 5°. Compared with the traditional uncalibrated method, the optimization of Kalman filtering significantly improves the measurement accuracy of the hydrophone for the target direction, thereby improving the direction-finding accuracy and environmental adaptability of the system.

[0035] The schematic diagram of the azimuth estimation deviation when the vector sensing system processes the target signal in an unknown magnetic field environment in the present invention is as follows: Figure 1 Given as , where α is the actual GPS angle, β is the estimated azimuth angle, and γ is the magnetic compass deviation angle.

[0036] like Figure 3 As shown, during the autonomous calibration phase, after the sensor system reaches a predetermined depth, the acoustic pressure channel receives a signal encoded and modulated by the initial characteristic signal, the sensor's GPS coordinates at entry, and the GPS coordinates of the cooperative target. After receiving the characteristic signal and detecting the synchronization header, the demodulation and decoding unit demodulates and decodes the signal containing the sensor's GPS coordinates at entry and the cooperative target's GPS coordinates, and calculates the cooperative target's true position α.

[0037] The preprocessing unit corrects the vector sensor's sound pressure channel and sensitivity to obtain sound pressure and velocity information. The positioning calculation unit incorporates magnetic compass angle information to correct the sensor's attitude and estimates the direction of the cooperative target, obtaining an estimated angle β. The actual GPS direction angle and the estimated direction angle are then input into the deviation calibration unit for dynamic Kalman filtering and compensation of the magnetic compass deviation angle γ. The resulting direction information, along with the dynamic correction, is then transferred to the self-contained module for storage.

[0038] The difference between the estimated azimuth angle and the actual azimuth angle of the target at different positions and times is set to a fixed value as the test standard. In the actual ship sailing test, five relatively scattered positions are selected and 50 seconds of data information at the corresponding positions are extracted for processing and analysis. The comparison results show that after excluding some outliers, the difference γ between the estimated target azimuth angle and the actual azimuth angle at the five processed positions is concentrated at 67°±4°, which basically conforms to the fixed difference and verifies the availability of the data.

[0039] Implementation Method 2 This embodiment provides a highly robust attitude calibration method for a self-contained vector sensor in an uncertain environment. The calibration method uses the highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment as described in Embodiment 1. The calibration method includes the following steps: Step 1: Set conditions; Step 2: Establish a Kalman filter model based on the conditions set in step 1; Step 3: Based on the model established in step 2, perform Kalman filter estimation; Step 4: Based on the Kalman filter estimation in step 3, the calibration process is continuously optimized in real time to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0040] The schematic diagram of the azimuth estimation deviation when the vector sensing system processes the target signal in an unknown magnetic field environment in the present invention is as follows: Figure 1 Given, where α is the actual GPS angle, β is the estimated azimuth angle, and γ is the magnetic compass deviation angle Furthermore, the step 1 is specifically as follows: for relatively stable sea conditions, the cooperative target is almost motionless in a short period of time, so its true angle remains unchanged in continuous sampling snapshots; based on this assumption, the state is directly set to the angle of the cooperative target, and the Kalman filter method is used to estimate the DOA deviation, thereby obtaining the compass deviation angle estimate in this case.

[0041] Furthermore, the step 2 specifically includes the following steps: Step 21: Give the state equation based on prior knowledge; Since the prior knowledge is:

[0042] in, It is the result of the prior k+1 time, which is obtained by multiplying the state estimate at k-1 time by the state transfer matrix A:

[0043]

[0044] in, is the process noise, the state transition matrix ,state is the deviation angle at the kth moment, so the deviation angle at each moment on the model is the same; the initial state for That is, the initial deviation angle is the process noise; Step 22: Measurement equation based on the equation of state;

[0045] in, is the actual observation value at time k, is the observation transfer matrix, is the actual DOA measurement angle, R is the measurement noise, and subsequent research will be conducted under different working conditions. The angle measured by GPS of the actual target, or written in standard form as a measurement ;

[0046] Right now ; Step 2 and 3: Perform Kalman filtering on the measurement equation.

[0047]

[0048] or

[0049] in, is the result at time k obtained by estimating the state at time k-1, is the result of the prior k moment, K is the Kalman gain.

[0050] Furthermore, the step three is specifically as follows: Initial estimate: Get a preliminary deviation angle estimate based on the initial GPS angle and the DOA measurement angle difference; Filtering process: Each time new data arrives, the Kalman filter corrects the estimated value of the current deviation angle based on the previous estimate, the current measurement value, and the measurement noise; Updates and corrections: As measurement data accumulates, the Kalman filter continuously adjusts the estimate of the deviation angle, and over time, it increases the robustness of the system and reduces the impact of noise and external interference on the results.

[0051] Furthermore, the step 4 specifically includes, in actual application of the hydrophone, continuously optimizing the calibration process by comparing the deviation between the estimated deviation angle and the true angle (GPS angle); Through real-time calibration, it can adapt to changes in temperature, salinity, noise and other factors in the underwater environment, ensuring that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0052] The magnetic compass deviation angle γ estimated by the algorithm of the present invention after processing the test data and the result analysis are as follows: Figure 5 and Figure 6 shown.

[0053] The final result is that the actual magnetic declination angle is almost consistent with the estimated value in the airspace, that is, the deviation angle γ can be used to calibrate the current azimuth of arrival estimation result.

Claims

1. A highly robust attitude calibration system for self-contained vector sensors in uncertain environments, comprising: Self-capacitance vector sensor; characterized in that the system includes a magnetic compass, a self-capacitance module and an azimuth reference self-correction module; The azimuth reference self-correction module is integrated into the circuit of the magnetic compass in the vector sensor; The azimuth reference self-correction module is used to process the GPS azimuth modulation signal transmitted by the cooperative target during the autonomous calibration phase; The self-contained module is used to complete the self-contained storage of data; The azimuth reference self-calibration module receives a signal that is coded and modulated by the initial characteristic signal, the GPS coordinates of the sensor entering the water, and the GPS coordinates of the cooperative target after the sensor system enters the water to a predetermined depth during the autonomous calibration phase. After receiving the characteristic signal and detecting the synchronization header, the sensor system demodulates and decodes the signal containing the sensor's submerged GPS and the cooperative target's GPS information to calculate the cooperative target's true position α. The vector sensor estimates the direction of the cooperative target and obtains the estimated angle β of the cooperative target; The GPS true azimuth angle α of the cooperative target and the estimated angle β of the cooperative target are dynamically estimated by Kalman filtering and the magnetic compass deviation angle γ is compensated.

2. The calibration system according to claim 1, characterized in that: After the calibration is completed, the azimuth reference self-correction module transfers the azimuth result information with the dynamic correction amount to the self-capacity module for storage; The azimuth reference self-correction module transmits the dynamically corrected azimuth estimation result to the self-contained module in real time.

3. The calibration system according to claim 1, characterized in that: The self-contained module includes a low-noise conditioning part, a high-precision acquisition part and a data storage part; it is used to store multi-channel data of the vector sensor and magnetic compass angle information within a predetermined time, and receive and store the azimuth estimation result of the azimuth reference self-correction module.

4. A calibration method for a highly robust attitude calibration system of a self-contained vector sensor in an uncertain environment, characterized in that: The calibration method uses the highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment as described in any one of claims 1 to 3, and the calibration method comprises the following steps: Step 1: Set conditions; Step 2: Establish a Kalman filter model based on the conditions set in step 1; Step 3: Based on the model established in step 2, perform Kalman filter estimation; Step 4: Based on the Kalman filter estimation in step 3, the calibration process is continuously optimized in real time to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions.

5. The calibration method according to claim 4, characterized in that: Specifically, step one includes: for a relatively stable sea condition, the true angle of the cooperative target remains unchanged in continuous sampling snapshots; the state is directly set to the angle of the cooperative target, and the DOA is estimated using the Kalman filter method to obtain the estimated compass deviation angle in this case.

6. The calibration method according to claim 4, characterized in that: The step 2 specifically includes the following steps: Step 21: Give the state equation based on prior knowledge; Step 22: Measurement equation based on the equation of state; Step 2 and 3: Perform Kalman filtering on the measurement equation.

7. The calibration method according to claim 5, characterized in that: The step three is specifically as follows: Initial estimate: Get a preliminary deviation angle estimate based on the initial GPS angle and the DOA measurement angle difference; Filtering process: Each time new data arrives, the Kalman filter corrects the estimated value of the current deviation angle based on the previous estimate, the current measurement value, and the measurement noise; Update and Correction: As measurement data accumulates, the Kalman filter continuously adjusts the estimate of the deviation angle.

8. The calibration method according to claim 5, characterized in that: Specifically, step 4 is to continuously optimize the calibration by comparing the deviation between the estimated deviation angle and the true angle, so as to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions.

9. An application of a highly robust attitude calibration system for a self-contained vector sensor in an uncertain environment as claimed in any one of claims 1 to 3, characterized in that: Applied to complex underwater environments to reduce the impact of noise and external interference on the results.

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