Doppler log bottom tracking method and system based on signal frequency domain energy combined criterion

Abstract

The invention relates to a Doppler log bottom tracking method and system based on a signal frequency domain energy joint criterion. The method comprises the following steps: carrying out band-pass filtering on a Doppler log echo signal; carrying out fast Fourier transform; selecting an effective frequency band range according to the system working frequency, the speed measurement range and the beam included angle; retaining spectral lines in an effective frequency band range, and setting the amplitudes of other spectral lines to be zero; performing fast inverse Fourier transform on the signals in the remaining frequency bands; constructing an energy detection sequence according to the pulse width of the transmitted signal and calculating a background noise energy reference; calculating a time domain energy ratio; performing fast Fourier transform on echo signal groups; calculating a frequency domain confidence factor; and determining the position of the underwater echo signal by using double criteria. According to the invention, stable and reliable bottom tracking can be realized under the conditions of low signal-to-noise ratio, strong noise interference environment and complex underwater topography; meanwhile, the self-adaptive adjustment function is achieved, and self-adaptive adjustment of the bandwidth and self-adaptive adjustment of bottom tracking criterion parameters can be achieved under different hydrological conditions.

Description

Technical Field

[0001] This invention relates to the field of acoustic Doppler measurement technology, specifically a method and system for accurately tracking seabed echoes by combining frequency domain feature screening and energy sequence analysis, which is particularly suitable for underwater tracking scenarios using Doppler logs in environments with strong noise interference. Background Technology

[0002] Doppler logs, as crucial equipment in ship navigation and marine engineering, primarily function to accurately measure a ship's speed and distance relative to the seabed through the acoustic Doppler effect, providing critical data support for safe navigation, marine resource exploration, and underwater operations. Bottom tracking technology is one of the core technologies of Doppler logs. However, due to the influence of factors such as the distribution of scattering bodies, sound wave propagation attenuation, seabed topography, and the ship's own rolling, it is necessary to address the complexity of bottom determination and adaptability to dynamic environments. Current Doppler log bottom tracking methods have limitations, including the lag in response to transient energy changes in frequency domain analysis methods and the susceptibility to misjudgments in complex frequency response environments. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a Doppler log bottom tracking method and system based on a joint criterion of signal frequency domain energy.

[0004] One of the above-mentioned objectives of the present invention is achieved by the following technical solution:

[0005] A Doppler log bottom tracking method based on signal frequency domain energy joint criterion includes the following steps:

[0006] S1: Bandpass filtering is applied to the Doppler log signal to achieve echo signal preprocessing;

[0007] S2: Perform a fast Fourier transform on the preprocessed echo signal;

[0008] S3: Select the effective frequency band range based on the system's operating frequency, speed measurement range, and beam angle;

[0009] S4: Perform spectrum purification, retaining spectral lines within the effective frequency band and setting the amplitude of other spectral lines to zero;

[0010] S5: Perform fast inverse Fourier transform on the signal segments within the retained frequency band, and then use the overlapping addition method to reconstruct the time domain;

[0011] S6: Construct an energy detection sequence based on the pulse width of the transmitted signal and calculate the background noise energy reference;

[0012] S7: Calculate the time-domain energy ratio;

[0013] S8: Group the Doppler log echo signals according to the transmitted signal length, and then perform a fast Fourier transform;

[0014] S9: Calculate the frequency domain confidence factor based on the Fourier transform results of S8;

[0015] S10: Based on the time-domain energy ratio calculated in S7 and the frequency-domain confidence factor calculated in S9, the arrival time of the effective echo at the bottom of the water is determined by applying the dual criteria.

[0016] Furthermore, in step S3, based on the system operating frequency f a Speed ​​measurement range ±v max Given a beam angle α, a sound speed in water of C, and the selected effective frequency band range: [f] a -Δf,f a +Δf], where:

[0017]

[0018] Based on the effective frequency band range, construct the frequency domain mask matrix M(k):

[0019]

[0020] Furthermore, in step S4, the fast Fourier transform result X(k) of the echo signal x(n) is processed to obtain...

[0021]

[0022] Furthermore, in step S5, the processed spectral signal... Perform an inverse fast Fourier transform and use the overlap-addition method to recover the continuous time-domain signal. Specifically, it includes:

[0023] First Divide into L overlapping segments, using This indicates that the length is M, and there are R overlaps between adjacent segments. First, perform a fast inverse Fourier transform on each segment:

[0024]

[0025] Then, the continuous time-domain signal is recovered by superposition.

[0026]

[0027] Where w(n) is a window function used to smooth the overlapping parts.

[0028] Furthermore, step S6 includes:

[0029] Constructing an energy detection sequence using a sliding energy calculation window:

[0030]

[0031] Where W is the pulse width of the transmitted signal;

[0032] Then, select received data with a signal pre-signal gap of L = 50ms, and calculate the background noise energy reference E. noise :

[0033]

[0034] Furthermore, in step S7, the time-domain energy ratio is calculated as follows:

[0035]

[0036] Furthermore, in step S8, the echo signals are grouped according to the echo signal x(n):

[0037] y n (i)=x(n+i)i∈[0,W)

[0038] Perform a Fast Fourier Transform on each group of signals to obtain Y. n (k).

[0039] Furthermore, in step S9, according to Y n (k) Calculate the frequency domain confidence factor:

[0040]

[0041] Furthermore, in step S10, the following criteria are applied:

[0042] Condition 1: R e (n)≥2;

[0043] Condition 2:

[0044] Among them, R emax For R e (n) The maximum value of the sequence, λ is the decision parameter, usually taken as 0.05; filter points that simultaneously satisfy conditions 1 and 2, and when multiple points satisfy the conditions, select one of them R. e (n) The maximum value point is the starting point of the bottom echo signal, and the echo signal length is W; if there is no point that meets the condition, the echo signal will lose its bottom.

[0045] When the bottom is lost 3 times in a row, the decision parameter λ = 0.03 + 0.005t is adaptively adjusted, where t is the number of consecutive bottom losses, with a maximum of 8.

[0046] When five consecutive baseline drops occur, a broadband scanning mode is simultaneously initiated, adjusting the effective frequency band to [f]. a -2Δf,f a +2Δf];

[0047] After resuming tracking, switch to adaptive tracking mode.

[0048] The second objective of this invention is achieved through the following technical solution:

[0049] A Doppler log bottom tracking system based on a joint criterion of signal frequency domain energy, used to execute the aforementioned Doppler log bottom tracking method based on a joint criterion of signal frequency domain energy, includes:

[0050] The multi-channel data acquisition module includes: a 16-bit high-precision ADC converter and a voltage-controlled gain amplifier (TVG) for receiving and amplifying echo signals; it also includes an environmental sensing unit, which includes a water temperature sensor, a salinity sensor, and an attitude sensor, for dynamically adjusting Δf.

[0051] The real-time signal processing module includes: an FPGA-implemented FFT / IFFT processing unit and a DSP-implemented adaptive passband selection unit, used for pre-bandpass filtering, FFT / IFFT processing, time-domain energy ratio calculation, frequency-domain confidence factor calculation, and adaptive bandwidth selection of the echo signal.

[0052] The joint criterion decision module includes: frequency domain energy confidence data stored in RAM, and logic decision and control unit implemented by DSP. It is used to extract underwater echo signals according to the criterion conditions based on the calculation results of time domain energy ratio and frequency domain confidence factor, and to handle abnormal situations according to the set mechanism.

[0053] The result output module includes an RS422 / Ethernet interface and a fault alarm indicator, used to output the results to the outside world.

[0054] The advantages and positive effects of this invention are as follows:

[0055] This invention provides a Doppler log bottom tracking method and system based on a joint criterion of signal frequency domain energy. This method enables stable and reliable bottom tracking under conditions such as low signal-to-noise ratio, strong noise interference, and complex underwater topography. It can achieve a 95% bottom tracking accuracy even at a -3dB signal-to-noise ratio and also features adaptive adjustment capabilities, allowing for adaptive adjustment of bandwidth and bottom tracking criterion parameters under different hydrological conditions. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating a Doppler log bottom tracking method based on a joint criterion of signal frequency domain energy provided by the present invention;

[0058] Figure 2 A block diagram of a Doppler log bottom tracking system based on a joint criterion of signal frequency domain energy is provided for this invention.

[0059] In the diagram: 201, Multi-channel data acquisition module; 202, Real-time signal processing module; 203, Joint criterion decision module; 204, Result output module. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0061] In the description of this specification, the references to terms such as "an embodiment," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0062] The following is combined Figure 1 and Figure 2 This invention describes a Doppler log bottom tracking method and system based on a joint criterion of signal frequency domain energy.

[0063] like Figure 1 As shown, this invention provides a Doppler log bottom tracking method based on a joint criterion of signal frequency domain energy, comprising:

[0064] S1: Signal preprocessing, bandpass filtering of the Doppler log echo signal;

[0065] S2: Perform a fast Fourier transform on the Doppler log echo signal;

[0066] S3: Select the effective frequency band range based on the system's operating frequency, speed measurement range, and beam angle;

[0067] S4: Spectrum purification, retaining spectral lines within the effective frequency band and setting the amplitude of other spectral lines to zero;

[0068] S5: Time-domain reconstruction, performing fast inverse Fourier transform on the signal in the remaining frequency band;

[0069] S6: Construct an energy detection sequence based on the pulse width of the transmitted signal and calculate the background noise energy reference;

[0070] S7: Calculate the time-domain energy ratio;

[0071] S8: Group the Doppler log echo signals according to the transmitted signal length, and then perform a fast Fourier transform;

[0072] S9; Calculate the frequency domain confidence factor;

[0073] S10: Apply dual criteria to determine the arrival time of the effective echo from the seabed.

[0074] The present invention provides a Doppler log bottom tracking method based on a joint criterion of signal frequency domain energy. This method involves bandpass filtering the Doppler log echo signal, performing "spectrum purification", performing fast inverse Fourier transform, constructing an energy detection sequence, calculating the time-domain energy ratio, performing fast Fourier transform in groups, calculating the frequency domain confidence factor, and applying dual criteria to determine the position of the underwater echo signal.

[0075] This invention avoids the problems of traditional frequency domain analysis methods lagging in response to transient energy changes and energy detection methods being prone to misjudgment in complex frequency response environments. It reduces the signal-to-noise ratio requirement of echo signals and improves anti-interference capability.

[0076] Specifically, S1: Signal preprocessing, bandpass filtering of the Doppler log echo signal.

[0077] In embodiments of the present invention, the bandpass filter is generally taken as the system operating frequency f. a 10%, passband is generally 10%.

[0078] [0.9f a ,1.1f a ].

[0079] S2: Perform a fast Fourier transform on the Doppler log echo signal.

[0080] In an embodiment of the present invention, the length of the Fast Fourier Transform is consistent with the length of the original Doppler log echo signal.

[0081] S3: Based on the system operating frequency f a Speed ​​measurement range ±v max The beam angle is α, the speed of sound in water is C, and the effective frequency band is: [f a -Δf,f a +Δf], where:

[0082]

[0083] Based on the effective frequency band, construct the frequency domain mask matrix M(k):

[0084]

[0085] In an embodiment of the present invention, the system operates at a frequency of 300 kHz, has a velocity measurement range of ±10 m / s, a beam angle of 30°, a sound speed in water of 1500 m / s, and an effective frequency band of [298 kHz, 302 kHz]. The frequency domain mask matrix M(k) is:

[0086]

[0087] S4: Multiply the Fast Fourier Transform result X(k) of the echo signal x(n) with the mask matrix accordingly to obtain...

[0088]

[0089] In an embodiment of the present invention, calculation We can obtain:

[0090]

[0091] S5: Processing the spectrum signal Perform an inverse fast Fourier transform and use the overlap-addition method to recover the continuous time-domain signal.

[0092] In an embodiment of the present invention, for a length of N To perform a fast inverse Fourier transform, you can first... Divide into L overlapping segments, using This indicates that the length is M, and there are R overlaps between adjacent segments. First, perform a fast inverse Fourier transform on each segment:

[0093]

[0094] Then, the continuous time-domain signal is recovered by superposition.

[0095]

[0096] Where w(n) is a window function used to smooth the overlapping parts.

[0097] S6: Constructing an energy detection sequence using a sliding energy calculation window:

[0098]

[0099] Where W is the pulse width of the transmitted signal. Select received data with a pre-signal gap L = 50ms and calculate the background noise energy reference E. noise :

[0100]

[0101] S7: Calculate the time-domain energy ratio:

[0102]

[0103] S8: Group the echo signals according to the echo signal x(n):

[0104] y n (i)=x(n+i)i∈[0,W)

[0105] Perform a Fast Fourier Transform on each group of signals to obtain Y. n (k).

[0106] S9: According to Y n (k) Calculate the frequency domain confidence factor:

[0107]

[0108] S10: Apply the following criteria:

[0109] Condition 1: R e (n)≥2;

[0110] Condition 2:

[0111] Where R emax For R e (n) The maximum value of the sequence, λ is the decision parameter, usually taken as 0.05. Filter points that simultaneously satisfy conditions 1 and 2; when multiple points satisfy the conditions, select one of them, R. e (n) The maximum value point is the starting point of the bottom echo signal, and the echo signal length is W; if there is no point that meets the condition, the bottom of the echo signal will be lost.

[0112] When the bottom is lost 3 times in a row, the decision parameter λ = 0.03 + 0.005t is adaptively adjusted, where t is the number of consecutive bottom losses, with a maximum of 8.

[0113] When five consecutive baseline drops occur, a broadband scanning mode is simultaneously initiated, adjusting the effective frequency band to [f]. a -2Δf,f a +2Δf];

[0114] After resuming tracking, switch to adaptive tracking mode.

[0115] This invention also provides a Doppler log bottom tracking system based on a joint criterion of signal frequency domain energy, such as... Figure 2 As shown, it includes:

[0116] The multi-channel data acquisition module 201 includes: a 16-bit high-precision ADC converter and a voltage-controlled gain amplifier (TVG) for receiving and amplifying echo signals; it also includes an environmental sensing unit, which includes a water temperature sensor, a salinity sensor and an attitude sensor, for dynamically adjusting Δf in the above method.

[0117] The real-time signal processing module 202 includes: an FFT / IFFT processing unit implemented by an FPGA and an adaptive passband selection unit implemented by a DSP. The real-time signal processing module adopts a heterogeneous computing architecture, with the FPGA responsible for executing FFT / IFFT transformations and frequency domain calculations, and the DSP responsible for calculating dynamic parameters. The two interact with each other through RAM.

[0118] The joint criterion decision module 203 includes: frequency domain energy confidence data stored in RAM; and a logic decision and control unit implemented by DSP.

[0119] The result output module 204 includes: an RS422 / Ethernet interface; and a fault alarm indicator.

[0120] Through the coordinated operation of the above modules, the multi-channel data acquisition module 201 first receives and amplifies the Doppler log echo signal, converting the analog signal into a digital signal. The real-time signal processing module 202 performs pre-bandpass filtering, FFT / IFFT, time-domain energy ratio calculation, frequency-domain confidence factor calculation, and adaptive bandwidth selection on the echo signal. The joint criterion decision module 203 extracts the underwater echo signal based on the calculated time-domain energy ratio and frequency-domain confidence factor according to the criterion conditions, handles abnormal situations according to the set mechanism, and finally outputs the results to the outside through the result output module 204.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A Doppler log bottom tracking method based on a joint criterion of signal frequency domain energy, characterized in that: Includes the following steps: S1: Bandpass filtering is applied to the Doppler log signal to achieve echo signal preprocessing; S2: Perform a fast Fourier transform on the preprocessed echo signal; S3: Select the effective frequency band range based on the system's operating frequency, speed measurement range, and beam angle; S4: Perform spectrum purification, retaining spectral lines within the effective frequency band and setting the amplitude of other spectral lines to zero; S5: Perform fast inverse Fourier transform on the signal segments within the retained frequency band, and then use the overlapping addition method to reconstruct the time domain; S6: Construct an energy detection sequence based on the pulse width of the transmitted signal and calculate the background noise energy reference; S7: Calculate the time-domain energy ratio; S8: Group the Doppler log echo signals according to the transmitted signal length, and then perform a fast Fourier transform; S9: Calculate the frequency domain confidence factor based on the Fourier transform results of S8; S10: Based on the time-domain energy ratio calculated in S7 and the frequency-domain confidence factor calculated in S9, the arrival time of the effective echo at the bottom of the water is determined by applying the dual criteria.

2. The Doppler log bottom tracking method based on signal frequency domain energy joint criterion according to claim 1, characterized in that, In step S3, based on the system operating frequency f a Speed ​​measurement range ±v max Given a beam angle α, a sound speed in water of C, and the selected effective frequency band range: [f] a -Δf,f a +Δf], where: Based on the effective frequency band range, construct the frequency domain mask matrix M(k):

3. The Doppler log bottom tracking method based on the joint criterion of signal frequency domain energy as described in claim 2, characterized in that: In step S4, the Fast Fourier Transform result X(k) of the echo signal x(n) is processed to obtain...

4. The Doppler log bottom tracking method based on signal frequency domain energy joint criterion according to claim 3, characterized in that: In step S5, the processed spectrum signal Perform an inverse fast Fourier transform and use the overlap-addition method to recover the continuous time-domain signal. Specifically, it includes: First Divide into L overlapping segments, using This indicates that the length is M, and there are R overlaps between adjacent segments. First, perform a fast inverse Fourier transform on each segment: Then, the continuous time-domain signal is recovered by superposition. Where w(n) is a window function used to smooth the overlapping parts.

5. The Doppler log bottom tracking method based on the joint criterion of signal frequency domain energy according to claim 4, characterized in that: Step S6 includes: Constructing an energy detection sequence using a sliding energy calculation window: Where W is the pulse width of the transmitted signal; Then, select received data with a signal pre-signal gap of L = 50ms, and calculate the background noise energy reference E. noise :

6. The Doppler log bottom tracking method based on the joint criterion of signal frequency domain energy as described in claim 5, characterized in that: In step S7, the time-domain energy ratio is calculated as follows:

7. The Doppler log bottom tracking method based on the joint criterion of signal frequency domain energy as described in claim 6, characterized in that: In step S8, the echo signals are grouped according to the echo signal x(n): y n (i)=x(n+i)i∈[0,W) Perform a Fast Fourier Transform on each group of signals to obtain Y. n (k).

8. The Doppler log bottom tracking method based on the joint criterion of signal frequency domain energy according to claim 7, characterized in that, In step S9, according to Y n (k) Calculate the frequency domain confidence factor:

9. The Doppler log bottom tracking method based on the joint criterion of signal frequency domain energy as described in claim 8, characterized in that, In step S10, the following criteria are applied: Condition 1: R e (n) ≥ 2; Condition 2: Among them, R emax For R e (n) The maximum value of the sequence, λ is the decision parameter, usually taken as 0.05; filter points that simultaneously satisfy conditions 1 and 2, and when multiple points satisfy the conditions, select one of them R. e (n) The maximum value point is the starting point of the bottom echo signal, and the echo signal length is W; if there is no point that meets the condition, the echo signal will lose its bottom. When the bottom is lost 3 times in a row, the decision parameter λ = 0.03 + 0.005t is adaptively adjusted, where t is the number of consecutive bottom losses, with a maximum of 8. When five consecutive baseline drops occur, a broadband scanning mode is simultaneously initiated, adjusting the effective frequency band to [f]. a -2Δf,f a +2Δf]; After resuming tracking, switch to adaptive tracking mode.

10. A Doppler log bottom tracking system based on a joint criterion of signal frequency domain energy, used to execute the Doppler log bottom tracking method based on a joint criterion of signal frequency domain energy as described in any one of claims 1-9, comprising: The multi-channel data acquisition module includes: a 16-bit high-precision ADC converter and a voltage-controlled gain amplifier (TVG) for receiving and amplifying echo signals; it also includes an environmental sensing unit, which includes a water temperature sensor, a salinity sensor, and an attitude sensor, for dynamically adjusting Δf. The real-time signal processing module includes: an FPGA-implemented FFT / IFFT processing unit and a DSP-implemented adaptive passband selection unit, used for pre-bandpass filtering, FFT / IFFT processing, time-domain energy ratio calculation, frequency-domain confidence factor calculation, and adaptive bandwidth selection of the echo signal. The joint criterion decision module includes: frequency domain energy confidence data stored in RAM, and logic decision and control unit implemented by DSP. It is used to extract the underwater echo signal according to the criterion conditions based on the calculation results of time domain energy ratio and frequency domain confidence factor, and to handle abnormal situations according to the set mechanism. The result output module includes an RS422 / Ethernet interface and a fault alarm indicator, which are used to output the results to the outside world.

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