A Method for Aligning Attitude Sensor and Hydrophone Data Based on OTSM

Abstract

The present application discloses a method for aligning attitude sensor and hydrophone data based on OTSM, which belongs to the field of ocean monitoring technology. The present application is a method for aligning attitude sensor and hydrophone data based on OTSM synchronization technology. Through the synchronization algorithm of the time axis and the delay axis, pilot symbols are inserted into the hydrophone signal. The delay on different propagation paths is determined by finding the peak of the pilot signal to synchronize the data. The signal containing the pilot symbol is combined with the Walsh-Hadamard transform frequency compensation to achieve accurate frequency alignment of multi-sensor data in complex environments, ensure the frequency alignment of attitude data and hydrophone data, and achieve higher synchronization accuracy.

Description

Technical Field

[0001] The present application belongs to the field of ocean monitoring technology, and in particular relates to a method for aligning attitude sensor and hydrophone data based on OTSM. Background Art

[0002] In the current field of ocean observation and underwater sensing, the synchronous alignment technology of digital hydrophones and attitude sensors is mainly divided into the following methods:

[0003] 1. Independent acquisition and post-alignment methods

[0004] In this method, the hydrophone and attitude sensor independently collect data, each with its own timestamp. The data is then aligned using an algorithm. This approach relies on independent clock sources, which can lead to time drift during the acquisition process. A common practice is to re-align the two sets of data based on their timestamps during post-processing, aligning the two sets of data at different frequencies through interpolation or other alignment algorithms. Because the hydrophone and attitude sensor collect data separately, slight drift in the independent clock sources can cause data time deviations. Especially during long acquisition periods, the time errors caused by drift are difficult to eliminate. Furthermore, the data sampling frequencies of different sensors often differ, making alignment algorithms complex and computationally expensive, making them less effective in real-time applications.

[0005] 2. Central clock synchronization method

[0006] This method uses a unified clock source (such as a GPS signal or a high-precision internal clock) to synchronize the timestamps of the hydrophones and attitude sensors. Specifically, a unified time signal from a central clock is transmitted to each sensor via wired or wireless means. Each sensor then timestamps its data based on this clock. However, this method has limited applicability in marine or underwater environments. GPS signal transmission is hindered underwater, and even in shallow waters, fluctuations in the GPS signal can affect clock stability. Furthermore, using a central clock synchronization method typically requires high-precision clock equipment, which increases equipment cost, complicates system integration, and makes equipment maintenance difficult, making it unsuitable for long-term deployment.

[0007] 3. Hardware-triggered synchronization method

[0008] This method synchronizes the acquisition processes of the hydrophone and attitude sensor using hardware trigger signals. For example, a trigger signal is sent to both sensors at the same time, controlling them to start collecting data simultaneously. Hardware triggering can transmit signals through a physical connection, ensuring that the timestamps of the two sensors remain consistent. The design of hardware trigger synchronization requires additional interface devices, increasing the complexity of the system hardware. Furthermore, there is a slight delay in the physical transmission of the trigger signal, especially in long-distance or underwater environments, where the signal transmission path may be limited, resulting in reduced data alignment accuracy. Furthermore, this method is difficult to handle in dynamic application scenarios with high real-time requirements.

[0009] The three existing technical solutions mentioned above each have their own drawbacks for synchronizing data between digital hydrophones and attitude sensors: independent data acquisition and post-processing alignment methods lack time accuracy; central clock synchronization methods are susceptible to limitations in underwater environments and have high equipment costs; and hardware-triggered synchronization solutions require complex hardware interface design, and signal transmission delays increase data errors. These technical methods struggle to achieve precise and efficient alignment of hydrophones and attitude sensors in dynamic underwater environments, and they fail to address the technical issue of data timestamp asynchrony between hydrophones and attitude sensors in dynamic underwater environments. Summary of the Invention

[0010] In order to solve the technical problem of asynchrony of attitude sensor and hydrophone data timestamps in the prior art in ocean monitoring, this application proposes an attitude sensor and hydrophone data alignment method based on OTSM.

[0011] In a first aspect of the present application, the present application discloses a method for aligning attitude sensor and hydrophone data based on OTSM, which is characterized by comprising:

[0012] Step S1, independently collecting attitude sensor data and hydrophone data to obtain attitude sensor data sequence A and hydrophone data sequence B;

[0013] Step S2: Create a time series C. The time interval of time series C is smaller than the time interval of sequence A and sequence B, so that the timestamps of sequence A and sequence B can find the closest matching point in C.

[0014] Step S3, inserting pilot symbols into the hydrophone signal, placing the pilot symbols at specific positions in each frame;

[0015] Step S4: Using a sliding window search method to identify the peak position of the pilot symbol in the Delay-Time domain, thereby determining the delay of the signal on different paths and performing time synchronization on the data;

[0016] Step S5: After completing the time synchronization, it is necessary to compensate for the frequency offset in the signal to achieve frequency consistency between the attitude sensor and the hydrophone data;

[0017] Step S6: Find the closest T in the attitude sensor data sequence A and the hydrophone data sequence B. C The corresponding data points in sequence A and sequence B are inserted into the corresponding positions in time series C to update time series C;

[0018] Step S7: Check each time point of the time series C to find the synchronization point containing both the attitude sensor and the hydrophone data, extract the data entries that meet the conditions, and generate a new aligned data sequence D;

[0019] Step S8: Output the aligned data sequence for subsequent analysis and processing.

[0020] Furthermore, the attitude sensor data in step S1 includes attitude angle, time and acceleration, and the hydrophone data includes sound wave signal, frequency, sound intensity, waveform and time.

[0021] Furthermore, in step S2, the time interval of time series C is shorter than the time intervals of sequence A and sequence B, which includes: the sampling frequency of the attitude sensor is 10 Hz, the sampling frequency of the hydrophone is 5 Hz, and the time interval of time series C is 1 Hz.

[0022] Furthermore, step S2 includes: defining a time series C(T n , tag1, attitude, tag2,hydrophone), select a time point T from the time series C C As the reference time for the current processing; where T n is time, attitude is attitude sensor data, hydrophone is hydrophone data, tag1 and tag2 represent attitude sensor data and hydrophone data tags respectively, and the initial values of tag1 and tag2 are 0.

[0023] Furthermore, step S4 further includes: performing weighted averaging processing on the delays on different paths to calculate the final synchronization delay, and the calculation formula is: ,in, is the delay of the i-th path , N is the number of different signal propagation paths, is the weight of different paths; the received hydrophone signal is delayed and compensated according to the weighted average delay, so that the attitude sensor and hydrophone data are synchronized in time.

[0024] Furthermore, step S5 includes: after time synchronization, converting the signal containing the pilot symbol into the delay-sequence domain through Walsh-Hadamard transform, and performing frequency synchronization in the delay-sequence domain using the phase characteristics of the pilot signal.

[0025] Furthermore, performing frequency synchronization using the phase characteristic of the pilot signal in step S5 includes:

[0026] The carrier frequency offset (CFO) is calculated using the pilot symbol as the frequency reference, and the estimated CFO value is used to compensate for the frequency drift in the signal to ensure that the frequency of the attitude data and the hydrophone data are aligned.

[0027] Furthermore, step S6 specifically includes:

[0028] Step S61, search for the closest T in sequence A C If a data point T1 matching the timestamp is found, the data is inserted into the corresponding position in the time series C and tag1=1 is set to mark the data point at T C There is attitude sensor data at the time point; if no data point that meets the conditions is found, tag1=0;

[0029] Step S62, search for the closest T in sequence B C If a data point T2 matching the timestamp is found, the data is inserted into the corresponding position in the time series C and tag2=1 is set to mark the data point at T C There is hydrophone data at the time point; if no data point that meets the conditions is found, tag2=0.

[0030] In a second aspect of the present application, the present application provides a computer device comprising a memory, a processor, and program instructions stored in the memory for execution by the processor, wherein the processor executes the program instructions to implement the steps of any one of the methods described in the first aspect.

[0031] In a third aspect of the present application, the present application provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, it implements any one of the methods in the first aspect.

[0032] The present application discloses a method for aligning attitude sensor and hydrophone data based on OTSM, which belongs to the field of ocean monitoring technology. The present application is a method for aligning attitude sensor and hydrophone data based on OTSM synchronization technology. Through the synchronization algorithm of the time axis and the delay axis, pilot symbols are inserted into the hydrophone signal, and the delay on different propagation paths is determined by finding the peak of the pilot signal to synchronize the data. The signal containing the pilot symbol is combined with the Walsh-Hadamard transform frequency compensation to achieve accurate frequency alignment of multi-sensor data in complex environments, ensure the frequency alignment of attitude data and hydrophone data, and achieve higher synchronization accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of a method for aligning attitude sensor and hydrophone data based on OTSM in this application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to further clearly and completely describe the technical solutions in the embodiments of this application. It should be noted that 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 those skilled in the art without making any creative work shall fall within the scope of protection of this application.

[0035] In order to make the purpose, technical solutions and advantages of this application more clear, the technical solutions of the present invention are clearly and completely described below with reference to the accompanying drawings and specific embodiments. It should be noted that those skilled in the art can make various changes and improvements without departing from the concept of the present invention, and these are all within the scope of protection of this application.

[0036] The embodiments of the present application are further described in detail below with reference to the accompanying drawings:

[0037] like Figure 1 As shown, this embodiment provides a method for aligning attitude sensor and hydrophone data based on OTSM, including:

[0038] Step 1: Generate time series C

[0039] Create a time series C with a time interval smaller than that of series A and B, so that the timestamps of A and B can find the closest matching point in C.

[0040] Define C(T n, tag1, attitude, tag2, hydrophone) protocol format, initialize all tag1 and tag2 values to 0 to mark whether there is corresponding data. Select a time point TC from the time series C as the reference time for the current processing.

[0041] In one embodiment, the attitude sensor sampling frequency is set to 10 Hz to collect attitude angle and acceleration data under dynamic conditions to ensure the continuity of data changes; the hydrophone sampling frequency is set to 5 Hz to capture changes in acoustic signals in the underwater environment; the time interval of time series C is set to 1 Hz, making its frequency higher than the attitude and hydrophone data sequences, ensuring that the closest attitude and acoustic data can be found for alignment.

[0042] Step 2: Insertion and propagation of pilot symbols

[0043] Pilot symbols are inserted into the hydrophone signal. Pilot symbols are known and typically have good correlation characteristics, facilitating signal detection and delay estimation at the receiver. Pilot symbols are placed at specific locations within each frame, enabling detection at the receiver using a sliding window search. Pilot symbols reach the receiver via different propagation paths, each of which incurs a different delay due to multipath effects.

[0044] Step 3: Multipath identification

[0045] The sliding window method is used to identify the peak position of the pilot symbol in the DT (Delay-Time) domain to determine the delay on different paths. All paths are weighted averaged to calculate the final synchronization delay and perform delay compensation on the received hydrophone signal. , according to its signal strength , assign weight The delay after weighted average

[0046]

[0047] Step 4: Frequency Synchronization

[0048] After time synchronization, frequency offset in the signal needs to be compensated to ensure frequency consistency between the attitude sensor and hydrophone data. After time synchronization, the signal containing the pilot symbols is converted to the DS domain (Delay-Sequence domain) using the Walsh-Hadamard Transform (WHT). In the DS domain, the phase characteristics of the pilot signal are used for frequency synchronization. The pilot symbols provide a frequency reference in the DS domain for calculating the carrier frequency offset (CFO). By matching the pilot signals, the frequency offset in the signal is accurately estimated. The estimated CFO value is used to compensate for frequency drift in the signal, ensuring frequency alignment between the attitude and hydrophone data, thereby achieving higher synchronization accuracy.

[0049] Step 5: Inserting the Gesture Sensor Data

[0050] Find the closest sequence to T in sequence A. C If a data point T1 matching the timestamp is found, the data is inserted into the corresponding position in the time series C.

[0051] Set tag1 = 1 to mark the C There is attitude sensor data at the time point; if no data point that meets the conditions is found, tag1 = 0.

[0052] Step 6: Inserting Hydrophone Data

[0053] Similarly, find the closest sequence to T in sequence B. C If a data point T2 matching the timestamp is found, the data is inserted into the corresponding position in the time series C.

[0054] Set tag2 = 1 to mark the C There is hydrophone data at the time point; if no data point that meets the conditions is found, tag2 = 0.

[0055] Step 7: Screen and generate alignment data sequence D

[0056] Check each time point of time series C and find the time point that satisfies both tag1 = 1 and tag2 = 1, that is, find the synchronization point containing the attitude sensor and hydrophone data.

[0057] The data entries that meet the conditions are extracted to generate a new aligned data sequence D.

[0058] Step 8: Output data sequence D

[0059] The data sequence D contains the aligned attitude sensor and hydrophone data, which can be used for subsequent analysis and processing.

[0060] Specific application scenarios of the method of this embodiment include Case 1 and Case 2.

[0061] Case 1: Used for marine dynamic environment monitoring

[0062] In the marine environment, real-time monitoring of the dynamic changes in ocean currents and the acoustic noise characteristics of the surrounding environment is crucial for marine research and the maintenance of underwater facilities. This invention achieves dynamic monitoring of complex underwater environments by aligning high-precision data from attitude sensors and hydrophones.

[0063] Experimental process:

[0064] 1. Data Collection:

[0065] The attitude sensor monitors the device's angle and displacement underwater to assess the impact of ocean currents on its attitude. The hydrophone collects acoustic signals from the surrounding environment to record the characteristics of ocean noise. The data collection period is set to 10 minutes, with the attitude sensor collecting data at a 10Hz frequency and the hydrophone collecting acoustic signals at a 5Hz frequency.

[0066] 2. Pilot Symbol Insertion and Propagation: Pilot symbols are embedded in the hydrophone's collected signal to facilitate subsequent time and frequency synchronization. Pilot symbols are placed at specific locations within the signal frame and propagate to the receiver along different paths, which incur varying delays due to multipath effects.

[0067] 3 Delay and frequency compensation:

[0068] Delay compensation: The multipath weighted averaging method is used to perform weighted averaging on the delays of different paths to obtain the final synchronization delay, and the delay of the hydrophone acoustic signal is compensated.

[0069] Frequency synchronization: The signal is converted to the delay-sequence domain (DS domain) through the Walsh-Hadamard transform (WHT). The phase information of the pilot signal is used for frequency synchronization to compensate for the carrier frequency offset in the signal and ensure the frequency consistency of the attitude sensor and hydrophone data.

[0070] 4 Data alignment and analysis:

[0071] The attitude sensor data and the compensated hydrophone data are aligned to generate an aligned data sequence D. This aligned data is used to analyze the impact of ocean currents on the device's attitude, as well as the source and intensity of acoustic noise. Further data processing of data sequence D allows the correlation between the device's attitude and ambient noise to be calculated, thereby helping to understand the impact of ocean dynamics on the device.

[0072] Case 2: Accurately locate the sound source

[0073] 1. Data Collection:

[0074] The attitude sensor monitors the underwater motion of the positioning device, ensuring real-time recording of its attitude information. The hydrophone monitors the acoustic signals emitted by underwater sound sources, capturing the various paths and signal changes during their propagation. The data collection period is set to 15 minutes, with a sampling frequency of 10 Hz for the attitude sensor and 5 Hz for the hydrophone.

[0075] 2 Insertion of pilot symbols:

[0076] Specific pilot symbols are embedded in the sound source signal to identify the signal's propagation path and perform time synchronization. Due to the complexity of the underwater environment, the sound source signal reaches the receiver through multiple paths, and different paths produce different propagation delays.

[0077] 3 Multipath Delay Estimation and Frequency Synchronization:

[0078] Delay Estimation: In the DT (Delay-Time) domain, a sliding window is used to search for the position of pilot symbols to determine the propagation delay of each path. The final delay compensation value is then calculated through weighted averaging and applied to the hydrophone data.

[0079] Frequency synchronization: The signal is converted to the DS domain using the WHT transform, and the carrier frequency offset is compensated by the phase characteristics of the pilot symbol to ensure the frequency consistency of the attitude data and the hydrophone data.

[0080] 4 Sound source localization:

[0081] After delay and frequency compensation, an aligned data sequence D is generated. Using this aligned data, combined with a Doppler shift algorithm, the sound source position is estimated. The relative position of the sound source relative to each receiver is calculated by the time delay differences between the pilot symbols received by multiple receivers.

[0082] This application is a method for aligning attitude sensor and hydrophone data based on OTSM synchronization technology. Through the synchronization algorithm of the time axis and the delay axis, pilot symbols are inserted into the hydrophone signal. The delay on different propagation paths is determined by finding the peak of the pilot signal to synchronize the data. The signal containing the pilot symbol is combined with the Walsh-Hadamard transform frequency compensation to achieve precise frequency alignment of multi-sensor data in complex environments, ensure the frequency alignment of attitude data and hydrophone data, and achieve higher synchronization accuracy.

Claims

1. A method for aligning attitude sensor and hydrophone data based on OTSM, characterized in that: include: Step S1, independently collecting attitude sensor data and hydrophone data to obtain attitude sensor data sequence A and hydrophone data sequence B; Step S2: Create a time series C. The time interval of time series C is smaller than the time interval of sequence A and sequence B, so that the timestamps of sequence A and sequence B can find the closest matching point in C. Select a time point T from time series C. C As a reference time for the current processing; Step S3, inserting pilot symbols into the hydrophone signal, placing the pilot symbols at specific positions in each frame; Step S4: Using a sliding window search method to identify the peak position of the pilot symbol in the Delay-Time domain, thereby determining the delay of the signal on different paths and performing time synchronization on the data; Step S5: After completing the time synchronization, it is necessary to compensate for the frequency offset in the signal to achieve frequency consistency between the attitude sensor and the hydrophone data; Step S6: Find the closest T in the attitude sensor data sequence A and the hydrophone data sequence B. C The corresponding data points in sequence A and sequence B are inserted into the corresponding positions in time series C to update time series C; Step S7: Check each time point of the time series C to find the synchronization point containing both the attitude sensor and the hydrophone data, extract the data entries that meet the conditions, and generate a new aligned data sequence D; Step S8: Output the aligned data sequence for subsequent analysis and processing.

2. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 1, characterized in that: The attitude sensor data in step S1 includes attitude angle, time and acceleration, and the hydrophone data includes sound wave signal, frequency, sound intensity, waveform and time.

3. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 1, characterized in that: The fact that the time interval of the time series C in step S2 is shorter than the time intervals of the sequence A and the sequence B includes: the sampling frequency of the attitude sensor is 10 Hz, the sampling frequency of the hydrophone is 5 Hz, and the time interval of the time series C is 1 Hz.

4. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 1, characterized in that: The step S2 includes: defining a time series C(T n , tag1, attitude, tag2, hydrophone), where T n is time, attitude is attitude sensor data, hydrophone is hydrophone data, tag1 and tag2 represent attitude sensor data and hydrophone data tags respectively, and the initial values of tag1 and tag2 are 0.

5. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 1, characterized in that: The step S4 further includes: performing weighted averaging on the delays on different paths to calculate the final synchronization delay, and the calculation formula is: ,in, is the delay of the i-th path , N is the number of different signal propagation paths, is the weight of different signal propagation paths; the received hydrophone signal is delayed and compensated according to the weighted average delay, so that the attitude sensor and hydrophone data are synchronized in time.

6. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 1, characterized in that: The step S5 includes: after time synchronization, converting the signal containing the pilot symbol into the delay-sequence domain through Walsh-Hadamard transform, and performing frequency synchronization in the delay-sequence domain using the phase characteristics of the pilot signal.

7. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 6, characterized in that: The frequency synchronization using the phase characteristics of the pilot signal in step S5 includes: calculating the carrier frequency offset CFO using the pilot symbol as a frequency reference, and using the estimated CFO value to compensate for the frequency drift in the signal to ensure that the frequency of the attitude data and the hydrophone data are aligned.

8. The method for aligning attitude sensor and hydrophone data based on OTSM according to claim 1, characterized in that: The step S6 specifically includes: Step S61, search for the closest T in sequence A C If a data point T1 matching the timestamp is found, the data is inserted into the corresponding position in the time series C and tag1=1 is set to mark the data point at T C There is attitude sensor data at the time point; if no data point that meets the conditions is found, tag1=0; Step S62, search for the closest T in sequence B C If a data point T2 matching the timestamp is found, the data is inserted into the corresponding position in the time series C and tag2=1 is set to mark the data point at T C There is hydrophone data at the time point; if no data point that meets the conditions is found, tag2=0.

9. A computer device comprising a memory, a processor, and program instructions stored in the memory and executable by the processor, wherein the processor executes the program instructions to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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