A robust attitude calibration system and method for self-contained vector sensors in uncertain environments.

Abstract

This invention belongs to the field of underwater acoustic engineering technology, specifically relating to a self-contained vector sensor highly robust attitude calibration system and method for uncertain environments. The azimuth reference self-calibration module is integrated into the magnetic compass circuitry of the vector sensor; this module processes GPS azimuth modulation signals transmitted from cooperative targets during the autonomous calibration phase; the self-contained module performs self-contained data storage. This invention addresses the performance limitations of existing technologies due to reliance on magnetic compasses, thereby reducing magnetic compass deviations or inaccuracies in complex underwater environments.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic engineering technology, specifically relating to a self-contained vector sensor with a highly robust attitude calibration system and method for uncertain environments. Background Technology

[0002] Vector sensors can simultaneously acquire sound pressure and particle velocity in an acoustic field, and their vector channels retain dipole directivity at low frequencies, offering a significant advantage over conventional sound pressure arrays in low-frequency detection by suppressing isotropic noise. Self-contained vector sensors have gained widespread attention due to their small size, low power consumption, and ability to autonomously store and process signals. A self-contained vector sensor system consists of a vector sensor, a self-contained module, a magnetic compass, a pressure chamber, and a suspension frame. The vector sensor is a piezoelectric accelerometer, composed 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 provides the basis for underwater acoustic field detection by characterizing the three-dimensional attitude information of the vector sensor system.

[0003] Self-contained vector sensors offer advantages in orientation and positioning during underwater acoustic field detection. However, their built-in magnetic compasses are susceptible to interference from ferromagnetic environments (such as ship hulls), leading to measurement deviations or irreversible failures. Furthermore, existing alternatives, such as fiber optic gyroscopes, suffer from large size, high power consumption, and the need for periodic calibration, making them unsuitable for long-term underwater operation of miniaturized devices. If the magnetic compass is permanently mounted on the ship hull, the constant interference from the ship's magnetic field may cause directional reference deviations, resulting in inaccurate magnetic compass readings after the sensor enters the water, severely limiting its high-precision positioning and orientation capabilities. Currently, there is no good solution for underwater calibration to address magnetic compass deviations. Due to platform limitations, wired data transmission for underwater acoustic calibration places high demands on the sealing performance of sensor system connectors and makes it impossible to integrate the sensor system with a sound source for calibration. Offline calibration via post-processing of self-contained vector sensor data cannot meet the real-time requirements of dynamic positioning scenarios. Summary of the Invention

[0004] This invention provides a self-contained vector sensor with a highly robust attitude calibration system for uncertain environments, addressing the performance limitations of existing technologies due to their reliance on magnetic compasses.

[0005] This invention provides a highly robust attitude calibration method for a self-contained vector sensor in uncertain environments, which can reduce the occurrence of magnetic compass deviations or inaccuracies in complex underwater environments.

[0006] This invention is achieved through the following technical solution:

[0007] A self-contained vector sensor-based high-robust attitude calibration system for uncertain environments comprises a self-contained vector sensor; the system includes a magnetic compass, a self-contained module, and an orientation reference self-calibration module;

[0008] The orientation reference self-calibration module is integrated into the magnetic compass circuit of the vector sensor;

[0009] The azimuth reference self-calibration module is used to process the GPS azimuth modulation signal transmitted by the cooperative target during the autonomous calibration phase.

[0010] The self-retaining module is used to complete the self-retaining storage of data.

[0011] Furthermore, during the autonomous calibration phase, the orientation reference self-calibration module receives a signal encoded and modulated from the initial feature signal, the sensor's GPS coordinates upon entering the water, and the GPS coordinates of the cooperative target after the sensor system has reached a predetermined depth in the water.

[0012] After receiving the characteristic signal detection synchronization head, the sensor system demodulates and decodes the signal containing the sensor's water-entry GPS and the cooperative target's GPS information, and calculates the true orientation α of the cooperative target.

[0013] The vector sensor performs orientation estimation on the cooperative target to obtain the estimated angle β of the cooperative target;

[0014] The true GPS azimuth angle α of the cooperative target and the estimated angle β of the cooperative target are dynamically estimated using Kalman filtering, and the magnetic compass deviation angle γ is compensated.

[0015] Furthermore, after calibration, the azimuth reference self-calibration module transmits the azimuth result information with dynamic correction to the self-contained module for storage.

[0016] The azimuth reference self-calibration module transmits the dynamically corrected azimuth estimation results to the self-contained module in real time.

[0017] Furthermore, the self-contained module includes a low-noise conditioning section, a high-precision acquisition section, and a data storage section; it is used to store multi-channel data of the vector sensor and magnetic compass angle information within a predetermined time period, and to receive and store the azimuth estimation results of the azimuth reference self-calibration module.

[0018] A calibration method for a self-contained vector sensor with highly robust attitude calibration system in uncertain environments, the calibration method using the self-contained vector sensor with highly robust attitude calibration system in uncertain environments, the calibration method comprising the following steps:

[0019] Step 1: Set conditions;

[0020] Step 2: Establish a Kalman filter model based on the conditions set in Step 1;

[0021] Step 3: Based on the model established in Step 2, perform Kalman filter estimation;

[0022] Step 4: Based on the Kalman filter estimation in Step 3, continuously optimize the calibration process in real time to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0023] Furthermore, step one specifically involves the following steps: for relatively stable sea states, the true angle of the cooperative target remains unchanged during 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 compass deviation angle estimate under this condition.

[0024] Furthermore, step two specifically includes the following steps:

[0025] Step 21: Give the state equation based on prior knowledge;

[0026] Step 22: Measurement equations based on state equations;

[0027] Steps 2 and 3: Perform Kalman filtering on the measurement equation.

[0028] Furthermore, step three specifically involves:

[0029] Initial estimation: A preliminary estimate of the deviation angle is obtained by using the initial GPS angle and the difference between the DOA measured angle;

[0030] Filtering process: Each time new data arrives, the Kalman filter will correct the estimated value of the current deviation angle based on the previous estimate, the current measurement value, and the measurement noise.

[0031] Updates and corrections: As measurement data accumulates, the Kalman filter will continuously adjust the estimation of the deviation angle.

[0032] Furthermore, step four specifically involves continuously optimizing the calibration by comparing the estimated deviation angle with the actual angle, ensuring that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0033] A self-contained vector sensor attitude calibration system with high robustness in uncertain environments, as described above, is applied to complex underwater environments to reduce the impact of noise and external interference on the results.

[0034] The beneficial effects of this invention are:

[0035] The compass deviation angle estimated by this invention has an error of less than 5° compared to the actual deviation angle. Compared with the traditional uncalibrated method, the optimization of Kalman filtering significantly improves the accuracy of the hydrophone in measuring the target orientation, thereby improving the system's direction finding accuracy and environmental adaptability.

[0036] This invention enables efficient and robust calibration of the internal magnetic compass deviation angle of a self-contained vector sensor system in dynamic, unknown magnetic field environments underwater. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the orientation estimation deviation of the self-contained vector sensor of the present invention.

[0038] Figure 2 This is a schematic diagram of the system modularization of the present invention.

[0039] Figure 3 This is a block diagram of the self-capacitive vector sensor of the present invention.

[0040] Figure 4 This is a schematic diagram illustrating the deviation γ between the actual GPS azimuth angle and the estimated azimuth angle of the present invention.

[0041] Figure 5 This is a schematic diagram of the estimated magnetic declination γ at point A in this invention.

[0042] Figure 6 This is a schematic diagram of the estimated magnetic declination γ at point B in this invention. Detailed Implementation

[0043] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0044] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0045] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application 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.

[0046] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0048] Implementation Method 1

[0049] This embodiment provides a self-contained vector sensor system with high robustness in uncertain environments for attitude calibration. A schematic diagram illustrating the orientation estimation deviation of the vector sensing system when processing target signals under unknown magnetic field conditions is provided. Figure 1 Given, such as Figure 3 As shown, its components include a self-contained vector sensor; the system includes a magnetic compass, a self-contained module, and a self-calibrating module for orientation reference;

[0050] The orientation reference self-calibration module is integrated into the magnetic compass circuit of the vector sensor;

[0051] The azimuth reference self-calibration module is used to process the GPS azimuth modulation signal transmitted by the cooperative target during the autonomous calibration stage, so as to reduce the occurrence of magnetic compass deviation or inaccuracy in complex underwater environments.

[0052] The self-contained module is an existing module of the vector sensor, used to complete the self-contained storage of data.

[0053] Furthermore, during the autonomous calibration phase, the orientation reference self-calibration module receives a signal encoded and modulated from the initial feature signal, the sensor's GPS coordinates upon entering the water, and the GPS coordinates of the cooperative target after the sensor system has reached a predetermined depth in the water.

[0054] After receiving the characteristic signal detection synchronization head, the sensor system demodulates and decodes the signal containing the sensor's water-entry GPS and the cooperative target's GPS information, and calculates the true orientation α of the cooperative target.

[0055] At the same time, the vector sensor performs orientation estimation on the cooperative target to obtain the estimated angle β of the cooperative target;

[0056] Then, the actual GPS bearing angle α of the cooperative target is dynamically estimated using Kalman filtering and the estimated angle β of the cooperative target is compensated for with magnetic compass deviation angle γ.

[0057] Furthermore, after calibration, the azimuth reference self-calibration module transmits information such as the azimuth result with dynamic correction to the self-contained module for storage.

[0058] During the experimental phase, the azimuth reference self-calibration module transmits the dynamically corrected azimuth estimation results to the self-contained module in real time.

[0059] Furthermore, the self-contained module includes a low-noise conditioning section, a high-precision acquisition section, and a data storage section; it is used to store multi-channel data of the vector sensor and magnetic compass angle information within a predetermined time period, and to receive and store the azimuth estimation results of the azimuth reference self-calibration module.

[0060] The self-contained sensor system designed in this invention adds a azimuth reference self-calibration module. This module integrates algorithms related to signal preprocessing, vector acoustic signal processing, and autonomous dynamic calibration, as well as demodulation and decoding algorithms, to process the GPS azimuth modulation signal transmitted by cooperative targets during the autonomous calibration stage.

[0061] The estimated compass deviation angle has an error of less than 5° compared with the actual deviation angle. Compared with the traditional uncalibrated method, the optimization of Kalman filtering significantly improves the accuracy of the hydrophone in measuring the target orientation, thereby improving the system's orientation accuracy and environmental adaptability.

[0062] The schematic diagram of the azimuth estimation deviation of the vector sensing system when processing target signals under unknown magnetic field environment in this invention is shown in the figure. Figure 1 Given, where α is the actual GPS angle, β is the estimated azimuth angle, and γ is the magnetic compass deviation angle.

[0063] like Figure 3 As shown, during the autonomous calibration phase, after the sensor system reaches the predetermined depth in the water, the acoustic pressure channel receives a signal encoded and modulated from the initial characteristic signal, the sensor's GPS coordinates upon entering the water, and the GPS coordinates of the cooperative target. The demodulation and decoding unit, after receiving the characteristic signal and detecting the synchronization head, demodulates and decodes the signal containing the sensor's GPS and the cooperative target's GPS information, and calculates the true azimuth α of the cooperative target.

[0064] The preprocessing unit completes the sound pressure channel and vector channel sensitivity correction of the vector sensor to obtain the sound pressure velocity information of the sound field. The positioning calculation unit introduces the magnetic compass angle information to complete the sensor attitude correction and performs azimuth estimation for the cooperative target to obtain the estimated angle β of the cooperative target. Then, the actual GPS azimuth angle and the estimated azimuth angle input deviation calibration unit performs Kalman filtering dynamic estimation and compensates for the magnetic compass deviation angle γ, and transmits the azimuth result with dynamic correction and other information to the self-contained module for storage.

[0065] Using a fixed value as the difference between the estimated azimuth angle and the actual azimuth angle of the target at different locations and times, the test standard was set. In the actual ship navigation test, five relatively dispersed locations were selected, and 50 seconds of data information at the corresponding locations were extracted for processing and analysis.

[0066] The comparison results showed that, after excluding some outliers, the difference γ between the estimated target azimuth angle and the actual azimuth angle at the five processed locations was concentrated at 67°±4°, which basically conformed to the fixed difference, thus verifying the usability of the data.

[0067] Implementation Method 2

[0068] This embodiment provides a highly robust attitude calibration method for a self-contained vector sensor in uncertain environments. The calibration method uses the highly robust attitude calibration system for a self-contained vector sensor in uncertain environments as described in Embodiment 1. The calibration method includes the following steps:

[0069] Step 1: Set conditions;

[0070] Step 2: Establish a Kalman filter model based on the conditions set in Step 1;

[0071] Step 3: Based on the model established in Step 2, perform Kalman filter estimation;

[0072] Step 4: Based on the Kalman filter estimation in Step 3, continuously optimize the calibration process in real time to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0073] The schematic diagram of the azimuth estimation deviation of the vector sensing system when processing target signals under unknown magnetic field environment in this invention is shown in the figure. Figure 1 Given, where α is the actual GPS angle, β is the estimated azimuth angle, and γ is the magnetic compass deviation angle.

[0074] Furthermore, step one specifically involves the following: for relatively stable sea states, the cooperative target remains almost stationary for a short period of time, so its true angle remains unchanged during continuous sampling snapshots; based on this assumption, the angle of the cooperative target is directly set to the state, and the Kalman filter method is used to estimate the deviation of the DOA, thereby obtaining the compass deviation angle estimate under this condition.

[0075] Furthermore, step two specifically includes the following steps:

[0076] Step 21: Give the state equation based on prior knowledge;

[0077] Since the prior knowledge is:

[0078]

[0079] in, It is the result at the prior k+1 time, obtained by multiplying the state estimate at time k-1 by the state transition matrix A:

[0080]

[0081]

[0082] in, For process noise, the state transition matrix ,state Let be the deviation angle at time k, so the deviation angle is the same at every time step in the model; initial state for That is, the initial deviation angle, which is the process noise;

[0083] Step 22: Measurement equations based on state equations;

[0084]

[0085] in, The actual observed value at time k. To observe the transition matrix, The actual DOA measurement angle is given, and R represents the measurement noise, which will be studied further under different operating conditions. The actual angle of the cooperative target as measured by GPS, or written in standard form as measurement. ;

[0086]

[0087] Right now ;

[0088] Steps 2 and 3: Perform Kalman filtering on the measurement equation.

[0089]

[0090] or

[0091]

[0092] in, The result at time k is obtained from the state estimation at time k-1. This represents the prior result at time k. K For Kalman gain.

[0093] Furthermore, step three specifically involves:

[0094] Initial estimation: A preliminary estimate of the deviation angle is obtained by using the initial GPS angle and the difference between the DOA measured angle;

[0095] Filtering process: Each time new data arrives, the Kalman filter will correct the estimated value of the current deviation angle based on the previous estimate, the current measurement value, and the measurement noise.

[0096] Updates and corrections: As measurement data accumulates, the Kalman filter continuously adjusts the estimation of the deviation angle, and over time, enhances the robustness of the system and reduces the impact of noise and external interference on the results.

[0097] Furthermore, step four specifically involves continuously optimizing the calibration process by comparing the estimated deviation angle with the actual angle (GPS angle) in the actual application of the hydrophone.

[0098] Through real-time calibration, it can adapt to changes in factors such as temperature, salinity, and noise in the underwater environment, ensuring that the hydrophone can provide high-precision DOA estimation under various complex conditions.

[0099] The magnetic compass deviation angle γ estimated by the algorithm of this invention after processing the experimental data, and the result analysis are as follows: Figure 5 and Figure 6 As shown.

[0100] The final result shows that the actual magnetic declination angle is almost identical to the estimated value in the spatial domain, meaning that the deviation angle γ can be used to calibrate the current azimuth estimation result.

Claims

1. A calibration method for a self-contained vector sensor high-robust attitude calibration system in uncertain environments, the calibration system comprising a magnetic compass, a self-contained module, an azimuth reference self-calibration module, and a self-contained vector sensor; The orientation reference self-calibration module is integrated into the magnetic compass circuit of the vector sensor; The azimuth reference self-calibration module is used to process the GPS azimuth modulation signal transmitted by the cooperative target during the autonomous calibration phase. The self-compatible module is used to complete the self-compatible storage of data; Its features are, The calibration method includes the following steps: Step 1: Set conditions. Specifically, for relatively stable sea states, the true angle of the cooperative target remains unchanged in continuous sampling snapshots. The state in the corresponding state equation of the Kalman filter model is directly set as the angle of the cooperative target. The Kalman filter method is used to estimate the deviation of the DOA, thereby obtaining the compass deviation angle estimate under the corresponding 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, continuously optimize the calibration process in real time to ensure that the hydrophone can provide high-precision DOA estimation under various complex conditions; During the autonomous calibration phase, the self-calibration module of the orientation reference receives, through the acoustic pressure channel, a signal encoded and modulated from 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 has reached a predetermined depth in the water. After receiving the initial feature signal and detecting the synchronization head, the sensor system demodulates and decodes the signal containing the sensor's GPS information upon entering the water and the GPS information of the cooperative target, and calculates the true orientation α of the cooperative target. The vector sensor incorporates magnetic compass angle information to perform sensor attitude correction and performs orientation estimation of the cooperative target to obtain the estimated angle β of the cooperative target; The true GPS azimuth angle α of the cooperative target and the estimated angle β of the cooperative target are dynamically estimated using Kalman filtering, and the magnetic compass deviation angle γ is compensated. The self-contained module includes a low-noise conditioning section, a high-precision acquisition section, and a data storage section; it is used to store multi-channel data from vector sensors and magnetic compass angle information within a predetermined time period, and to receive and store the azimuth estimation results from the azimuth reference self-calibration module.

2. The calibration method according to claim 1, characterized in that, After calibration, the azimuth reference self-calibration module transmits the azimuth result information with dynamic correction to the self-contained module for storage; The azimuth reference self-calibration module transmits the dynamically corrected azimuth estimation results to the self-contained module in real time.

3. The calibration method according to claim 1, characterized in that, The third step is specifically as follows: Initial estimation: A preliminary estimate of the deviation angle is obtained by using the initial GPS angle and the difference between the DOA measured angle; Filtering process: Each time new data arrives, the Kalman filter will correct 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 will continuously adjust the estimation of the deviation angle.

4. The calibration method according to claim 1, characterized in that, Step four specifically involves continuously optimizing the calibration by comparing the estimated deviation angle with the actual angle, ensuring that the hydrophone can provide high-precision DOA estimation under various complex conditions.

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