Underwater topographic surveying and mapping method based on shallow water adaptive correction and multi-spectral analysis

Abstract

The invention discloses an underwater topographic surveying and mapping method based on shallow water adaptive correction and multi-spectral analysis, and the method comprises the following steps: S1, building an ultrasonic sensor model for underwater topographic surveying and mapping, and initializing the structural parameters of the ultrasonic sensor model, including an ultrasonic emission angle, a frequency range and a water depth detection module; s2, a water depth detection module is started in a shallow water area, the current water depth is measured through a sensor, and signal reflection characteristic data are recorded; s3, dynamically correcting ultrasonic emission parameters by adopting a frequency adjustment formula and an emission angle adjustment formula based on water depth and reflection characteristics; s4, ultrasonic signals of multiple frequency bands are generated and transmitted, and echo signals are received through a multi-spectrum analysis module and fused; the invention aims to solve the problems of large measurement error, low signal resolution, difficulty in eliminating multi-path effect, poor environmental adaptability and the like in the prior art, and remarkably improve the surveying and mapping precision and efficiency in shallow water areas and complex terrain environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater detection and mapping, and in particular is an underwater terrain mapping method based on shallow water adaptive correction and multi-spectrum analysis. Background Art

[0002] Underwater topographic mapping technology currently plays a vital role in scientific research, engineering construction, and ocean exploration. Acquiring high-precision topographic data is particularly important for shallow waters and complex terrain environments, crucial for activities such as waterway maintenance, environmental protection, and resource exploration. However, existing underwater topographic mapping technology still faces numerous challenges, particularly in shallow waters, where multipath effects and complex underwater environments hinder the accuracy and clarity of measurement data.

[0003] 1. Limitations of existing technologies

[0004] Most existing underwater topographic mapping systems rely on single-frequency ultrasonic transmission and reception. Single-frequency signals are susceptible to multipath interference underwater, especially in shallow waters. This effect is primarily caused by reflection and refraction of ultrasonic signals between the water column and the bottom, leading to signal overlap and path distortion, seriously compromising the accuracy of survey data. Furthermore, in complex shallow water environments, factors such as water velocity, temperature gradients, and bottom type further exacerbate signal interference, making signal processing even more difficult.

[0005] 2. Measurement error and signal ambiguity

[0006] In shallow waters, high-frequency ultrasonic signals, while offering high resolution, are highly susceptible to environmental interference, leading to signal overlap or blurring, making it difficult to accurately depict the details of the underwater terrain. On the other hand, low-frequency signals have strong penetration but low resolution, making them inadequate for detailed mapping. Therefore, achieving a balance between signal penetration and resolution in shallow waters remains a pressing technical challenge.

[0007] 3. Deficiencies of existing multipath effect processing methods

[0008] Traditional multipath processing methods typically rely on analyzing signal strength differences to identify and correct for multipath effects. However, this approach struggles to accurately characterize the impact of multipath in complex environments, particularly in areas with significant water temperature fluctuations or rapid currents. Relying solely on signal strength differences fails to fully identify time delays and path variation characteristics, resulting in unreliable measurement results. Furthermore, existing systems lack effective real-time adjustment mechanisms, making them unable to adapt to dynamically changing underwater environments, limiting the practicality of surveying and mapping systems.

[0009] 4. Limitations of Denoising Technology

[0010] Environmental noise is a major challenge in underwater mapping, especially in shallow waters. Ultrasonic signals are susceptible to noise interference due to turbulent currents, complex bottom sediments, and abundant biological activity. Traditional denoising methods are limited in their effectiveness against high-frequency noise, making it difficult to separate true echo signals from noise, thus affecting the accuracy of topographic data.

[0011] Given these challenges, a new underwater terrain mapping method is urgently needed that can dynamically adapt to changes in shallow water environments, reduce measurement errors caused by multipath effects, and simultaneously improve signal resolution and mapping accuracy. Furthermore, it can effectively fuse multispectral signals, combined with intelligent adaptive adjustment and denoising processing, to further improve the accuracy and environmental adaptability of terrain data, meeting the needs of mapping in complex waters. Summary of the Invention

[0012] In order to solve the problems raised in the above-mentioned background technology, the present invention provides an underwater terrain surveying and mapping method based on shallow water adaptive correction and multi-spectral analysis, aiming to solve the problems existing in the prior art such as large measurement errors, low signal resolution, difficulty in eliminating multipath effects, and poor environmental adaptability, and significantly improve the surveying and mapping accuracy and efficiency in shallow water areas and complex terrain environments.

[0013] To achieve the above object, the present invention provides the following technical solutions:

[0014] The underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis includes the following steps:

[0015] S1: Establish an ultrasonic sensor model for underwater terrain mapping and initialize the structural parameters of the ultrasonic sensor model, including ultrasonic emission angle, frequency range, and water depth detection module;

[0016] S2: Start the water depth detection module in the shallow water area, measure the current water depth through the sensor, and record the signal reflection characteristic data;

[0017] S3: Based on the water depth and reflection characteristics, the frequency adjustment formula and the emission angle adjustment formula are used to dynamically correct the ultrasonic emission parameters;

[0018] S4: Generates and transmits ultrasonic signals in multiple frequency bands, receives and fuses echo signals through the multi-spectrum analysis module;

[0019] S5: Use the fused signal strength data to construct a terrain model to obtain optimized shallow water area terrain data.

[0020] The frequency adjustment formula in step S3 is:

[0021]

[0022] Where: f opt : optimized ultrasonic emission frequency, in Hertz; f base : basic transmission frequency, in Hertz, ranging from 50kHz to 200kHz; d current : Current water depth, in meters; d max : The preset maximum water depth in meters.

[0023] The emission angle adjustment formula in step S3 is:

[0024]

[0025] Where: θ opt : The optimized ultrasonic emission angle, in degrees, ranging from 0° to 45°; d current : Current water depth, in meters; B: Acoustic beam width, in meters, ranging from 1 to 5 meters. The multi-spectral signal generated in step S4 includes a low-frequency band of 20 kHz to 50 kHz, a mid-frequency band of 50 kHz to 100 kHz, and a high-frequency band of 100 kHz to 200 kHz, to enhance the resolution of signal echoes from muddy or soft bottoms.

[0026] The multi-spectrum analysis module in step S4 is based on the weighted fusion formula:

[0027] I fused =w1I low +w2I mid +w3I high ;

[0028] Among them: I fused : total echo signal strength after fusion, in decibels; w1: weighting coefficient of low-frequency signal, ranging from 0 to 1; w2: weighting coefficient of mid-frequency signal, ranging from 0 to 1; w3: weighting coefficient of high-frequency signal, ranging from 0 to 1, and satisfying w1+w2+w3=1; I low : echo intensity of low-frequency signal, in decibels; I mid : echo intensity of the mid-frequency signal, in decibels; I high : The echo strength of the high-frequency signal, in decibels.

[0029] In step S5, an adaptive filtering algorithm is used to perform denoising on the fused signal data, extract effective components from the terrain signal, and enhance the signal.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Improve the accuracy of shallow water mapping

[0032] Traditional underwater mapping methods are susceptible to multipath effects and interference in shallow water areas, resulting in large errors in measurement data. This is especially true in areas with complex terrain, making it difficult to obtain high-precision terrain information. The present invention effectively reduces multipath interference in shallow water areas by introducing an intelligent shallow water adaptive correction module, combined with real-time dynamic adjustment of frequency and angle. Specifically, the frequency adjustment module dynamically adjusts the transmission frequency so that it automatically switches to a more suitable frequency range when interference is severe, thereby reducing the multipath effect. The transmission angle control module adaptively optimizes the transmission angle according to the water depth, further improving the quality of signal reception.

[0033] 2. Multi-spectrum analysis improves signal resolution

[0034] Existing surveying and mapping systems typically use single-frequency ultrasonic signals, making it difficult to obtain high-resolution echo signals from soft or muddy substrates. Furthermore, single-frequency signals have limited penetration, compromising the integrity of terrain data. This invention utilizes multi-spectral signal analysis technology to generate low-frequency, mid-frequency, and high-frequency signals and perform a weighted fusion, significantly improving adaptability to diverse substrates and signal clarity. The low-frequency signal provides penetration, the mid-frequency signal balances coverage, and the high-frequency signal ensures detailed resolution, ultimately generating a high-quality terrain model.

[0035] 3. Enhance measurement accuracy in complex environments

[0036] The multipath effect is particularly pronounced in shallow water and complex terrain. Traditional methods typically rely solely on signal strength differences for analysis, failing to accurately identify the delay characteristics along different paths, resulting in blurred or misaligned signals. The present invention introduces time delay feature analysis and multi-parameter fusion modeling technology. By collecting and analyzing various environmental parameters such as signal time delay, water flow velocity, temperature gradient, and terrain slope, the present invention can more accurately identify and correct the multipath effect. The multi-parameter fusion model uses machine learning algorithms, such as neural networks and support vector machines, to comprehensively describe the impact of the multipath effect and dynamically correct the signal path to ensure data accuracy.

[0037] 4. Real-time dynamic adjustment and efficient path correction

[0038] Traditional surveying and mapping methods cannot achieve real-time adjustment and correction, often requiring complex data post-processing after measurement, which increases operational complexity and time costs. The dynamic path correction module of the present invention can adjust the path length of the ultrasonic signal in real time based on the output of the multi-parameter fusion model, reducing measurement errors. This real-time adjustment mechanism improves the system's response speed and measurement efficiency, making the surveying and mapping process more automated and efficient.

[0039] 5. Strong environmental adaptability, suitable for a variety of complex waters

[0040] Existing ultrasonic mapping systems suffer from poor measurement accuracy in environments with large temperature fluctuations, fast flow rates, or complex bottom conditions, making it difficult to provide consistent topographic data. This new system uses high-precision sensors to monitor water velocity and temperature gradients, incorporating terrain slope information for modeling. This system is suitable for shallow waters, areas with high flow rates, or environments with complex terrain. This adaptability ensures the stability and accuracy of the mapping data.

[0041] 6. Adaptive filtering and denoising to improve signal quality

[0042] Environmental noise and high-frequency hum are common problems in underwater mapping. Existing denoising techniques struggle to effectively process complex signals, impacting the accuracy of terrain data. This paper uses an adaptive filtering algorithm to denoise multi-spectral signals. It automatically adjusts denoising parameters based on the signal's waveform characteristics and frequency distribution, effectively separating true signals from noise, extracting the effective signal, and generating an accurate terrain model.

[0043] 7. Multi-level optimization significantly improves surveying and mapping accuracy and efficiency

[0044] The multi-level optimization design of the present invention, including intelligent frequency and angle adaptive adjustment, multi-spectral signal fusion and multipath effect correction technology, enables the surveying and mapping system to automatically adapt to different environments and maintain high accuracy, reducing the need for manual intervention and post-processing, and improving overall surveying and mapping efficiency.

[0045] In general, the present invention solves the problems of multipath effect and signal ambiguity in shallow water areas by introducing intelligent adaptive adjustment and multi-spectrum analysis technology, significantly improves the resolution and accuracy of terrain mapping, has strong environmental adaptability, and is suitable for various complex underwater terrains. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0047] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0048] See also Figure 1The present invention relates to a method for underwater topography mapping based on intelligent shallow-water adaptive correction and multi-spectral analysis technology. By dynamically adjusting the ultrasonic frequency and emission angle, the method effectively reduces measurement errors in shallow waters and improves the resolution of echo signals from soft or muddy bottoms through multi-band signal fusion, thereby enhancing the accuracy and clarity of underwater topography.

[0049] S1 ultrasonic sensor model establishment and structural parameter initialization

[0050] Before shallow water terrain mapping begins, a model of the ultrasonic sensor must be built. This model includes the following modules: frequency adjustment module, emission angle control module, water depth detection module, and echo reception module.

[0051] 1.1 Frequency Regulation Module

[0052] In shallow water environments, high-frequency ultrasound has high resolution, but it is more susceptible to interference from multipath effects, resulting in signal overlap and measurement errors. Therefore, the frequency adjustment module is set to a low to medium frequency base frequency (f base ), typically 50-200kHz, to reduce multipath effects in shallow water environments. This module will dynamically adjust the frequency based on the current water depth in subsequent steps.

[0053] 1.2 Emission Angle Control Module

[0054] The emission angle control module is initially set to a basic emission angle of 15° (θ init ), this angle can be optimized according to the water depth in subsequent steps to reduce interference in the signal reflection path in shallow water.

[0055] 1.3 Water Depth Detection Module

[0056] The water depth detection module measures the current water depth and provides a basis for subsequent frequency and angle adjustments. By emitting ultrasonic signals and recording their reflection paths, the water depth in shallow water areas can be determined in real time, as well as the degree of environmental interference.

[0057] S2 starts the water depth detection module and records the signal reflection characteristics

[0058] Water depth has a significant impact on the reflection characteristics of ultrasonic signals. The multipath effect in shallow water areas is more intense, resulting in complex signal paths and significant reflection superposition. Therefore, the present invention starts the water depth detection module before measurement and records the current water depth (d current ) and reflective properties.

[0059] 2.1 Water depth detection

[0060] Water depth detection is achieved by transmitting and receiving low-frequency signals (such as 50kHz), and recording the intensity of the echo signal (Ir ) and the reflection angle (θ r Shallow waters can easily cause signals to reflect along multiple paths, creating a multipath superposition phenomenon that reduces signal clarity and measurement accuracy. Using echo strength and reflection angle, the system can assess the degree of multipath interference, providing data support for subsequent steps.

[0061] 2.2 Signal reflection characteristics analysis

[0062] The present invention identifies the multipath effect by real-time monitoring of the intensity and angle of the echo signal. By recording the initial echo intensity (direct echo) and the intensity of subsequent reflections (multipath echo), the reflection interference coefficient α can be calculated, which is defined as

[0063]

[0064] Among them, I multi Indicates the total strength of the multipath echo signal, I direct Indicates the strength of the direct echo signal. When the α value is high, it means that the multipath effect is strong, and frequency and angle adjustments need to be made in subsequent steps to reduce interference.

[0065] S3 dynamically adjusts the transmission frequency and angle

[0066] According to the water depth and reflection interference coefficient α, the ultrasonic transmission frequency and angle are dynamically adjusted to optimize the signal receiving path.

[0067] 3.1 Frequency Adjustment

[0068] In shallow water, higher frequency ultrasonic signals are more attenuated in water and are susceptible to interference. To this end, the present invention uses the following frequency adjustment formula to optimize the frequency:

[0069]

[0070] Where: f opt : The optimal frequency after adjustment, in Hertz (Hz). By lowering the frequency, the path superposition interference caused by high frequency is reduced. base : Basic frequency, the value range is 50 to 200kHz. current : Current water depth, in meters (m). max The maximum water depth, typically set at 20 meters, limits the frequency variation. This formula reduces the frequency as the water depth decreases, preventing reflection interference from high-frequency signals in shallow water. For deeper areas, the frequency is gradually increased to improve measurement resolution.

[0071] 3.2 Angle adjustment

[0072] The transmission angle control directly affects the signal path length and reflection intensity. By optimizing the transmission angle, multipath superposition in shallow water can be reduced. The present invention adopts the following angle adjustment formula:

[0073]

[0074] Where: θ opt : The optimized emission angle, in degrees, is usually controlled between 0° and 45°. current : Current water depth, in meters (m). B: Beam width, in meters (m), typically 1 to 5 meters. The physical significance of this formula lies in the fact that a larger transmission angle in shallow water reduces signal scattering interference from the underwater bottom, preventing echo overlap while increasing the intensity of the direct echo and improving measurement accuracy.

[0075] S4 generates multi-spectrum signals and performs multi-spectrum analysis

[0076] In order to improve the resolution in different bottom layers, the present invention introduces multi-spectral analysis technology to fuse the shallow water bottom layer signals by generating multi-band signals of low frequency (20-50kHz), medium frequency (50-100kHz), and high frequency (100-200kHz).

[0077] 4.1 Multi-band Signal Generation

[0078] The present invention uses a multi-band signal generation module to generate ultrasonic signals of different frequencies, each frequency band being adapted to different substrate characteristics. Low-frequency signals have strong penetration and are suitable for hard substrates, while high-frequency signals have high resolution and are suitable for soft or muddy substrates.

[0079] 4.2 Spectrum Fusion

[0080] Multi-band signals are processed using the following weighted fusion formula to enhance signal clarity:

[0081] I fused =w1I low +w2I mid +w3I high ;

[0082] Among them: I fused : The strength of the echo signal after fusion. w1,w2,w3: The weighting coefficients of the low, medium and high frequency band signals, ranging from 0 to 1, and w1+w2+w3=1. low ,I mid ,I high : The echo strength of low, medium, and high frequency signals. The weighting coefficients are adaptively adjusted based on the substrate type and signal clarity to ensure the appropriate frequency band combination for optimal signal fusion.

[0083] S5 signal denoising and terrain data generation

[0084] Due to the complexity of the shallow water environment, the present invention uses an adaptive filtering algorithm to denoise the multi-spectrum signal, filter out high-frequency noise and enhance the extraction of effective signals.

[0085] 5.1 Adaptive Filtering Denoising The adaptive filtering algorithm separates the noise signal by analyzing the waveform characteristics and frequency distribution of the signal, thereby effectively extracting the true echo signal. The denoised signal data is used to generate a shallow water terrain model.

[0086] 5.2 Terrain Data Generation

[0087] Based on the filtered signal strength, a 3D terrain model of shallow water areas is constructed. This model accurately reflects the underwater topography and is particularly suitable for high-precision mapping of complex, shallow water areas.

[0088] The present invention significantly improves the resolution and accuracy of shallow water area topography mapping through intelligent frequency and angle adaptive adjustment, combined with multi-spectral analysis and adaptive filtering algorithms.

[0089] Furthermore, the calculation of the reflection interference coefficient α depends on the intensity difference of the echo signal. In actual underwater multipath reflection, not only intensity is involved, but also different time delays or path differences may occur due to water flow, temperature difference and terrain. Simply relying on intensity may not accurately identify complex multipath phenomena.

[0090] Multipath can cause echo signals to be blurred or misplaced, affecting the resolution and accuracy of terrain data, especially in shallow water areas with large terrain undulations.

[0091] Therefore, when analyzing the signal reflection characteristics, the following steps are also included:

[0092] S1: Use the multi-parameter signal acquisition module to simultaneously obtain the intensity I of the ultrasonic echo signal r , time delay Δt, water flow velocity v w , temperature gradient ΔT and terrain slope θ s ;

[0093] S2: Calculate the delay characteristic Δt of each echo signal through the time delay analysis module, and distinguish the direct path signal from the multipath reflection signal based on the different path delay characteristics;

[0094] S3: Based on the multi-parameter information obtained in steps S1 and S2, a multi-parameter fusion model α is constructed. multi =f(I r ,Δt,v w ,ΔT,θ s ), used to describe the interference degree of multipath effect;

[0095] S4: Input the output of the multi-parameter fusion model into the dynamic path correction module to calculate the multi-path deviation Δd multi And correct the echo signal path length d corrected =d measured -Δd multi ;

[0096] S5: Generate corrected underwater terrain data to ensure that the error caused by multipath effect is reduced and the resolution and accuracy of the terrain data are improved.

[0097] Furthermore, the time delay Δt is calculated by the following formula:

[0098]

[0099] Where d is the total path length of signal propagation, and c is the speed of sound. The speed of sound c is affected by water temperature T and salinity S and is calculated according to the following formula:

[0100] c=1449.2+4.6T-0.055T 2 +0.00029T 3 +(1.34-0.01T)(S-35)+0.016z;

[0101] Among them, T is water temperature, S is salinity, and z is water depth.

[0102] Among them, the multi-parameter fusion model α multi Obtained through machine learning model training, including neural networks, support vector machines, or multivariate regression analysis.

[0103] The dynamic path correction module is based on the multipath deviation Δd multi (The path deviation caused by the multipath effect is calculated according to the model) Calculate the corrected path length d corrected ,in:

[0104] d corrected =d measured -Δd multi ;

[0105] Among them, d measured is the initial measurement path length.

[0106] The water flow velocity v w and temperature gradient ΔT are monitored in real time by high-precision sensors to identify the impact of the water environment on the sound wave path.

[0107] The terrain slope θ s Terrain sensor measurements are used to optimize the identification and modeling of multipath effects.

[0108] This method is suitable for shallow water areas and waters with large terrain undulations, so as to improve the resolution and accuracy of terrain data in complex environments.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis, characterized in that: The following steps are involved: S1: Establish an ultrasonic sensor model for underwater terrain mapping and initialize the structural parameters of the ultrasonic sensor model, including ultrasonic emission angle, frequency range, and water depth detection module; S2: Start the water depth detection module in the shallow water area, measure the current water depth through the sensor, and record the signal reflection characteristic data; S3: Based on the water depth and reflection characteristics, the frequency adjustment formula and the emission angle adjustment formula are used to dynamically correct the ultrasonic emission parameters; S4: Generates and transmits ultrasonic signals in multiple frequency bands, receives and fuses echo signals through the multi-spectrum analysis module; S5: Use the fused signal strength data to construct a terrain model to obtain optimized shallow water area terrain data.

2. The underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis according to claim 1 is characterized in that: The frequency adjustment formula in step S3 is: Where: f opt : optimized ultrasonic emission frequency, in Hertz; f base : basic transmission frequency, in Hertz, ranging from 50kHz to 200kHz; d current : Current water depth, in meters; d max : The preset maximum water depth in meters.

3. The underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis according to claim 1 is characterized in that: The emission angle adjustment formula in step S3 is: Where: θ opt : The optimized ultrasonic emission angle, in degrees, ranging from 0° to 45°; d current : Current water depth, in meters; B: Width of the sound beam, in meters, ranging from 1m to 5m.

4. The underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis according to claim 1 is characterized in that: The multi-spectral signal generated in step S4 includes a low frequency band: 20kHz-50kHz, a mid-frequency band: 50kHz-100kHz, and a high frequency band: 100kHz-200kHz, so as to enhance the signal echo resolution of muddy or soft bottoms.

5. The underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis according to claim 4 is characterized in that: The multi-spectrum analysis module in step S4 is based on the weighted fusion formula: I fused =w1I low +w2I mid +w3I high ; Among them: I fused : total echo signal strength after fusion, in decibels; w1: weighting coefficient of low-frequency signal, ranging from 0 to 1; w2: weighting coefficient of mid-frequency signal, ranging from 0 to 1; w3: weighting coefficient of high-frequency signal, ranging from 0 to 1, and satisfying w1+w2+w3=1; I low : echo intensity of low-frequency signal, in decibels; I mid : echo intensity of the mid-frequency signal, in decibels; I high : The echo strength of the high-frequency signal, in decibels.

6. The underwater terrain mapping method based on shallow water adaptive correction and multi-spectral analysis according to claim 1, characterized in that: In step S5, an adaptive filtering algorithm is used to perform denoising on the fused signal data, extract effective components from the terrain signal, and enhance the signal.

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