Ocean internal wave feature detection method based on AUV (Autonomous Underwater Vehicle) depth-keeping navigation

Abstract

The invention provides an ocean internal wave feature detection method based on AUV fixed-depth navigation, and relates to the technical field of ocean exploration, ocean background profile data are collected when an AUV sinks, and actually measured temperature and actually measured depth data are collected when the AUV is fixed depth; the method comprises the following steps: correcting actually measured temperature and actually measured depth data according to ocean background profile data, converting a water body temperature time sequence of an AUV depth-keeping navigation time period into a water body fluctuation time sequence, retaining a low-frequency signal in water body displacement fluctuation by using an LMD (Local Mean Decomposition) method, and determining the amplitude and wavelength of an internal wave.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean exploration, and specifically to a method for detecting ocean internal wave characteristics based on the constant-depth navigation of an AUV. Background Technique

[0004] Defects and deficiencies of the prior art:

[0005] Compared with traditional ocean internal wave detection means, the method of directly detecting ocean internal waves based on an autonomous underwater vehicle (AUV) has a low deployment cost, strong mobility, and can perform large-scale sampling in the entire ocean space. The current known research on detecting and identifying ocean internal waves based on an AUV platform has the following defects:

[0006] Regarding the method of judging and identifying ocean internal waves, existing patents and literature only stay in qualitative analysis and do not provide specific operation steps and judgment formulas for quantitative analysis;

[0007] The three-dimensional ocean background profile environment is complex and affected by factors such as geographical location, season, and ocean phenomena. Relying on the currently known background database for local small-scale fitting of ocean temperature-depth profiles has a large error;

[0008] No specific scheme for extracting ocean internal wave characteristics is provided. Summary of the Invention

[0009] To solve the above technical problems, the present invention proposes a method for detecting ocean internal wave characteristics based on the constant-depth navigation of an AUV. When the AUV sinks, it collects ocean background profile data, and when the AUV is at a constant depth, it collects measured temperature and measured depth data; based on the ocean background profile data, the measured temperature and measured depth data are corrected, and the water temperature time series during the AUV constant-depth navigation period is converted into a water body fluctuation time series. The LMD local mean decomposition method is used to retain the low-frequency signal in the water body displacement fluctuation and determine the amplitude and wavelength of the internal wave.

[0010] In a preferred embodiment, if there are ocean internal waves at the location where the AUV is navigating at a constant depth, it will show an increase in the fluctuation amplitude in the water body fluctuation time series. When the measured water body fluctuation amplitude is greater than the set ocean internal wave amplitude, it is considered that there may be ocean internal waves in this area; when the measured water body fluctuation amplitude, wavelength, and period all meet the characteristics of ocean internal waves, it is considered that there are ocean internal waves in this area.

[0011] In a preferred embodiment, the LMD local mean decomposition method includes:

[0012] Finding local extreme points: For the given water body fluctuation amplitude time series x(t), find all local extreme points of this series. Let the local maximum points of x(t) be xmax (i), the local minimum point is x min (j), where i and j are the serial numbers of the maximum point and the minimum point respectively;

[0013] Calculate the local mean function and the envelope estimation function:

[0014] For adjacent maximum points x max (i) and x max (i + 1), as well as adjacent minimum points x min (j) and x min (j + 1), calculate the local mean function m n (t) by the method of linear interpolation;

[0015] Calculate the mean between adjacent extreme points as the sampling points of the envelope estimation function a n (t), and obtain the envelope estimation function a n (t) by the method of interpolation;

[0016] Calculate the pure frequency modulation function: Subtract its local mean function m n (t) from the water body fluctuation amplitude time series x(t) to obtain h n (t) = x(t) - m n (t), divide h n (t) by the envelope estimation function a n (t) to obtain the pure frequency modulation function

[0017] Calculate the instantaneous frequency: Perform the Hilbert transform on the pure frequency modulation function S n (t) to obtain Then construct the analytic signal Its instantaneous phase Take the derivative of the instantaneous phase to obtain the instantaneous frequency

[0018] Construct the product function: According to the obtained envelope estimation function a n (t) and the instantaneous frequency ω n (t), construct the product function

[0019] Signal decomposition: Decompose the water body fluctuation amplitude time series x(t) into the sum of a series of product functions PF n (t), where N is the number of product functions obtained by decomposition, and r(t) is the residual term.

[0020] In a preferred embodiment, the characteristic mode presented by the last product function obtained by decomposing LMD is superimposed on the residual term to obtain the time series of internal wave feature extraction. This result is used as the basis for judging the water body fluctuation boundary, and then the wavelength and amplitude of the fluctuation are calculated. If the calculation result conforms to the scale characteristics of ocean internal waves, it is judged as ocean internal waves; otherwise, it is judged as background data. If it is judged as ocean internal waves, the wavelength and amplitude of the fluctuation are the characteristics of ocean internal waves.

[0021] In a preferred embodiment, after the data is judged as ocean internal wave data, the Hilbert transform is performed on the obtained water body fluctuation amplitude time series s(t):

[0022] Calculate the convolution of the time series s(t)

[0023] Combine s(t) and to form a new time series z(t),

[0024] where A(t) and θ(t) are the instantaneous amplitude and instantaneous phase of the time series respectively;

[0025] Calculate the instantaneous frequency ω(t) and characteristic time scale T from the instantaneous phase:

[0026]

[0027]

[0028] In a preferred embodiment, preprocess the data collected during AUV depth keeping:

[0029] Select a window to segment the data and calculate the mean square deviation STD of each segment of data:

[0030]

[0031] where, A i is the real ocean data measured by the AUV at the i-th moment, N is the total number of data in the data segment; μ is the average value of the real ocean data in the data segment:

[0032]

[0033] If the deviation between the observed value at the i-th moment in the data segment and the mean value of the data segment is greater than 4.5 times the STD, it is considered that the observed value at this moment is inaccurate, and the mean value of the 10 data before and after this time period is used as the observed value at this moment.

[0034] Compared with the prior art, the present invention has the following beneficial technical effects:

[0035] The method of directly detecting internal waves in the ocean based on AUV has low deployment cost, strong mobility, and can conduct large-scale sampling in the entire ocean space. Compared with the traditional method of using AUV to measure internal waves in the ocean, it has the following advantages:

[0036] Regarding the method of judging and identifying internal waves in the ocean, specific operation steps and judgment formulas are provided, which can conduct quantitative analysis of internal waves in the ocean;

[0037] The three-dimensional background profile of the ocean is complex and affected by factors such as geographical location, season, and ocean phenomena. Using real-time measured ocean environment data for temperature-depth profile fitting to characterize the local ocean environment is more representative and accurate than the traditional method (fitting profile from the database);

[0038] A specific scheme for extracting internal wave characteristics is provided. Description of the Drawings

[0039] Figure 1 It is a flowchart for internal wave judgment;

[0040] Figure 2 It is the measured data of the TD sensor carried by the AUV;

[0041] Figure 3 It is the ocean background profile;

[0042] Figure 4 It is the measured temperature data of the AUV at a fixed depth of 50m;

[0043] Figure 5 It is the measured depth data of the AUV at a fixed depth of 50m;

[0044] Figure 6 It is the calculated background depth of the water body;

[0045] Figure 7 It is the displacement fluctuation of the water body relative to the 0m position;

[0046] Figure 8 It is the depth displacement fluctuation of the water body relative to the average fixed depth position;

[0047] Figure 9 It is the LMD transform stratification result;

[0048] Figure 10 It is the low-frequency signal in the water body displacement fluctuation;

[0049] Figure 11 It is the Hilbert transform result;

[0050] Figure 12 It is a flowchart for feature extraction and determination of internal wave types. Detailed Implementation Manner

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] A method for detecting the characteristics of internal waves in the ocean based on the depth-keeping navigation of an AUV is mainly used to process the ocean environmental data collected by an AUV equipped with a temperature-depth sensor (TD), determine whether there are internal waves in the ocean environment, and extract the wavelength, amplitude, and period characteristics of the internal waves if they exist.

[0053] The operation process of the AUV after it enters the water can be seen from Figure 2 and can be specifically divided into the following four stages:

[0054] (1) The AUV sinking stage, at this time the AUV collects the ocean background profile data, and the fitted temperature-depth curve can be used to characterize the local ocean background field (in order to accurately depict the background field, the AUV generally sinks to a position deeper than the planned navigation depth in the first stage of its operation);

[0055] (2) The AUV floating up to the depth-keeping stage;

[0056] (3) The AUV depth-keeping stage, at this time the AUV collects the ocean environmental data, and the present algorithm invention mainly uses the temperature and depth data obtained by the TD sensor in this stage to judge the internal waves and extract the characteristics;

[0057] (4) The AUV floating up to the sea surface stage.

[0058] The present method invention mainly uses the data in the above stages (1) and (3), combines the time series analysis method and the statistical method, corrects the measured temperature and measured depth data measured by the AUV at depth-keeping according to the ocean environmental background profile, filters out the influence of the platform depth fluctuation on the measured temperature data, converts the water temperature time series of the AUV during depth-keeping navigation for a period of time into water body fluctuations, and then uses the LMD local mean decomposition method to retain the low-frequency signal in the water body displacement fluctuations, so as to determine the amplitude and wavelength of the internal waves. If there are ocean internal waves at the depth-keeping navigation of the AUV, the fluctuation amplitude will increase in the water body fluctuation time series. Therefore, when the measured water body fluctuation amplitude is greater than the set ocean internal wave amplitude, it is considered that there may be ocean internal waves in this area; when the measured water body fluctuation amplitude, wavelength, and period all meet the characteristics of ocean internal waves, it is considered that there are ocean internal waves in this area.

[0059] The specific process of this method is as Figure 1 shown, and the specific steps are as follows:

[0060] (1) Preprocess the marine environmental data measured during the AUV underwater operation;

[0061] During this process, quality control of the data is mainly carried out, which mainly refers to correcting the observed data in case of missing values or observed data deviating from the normal range at individual moments during on-site observations. The method is as follows:

[0062] First, select a window to segment the data; then calculate the standard deviation (STD) of each segment of data, and the formula is as follows:

[0063]

[0064] where, A i is the real marine data measured by the AUV at a certain moment, which can be temperature data, pressure data, depth data, etc.; N is the total number of data in the data segment; μ is the average value of the real marine data in the data segment, and the formula is as follows:

[0065]

[0066] If the deviation between the observed value at a certain moment in the data segment and the mean value of the data segment is greater than 4.5 times the STD, it is considered that the observed value at this moment is inaccurate, and the mean value of the 10 data before and after this period is used as the observed value at this moment.

[0067] (2) Use the marine environmental data in the first stage of the AUV underwater operation after preprocessing to draw a curve of the change of ocean temperature with depth (as Figure 3 shown); use the AUV depth-fixed data in the third stage of the AUV underwater operation after preprocessing to draw a temperature-depth time series diagram, as Figure 4 and Figure 5 shown.

[0068] (3) Substitute the preprocessed temperature-depth time series data into the marine background temperature-depth fitting curve (as Figure 3 shown) to obtain the time series of the water depth fluctuation amplitude in the AUV depth-fixed stage (as Figure 6 shown);

[0069] (4) Use LMD decomposition to perform modal decomposition on the time series of the water body fluctuation amplitude within the selected window length (as Figure 9 shown);

[0070] LMD (Local Mean Decomposition) is a method proposed by Jonathan S. Smith in 2005 for processing non-linear and non-stationary signals. Its core idea is to decompose a complex non-linear and non-stationary signal into the sum of a series of product functions (PFs) with physical meanings. It separates different frequency components and modulation characteristics in the signal through local analysis of the signal. The basic assumption of LMD decomposition is that the signal is composed of the superposition of multiple signals with different frequencies, and each signal can be represented by a PF. The specific decomposition process and formula are as follows:

[0071] Find local extreme points

[0072] For the given time series x(t) of the water body fluctuation amplitude, all local extreme points of this series should be found first. Let the local maximum points of x(t) be x max (i), and the local minimum points be x min (j), where i and j are the sequence numbers of the maximum points and minimum points respectively.

[0073] Calculate the local mean function and the envelope estimation function

[0074] Local mean function: For adjacent maximum points x max (i) and x max (i + 1), as well as adjacent minimum points x min (j) and x min (j + 1), calculate the local mean function m n (t) by linear interpolation. For example, between two adjacent maximum points, the local mean function is There is a similar calculation formula between two adjacent minimum points, and then all these local mean functions are connected to obtain the local mean function m n (t) of the entire signal.

[0075] Envelope estimation function: Calculate the mean between adjacent extreme points (maximum points and minimum points) as the sampling points of the envelope estimation function a n (t), and obtain the envelope estimation function a n (t) by interpolation as well.

[0076] Calculate the pure frequency modulation function

[0077] Subtract the local mean function m n (t) from the time series x(t) of the water body fluctuation amplitude to obtain h n (t) = x(t) - m n (t), and then h n(t) divided by the envelope estimation function a n (t), we get the pure frequency modulation function

[0078] Calculate instantaneous frequency

[0079] For pure frequency modulation function S n (t) is subjected to Hilbert transform, and we get Then construct the parsing signal Its instantaneous phase Taking the derivative of the instantaneous phase gives the instantaneous frequency

[0080] Constructing a product function

[0081] Estimate function a based on the obtained envelope n (t) and instantaneous frequency ω n (t), construct the product function

[0082] signal decomposition

[0083] Decompose the water body fluctuation amplitude time series x(t) into a series of product functions PF n (t), that is Where N is the number of product functions obtained by decomposition, and r(t) is the residual term, which is usually the trend term or noise in the sequence that cannot be further decomposed.

[0084] Through the above LMD decomposition process, the complex AUV water body fluctuation amplitude time series can be decomposed into multiple product functions, each of which reflects the frequency and amplitude modulation characteristics of the water body fluctuation amplitude under different local characteristics.

[0085] (5) The characteristic mode presented by the last product function obtained by LMD decomposition is superimposed with the residual term to obtain the time series of internal wave feature extraction, as shown in Figure 10 This result is used as the basis for judging the boundary of water body fluctuations, and then the wavelength and amplitude of the fluctuations are calculated. If the calculation result conforms to the scale characteristics of ocean internal waves, it is judged to be ocean internal waves, otherwise it is judged to be background data; if it is judged to be an ocean internal wave, then the wavelength and amplitude of the fluctuation are the characteristics of ocean internal waves.

[0086] (6) After the data is determined to be ocean internal wave data, the corresponding water body fluctuation amplitude time series obtained in step (3) is subjected to Hilbert transform.

[0087] Taking the water body fluctuation amplitude time series s(t) as an example, the process of calculating its time scale using Hilbert transform is as follows:

[0088] Calculate the convolution of the time series s(t)

[0089] Combine s(t) with to form a new time series z(t),

[0090] where A(t) and θ(t) are the instantaneous amplitude and instantaneous phase of the time series respectively;

[0091] Calculate the instantaneous frequency ω(t) and the characteristic time scale T from the instantaneous phase:

[0092]

[0093]

[0094] In practical applications, the influence of the boundary effect of the signal often needs to be considered. When calculating the characteristic time scale, a small part should be removed before the start and end of the instantaneous frequency ω(t) to improve the accuracy of the characteristic time scale.

[0095] The time-frequency distribution result calculated therefrom is as Figure 11 shown, where the main frequency period is also one of the characteristics of internal waves in the ocean.

[0096] (7) Determine the type of the measured internal wave, such as an internal solitary wave or a wave train, based on the characteristics of the internal wave in the ocean obtained in steps (5) and (6), including the wavelength, amplitude, and period, and store them in the internal wave feature set as a reference for the next internal wave judgment.

[0097] The following takes the measured data during a certain experiment when the AUV depth is fixed at 50 m as an example to describe the principle and working process of the water body displacement fluctuation method.

[0098] In the first step, find the corresponding water body background depth (as Figure 3 shown) of each measured temperature data (as Figure 4 shown) when the AUV depth is fixed at 50 m in the measured background profile (as Figure 6 shown). For example, at 12:43, the measured temperature data of the AUV is 23.2 °C, and in the background profile, 23.2 °C corresponds to 32 m, that is, the water body background depth measured by the AUV at this moment is 32 m; at 12:56, the corresponding measured temperature data of the AUV is 22.1 °C, and in the background profile, 22.1 °C corresponds to 42 m, that is, the water body background depth measured by the AUV at this moment is 42 m. Calculate the depth of the water body where the AUV measures the temperature in this way.

[0099] In the second step, subtract the water body background depth obtained in the first step (as Figure 5 shown) from the actual platform depth fluctuation (asFigure 6 As shown in the figure), the depth fluctuation of the water body relative to the 0m position is obtained, as Figure 7 shown.

[0100] The third step is to calculate the average depth of the actual platform depth fluctuation when the AUV is at a fixed depth of 50m. This depth is generally near the fixed depth. The fixed depth average depth is added to the depth fluctuation of the water body relative to the 0m position calculated in the second step to obtain the depth fluctuation of the water body relative to the average fixed depth position, as shown in the figure below.

[0101] The fourth step is to use the LMD local mean decomposition method to process the water depth displacement fluctuation results. The decomposition results are as follows: Figure 9 As shown, retain the last product function (i.e., the fourth layer result in this example) and the residual term after LMD decomposition, and add them together to get the result as follows Figure 10 As shown in the figure, it is the low-frequency signal in the water displacement fluctuation when the AUV is at a fixed depth of 50m.

[0102] The fifth step is to determine the start and end time of the water body fluctuations based on the low-frequency signal in the water displacement fluctuations when the AUV is at a fixed depth of 50m, and calculate the amplitude and wavelength of the water body fluctuations based on the start and end times. As shown in the attached figure, the maximum fluctuation of the water body displacement is between 12:35 and 12:47 at this time. The internal wave amplitude is calculated to be 31.2m and the internal wave wavelength is 1612.8m. It is judged that the fluctuation is suspected to be an ocean internal wave. Similarly, the signals and characteristics of the water body temperature fluctuations can be obtained according to the above method.

[0103] Step 6: After determining that the depth determination data contains internal waves, the result obtained in step 3 (such as Figure 8 The Hilbert transform is performed on the internal waves (as shown in Figure 1), and one eighth of the data is removed before and after to reduce the impact of edge effects on time calculation. The time-frequency characteristics of the internal waves obtained after the Hilbert transform are as follows: Figure 11 As shown, the main frequency period is 13.4 minutes.

[0104] The seventh step is to compare the suspected signal characteristics with the ocean internal wave characteristics, determine the ocean internal wave type, and store it in the feature data set. The flow chart is as follows: Figure 12 shown.

[0105] At this point, the specific process of the algorithm for ocean internal wave judgment and feature extraction based on AUV of the present invention is completed.

[0106] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes that fall within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. An ocean internal wave feature detection method based on the depth-keeping navigation of an AUV, characterized in that, When the AUV sinks, it collects ocean background profile data. When the AUV maintains a constant depth, it collects measured temperature and measured depth data. Based on the ocean background profile data, the measured temperature and measured depth data are corrected. The water temperature time series during the period when the AUV sails at a constant depth is converted into a water body fluctuation time series. The LMD (Local Mean Decomposition) method is used to retain the low-frequency signal in the water body displacement fluctuation, and the amplitude and wavelength of the internal wave are determined.

2. The method for detecting ocean internal wave characteristics based on the depth-keeping navigation of an AUV according to claim 1, wherein If there is an ocean internal wave at the location where the AUV sails at a constant depth, it will show an increase in the fluctuation amplitude in the water body fluctuation time series. When the measured water body fluctuation amplitude is greater than the set ocean internal wave amplitude, it is considered that there may be an ocean internal wave in this area. When the measured water body fluctuation amplitude, wavelength, and period all meet the characteristics of ocean internal waves, it is considered that there is an ocean internal wave in this area.

3. The method for detecting ocean internal wave characteristics based on the depth-keeping navigation of an AUV according to claim 2, characterized in that, The LMD (Local Mean Decomposition) method includes: Finding local extreme points: For a given time series x(t) of water body fluctuation amplitudes, find all local extreme points of the series. Let the local maximum points of x(t) be x max (i), and the local minimum points be x min (j), where i and j are the serial numbers of the maximum and minimum points respectively; Calculate the local mean function and the envelope estimation function: For adjacent maximum points x max (i) and x max (i + 1), and adjacent minimum points x min (j) and x min (j + 1), calculate the local mean function m n (t) by linear interpolation; Calculate the mean value between adjacent extreme points as the envelope estimation function a n For the sampling points of (t), obtain the envelope estimation function a n (t) through interpolation; Calculating the pure frequency modulation function: Subtract the local mean function m of the water body fluctuation amplitude time series x(t) from it to obtain h n (t), getting h n (t) = x(t) - m n (t). Divide h n (t) by the envelope estimation function a n (t) to obtain the pure frequency modulation function Calculate the instantaneous frequency: For the pure frequency modulation function S n (t), perform the Hilbert transform to obtain Then construct the analytic signal whose instantaneous phase Take the derivative of the instantaneous phase to obtain the instantaneous frequency Construct a product function: Based on the obtained envelope estimation function a n (t) and the instantaneous frequency ω n (t), construct the product function Signal decomposition: Decompose the water body fluctuation amplitude time series x(t) into a sum of a series of product functions PF n (t), where N is the number of product functions obtained by decomposition, and r(t) is the residual term.

4. The method for detecting ocean internal wave characteristics based on the depth-keeping navigation of an AUV according to claim 3, wherein The characteristic mode presented by the last product function obtained by LMD decomposition is superimposed on the residual term to obtain the time series for internal wave feature extraction. This result is used as the basis for judging the boundary of the water body fluctuation, and then the wavelength and amplitude of this fluctuation are calculated. If the calculation result conforms to the scale characteristics of ocean internal waves, it is judged as an ocean internal wave; otherwise, it is judged as background data. If it is judged as an ocean internal wave, the wavelength and amplitude of the fluctuation are the characteristics of the ocean internal wave.

5. The method for detecting ocean internal wave characteristics based on AUV depth-keeping navigation according to claim 4, characterized in that After the data is judged as ocean internal wave data, the obtained water body fluctuation amplitude time series s(t) is subjected to Hilbert transform: Calculate the convolution of the time series s(t) Combine s(t) and to form a new time series z(t). where A(t) and θ(t) are the instantaneous amplitude and instantaneous phase of the time series respectively; The instantaneous frequency ω(t) and characteristic time scale T are calculated from the instantaneous phase:

6. The method for detecting ocean internal wave characteristics based on the depth-keeping navigation of an AUV according to claim 1, wherein Preprocess the data collected when the AUV maintains a constant depth: Select a window to segment the data and calculate the standard deviation STD of each segment of data: Among them, A i is the real ocean data measured by the AUV at the i-th moment, N is the total number of data in the data segment; μ is the average value of the real ocean data in the data segment: If the deviation of the observed value at time i within the data segment from the mean value of this data segment is greater than 4.5 times the STD, it is considered that the observed value at this time is inaccurate, and the mean value of 10 data before and after this time period is used as the observed value at this time.

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