Robust ranging method, device and medium for underwater multispectral full-waveform lidar
By acquiring and processing multi-wavelength echo data in an underwater multispectral lidar, a target echo model is constructed, and the slope characteristics of the rising edge of the interface echo are utilized to solve the ranging instability problem caused by interface echo peak saturation and trailing distortion, thus achieving robust cross-wavelength ranging correction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU INST FOR ADVANCED STUDY UCAS
- Filing Date
- 2026-04-02
- Publication Date
- 2026-08-04
AI Technical Summary
In underwater multispectral lidar, interface echo peak saturation clipping and trailing distortion lead to unreliable peak points, poor ranging stability and consistency, and inconsistent cross-wavelength ranging results that are difficult to unify.
By repeatedly acquiring multiple sets of multi-wavelength echo data, Gaussian smoothing filtering and target echo model construction are performed. Peak values are detected by sliding window normalized cross-correlation matching. Combined with the slope characteristics of the rising edge of the interface echo, robust time features are extracted and corrected.
Under the conditions of interface echo peak saturation and tail distortion, the reliability and stability of ranging are improved, and consistency correction of cross-wavelength ranging results is achieved.
Smart Images
Figure CN121956017B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of multispectral lidar signal processing and ranging calibration technology, specifically to a robust ranging method, device, and medium for underwater multispectral full-waveform lidar. Background Technology
[0002] Multispectral lidar acquires complete waveforms of echoes over time at multiple wavelengths, simultaneously obtaining target distance information and wavelength-dependent reflection / scattering differences. It is widely used in underwater target detection, shallow-sea depth sounding, environmental monitoring, and multi-parameter inversion. Compared to pulse ranging, which only outputs single-point distance or a few features, full waveform data can characterize the morphology and structure of the echoes, improving ranging accuracy and robustness in complex media.
[0003] However, in underwater multispectral lidar scenarios, water volume scattering, backscattering of suspended particles, and interface-related multipath propagation introduce multiple echo components with different propagation paths. These late-arriving components concentrate towards the side with the larger time delay in the time domain and superimpose with the attenuation segment of the main echo, causing the waveform's falling edge to decay more slowly, deflect to the right, and develop a long tail. This, in turn, leads to a systematic shift in time positioning based on the peak point. Simultaneously, interface echoes in practical systems often exhibit a clipped saturation plateau due to receiver link saturation or analog-to-digital converter range limitations, distorting the peak shape and causing unreliable detection of the peak moment. If the peak point is still used as the ranging reference, the ranging stability and consistency will be significantly reduced.
[0004] Furthermore, multispectral systems exhibit differences in transmit energy, receive response, gain, and system delay across different wavelength channels, potentially leading to inconsistent distance measurements at the same actual distance. Additionally, multiple acquisitions at the same wavelength can introduce random fluctuations and system bias. Common existing techniques include Gaussian or multi-Gaussian fitting of single waveforms, compensation for saturated waveforms, or threshold intersection location. However, under conditions of significant tailing and interface echo saturation, peaks become unusable, falling edge distortion is severe, and fitting stability and cross-wavelength consistency remain difficult to guarantee.
[0005] Therefore, a robust ranging and calibration method is needed that can stably extract time features under conditions of peak saturation clipping and post-peak tail distortion of the interface echo, and achieve unified correction for both same-wavelength and cross-wavelength measurements by combining known absolute distances. Summary of the Invention
[0006] The purpose of this invention is to provide a robust ranging method, device, and medium for underwater multispectral full-waveform lidar, in order to solve problems in the prior art such as unusable peak points due to interface echo saturation clipping, peak point time shift due to rightward tailing, and inconsistent ranging results for different wavelength channels that are difficult to calibrate uniformly.
[0007] To achieve the above objectives, the present invention provides the following technical solution:
[0008] A robust ranging method for underwater multispectral full-waveform lidar includes the following steps:
[0009] S1, Data Acquisition: Repeatedly acquire multiple sets of multi-wavelength echo data within a preset wavelength range and store them in a storage medium for subsequent reading and processing, and acquire calibration waveforms at at least one known distance;
[0010] S2, Waveform preprocessing: Gaussian smoothing filter is applied to the original echo to suppress noise and obtain a smooth echo;
[0011] S3, Target echo model construction: Extract the target echo segment within the preset time window from the calibration waveform, perform peak alignment and amplitude normalization on each echo segment and calculate the average to generate a shared target echo reference model;
[0012] S4, Target echo detection: Template matching is performed using sliding window normalized cross-correlation to obtain a similarity sequence. The echo segments corresponding to the highest preset number of local peaks are selected as candidates. After removing noise by amplitude significance judgment, the latest peak is selected as the target echo arrival point in chronological order.
[0013] S5, Time Feature Extraction: Locate the peak point within the target echo interval and calculate the slope on the rising edge to extract the maximum positive slope point. Perform saturation plateau detection on the interface echo and extract its maximum positive slope point on the rising edge.
[0014] S6, Peak Estimation and Distance Calculation: The sampling offset between the target echo peak point and the maximum slope point is used as the system offset. The equivalent peak point of the interface echo is then calculated by back-calculating the sampling point difference between the interface equivalent peak point and the target peak point, combined with the sampling interval. The target distance is then calculated according to the time-of-flight ranging relationship.
[0015] Further, S1 includes the following steps:
[0016] Set the wavelength set according to the preset wavelength interval within the preset wavelength range:
[0017] ,
[0018] Where Λ is the set of wavelengths, λ i Let N be the i-th wavelength, and N be the number of wavelengths.
[0019] For each wavelength λ i Echo sequences of a preset number of groups are acquired at preset sampling intervals and stored in a three-dimensional structure of wavelength index, group number index, and time sampling point index, and are kept at at least one known calibration distance d. calUnder certain conditions, calibration waveforms are collected for template construction.
[0020] Further, S2 includes the following steps:
[0021] S2.1, for the original echo x i,m [k] Perform Gaussian smoothing filtering to obtain the smoothed echo y i,m [k], where x i,m [k] represents the original echo amplitude of the k-th sampling point under the condition that the wavelength index is i and the group number index is m; y i,m [k] represents the smoothed echo amplitude of the kth sampling point under the condition that the wavelength index is i and the group number index is m;
[0022] S2.2, smooth the echo y i,m [k] serves as the input for subsequent sliding window cross-correlation matching, peak localization, and slope calculation.
[0023] Further, S3 includes the following steps:
[0024] S3.1, at the known calibration distance d cal Under these conditions, for each wavelength λ i Extract the target echo band within a fixed time window of length W;
[0025] S3.2, denoted as k as the peak index of the i-th target echo band. p,i And shift each segment so that its peak index is unified with the reference peak index k. p ;
[0026] S3.3, average the normalized echo bands of the entire wavelength to generate a shared target echo reference template r[g], where g is the index of the sampling point within the target echo reference template.
[0027] Further, S4 includes the following steps:
[0028] S4.1, for smooth echo y i,m [k] is subjected to sliding window normalized cross-correlation calculation with the shared target echo reference template r[g] to obtain the similarity sequence c[τ], where τ is the sliding window start position index;
[0029] Select the P echo bands corresponding to the highest local peaks in the similarity sequence as a candidate set;
[0030] S4.2 Calculate the noise baseline mean μ within the preset noise baseline interval. n Standard deviation of noise baseline σ n And use the amplitude determination threshold T=μ n +ασ n The significance of the amplitude is determined, where α is a preset coefficient;
[0031] S4.3 Sort the candidate echo set that has passed the judgment in chronological order, and select the peak with the latest arrival time as the final target echo arrival point.
[0032] Further, S5 includes the following steps:
[0033] S5.1, Calculate the discrete slope sequence within the rising edge interval of the target echo according to the sampling interval Δt:
[0034] ,
[0035] Among them, s i,m [k] represents the discrete slope of the k-th sampling point given wavelength index i and group number index m. i,m [k+1], y i,m [k-1] represents the smoothed echo, k+1 represents the index of the next sampling point after the k-th sampling point, and k-1 represents the index of the previous sampling point after the k-th sampling point. a ,k b [ ] represents the index interval of the rising edge of the target echo;
[0036] The point with the maximum positive slope is taken as the robust time delay characteristic point of the target echo;
[0037] S5.2, perform saturation plateau detection on the interface echo, when L consecutive sampling points satisfy y[k]≥V sat When a point is identified as a saturation plateau, L is a preset threshold for the number of consecutive points, and V... sat This is the system saturation amplitude threshold;
[0038] Calculate the discrete slope s within the rising edge interval before the saturation plateau. i,m [k], extracts the point with the maximum positive slope on the interface.
[0039] Further, S6 includes the following steps:
[0040] S6.1 Calculate the sampling offset Δk between the target echo peak point and the target echo maximum positive slope point, and use the sampling offset Δk to back-calculate the interface echo equivalent peak point;
[0041] S6.2, based on the sampling point difference Δk between the target echo peak point and the equivalent peak point of the interface echo. T Converting time difference The distance is calculated based on the time-of-flight ranging relationship, where Δt is the sampling interval.
[0042] The present invention also provides a robust ranging device for underwater multispectral full-waveform lidar, comprising one or more processors and a memory, wherein the memory stores instructions, and when the instructions are executed by the processor, the robust ranging method for underwater multispectral full-waveform lidar as described above is implemented.
[0043] The present invention also provides a readable storage medium having a program stored thereon, which, when executed by a processor, implements the robust ranging method for underwater multispectral full-waveform lidar as described above.
[0044] Compared with the prior art, the present invention has the following beneficial effects:
[0045] (1) Under the condition of saturated clipping at the peak of the interface echo, the present invention does not rely on the peak point of the interface echo, but rather uses the stable characteristics of the rising edge of the interface echo to backtrack the peak time, thereby improving the reliability of the interface echo time positioning.
[0046] (2) To address right-skew distortion caused by trailing, this invention suppresses false detections caused by late-arrival scattering and long-tail superposition by constructing a target echo reference and using dual-condition detection, thereby improving the stability of target echo localization.
[0047] (3) The method of the present invention can be implemented in software in the multispectral lidar ranging and calibration process, which is convenient for engineering deployment and provides a new technical path and application prospect for the accurate ranging and calibration of underwater multispectral lidar. Attached Figure Description
[0048] Figure 1 This is a flowchart of a robust ranging method for underwater multispectral full-waveform lidar according to the present invention.
[0049] Figure 2 This is a schematic diagram of the intensity-time real waveform for multiple wavelengths, showing 14 wavelengths.
[0050] Figure 3 A comparative diagram showing the distance estimation of 200 sets of repeated measurement data using different ranging methods when the actual distance is 20 m, wherein: (a) Gaussian fitting method; (b) exponential Gaussian fitting method; (c) linear fitting method; (d) the robust ranging method of underwater multispectral lidar of the present invention.
[0051] Figure 4 This diagram illustrates the comparison of the average absolute distance deviation of a robust underwater multispectral full-waveform lidar ranging method of the present invention with Gaussian fitting, exponential Gaussian fitting, and linear fitting methods, based on 200 sets of repeated measurements at actual distances of 20m, 22m, 24m, 26m, 28m, and 30m.
[0052] Figure 5This is a schematic diagram of a robust underwater multispectral full-waveform lidar ranging device according to the present invention. Detailed Implementation
[0053] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative effort are all within the protection scope of the present invention.
[0054] like Figure 1 As shown, a robust ranging method for underwater multispectral full-waveform lidar is achieved through the following steps:
[0055] S1, Data Acquisition
[0056] In an underwater multispectral lidar system, the transmitter operates within a preset wavelength range (450nm-580nm) at preset wavelength intervals (10nm), while the receiver repeatedly samples and stores the echoes. Simultaneously, calibration waveforms are acquired under at least one known distance condition (20m), such as... Figure 2 As shown.
[0057] The wavelength set is represented as:
[0058] ,
[0059] Where Λ represents the set of wavelengths involved in the measurement; λ i This represents i working wavelengths; N represents the number of wavelengths.
[0060] For each wavelength λ i M groups (200 groups) of echo sequences were acquired and stored in a three-dimensional structure based on wavelength index, group number index, and time sampling point index. The original echo can be denoted as x. i,m [k], where x i,m [k] represents the original echo amplitude of the kth sampling point under the condition that the wavelength index is i and the group number index is m, with a sampling interval of Δt (0.1ns).
[0061] The known distance calibration waveform can be obtained by placing a planar calibration target within the system's field of view.
[0062] S2, Waveform Preprocessing
[0063] To suppress noise and improve the stability of template matching and temporal feature extraction, this embodiment performs Gaussian smoothing filtering on the original echo to obtain a smoothed echo. Smoothed echo y i,m [k] serves as the input for subsequent sliding window normalized cross-correlation matching, peak localization, and slope calculation to reduce the impact of noise on temporal feature extraction, where yi,m [k] represents the smoothed echo amplitude of the kth sampling point under the condition that the wavelength index is i and the group number index is m.
[0064] S3, Target echo model construction: In the calibration waveform, the target echo segment within the preset time window is extracted, and the peak value of each echo segment is aligned and the amplitude is normalized and averaged to generate a shared target echo reference model.
[0065] S3.1, Target echo band interception: Under at least one known distance condition (20m), for each wavelength λ i The calibration waveform is used to extract a target echo segment of length W from a preset time window, denoted as:
[0066] ,
[0067] Among them, u i [k] represents the wavelength λ. i The corresponding echo band to be captured; W is the capture window length.
[0068] S3.2, Peak Alignment:
[0069] First, find the peak index within each echo band:
[0070] ,
[0071] Where, k p,i wavelength λ i The echo band peak position index, k represents the kth sampling point of the target echo band, and argmax represents the independent variable that makes the objective function reach its maximum value.
[0072] Align each echo band to a unified peak index k based on its peak value. p The aligned echo band can be denoted as .
[0073] S3.3, Amplitude Normalization and Averaging: To eliminate energy differences between different wavelengths or different sampling times, amplitude normalization is performed after alignment.
[0074] ,
[0075] in, This represents the normalized echo band; max indicates taking the maximum value of the objective function, i.e., the peak amplitude of this echo band.
[0076] Averaging across the normalized echo bands across the entire wavelength range yields a shared target echo reference template:
[0077] ,
[0078] Where r[g] is the shared target echo reference model, and N represents the number of wavelengths.
[0079] S4, Target Echo Detection
[0080] In this embodiment, a sliding window normalized cross-correlation is used to perform template matching to obtain a similarity sequence. The echo bands corresponding to the highest preset number of local peaks are selected as candidates. After removing noise by determining the amplitude significance, the latest peak is selected as the target echo arrival point in chronological order.
[0081] S4.1 Sliding window normalized cross-correlation and candidate peak selection:
[0082] For the smoothed echo y i,m Calculate the similarity sequence between [k] and the shared target echo reference template r[g]:
[0083] ,
[0084] Among them, c i,m [τ] represents the similarity sequence at wavelength i and group number m; τ is the index of the sliding window starting position. y is the mean echo value within the sliding window. i,m [k] represents the smoothed echo amplitude at the k-th sampling point, given wavelength index i and group number index m, where W is the truncation window length. The mean of the target echo reference model.
[0085] In c i,m Local maxima are detected in [τ], and the P local peaks with the highest similarity scores are selected as the candidate set, where P is a preset number.
[0086] S4.2, Determination of the significance of amplitude:
[0087] Within the preset noise baseline range Ω n Within, calculate the noise baseline mean μ n Standard deviation of noise baseline σ n :
[0088] ,
[0089] Among them, Ω n For the set of noise baseline sampling points, It is the number of its elements.
[0090] Amplitude determination threshold T is used:
[0091]
[0092] Where α is a preset coefficient.
[0093] For each candidate echo band, calculate its peak amplitude A. j , when A j If the value is greater than or equal to T, the candidate is determined to be a valid target echo candidate; otherwise, it is discarded as a noise candidate.
[0094] S4.3, Destination Selection:
[0095] The candidates that pass the evaluation are sorted in chronological order, and the candidate with the latest arrival time is selected as the final target echo arrival point. The latest arrival time can be calculated using the peak index within the window.
[0096] S5, Temporal Feature Extraction
[0097] Within the target echo range, this embodiment simultaneously extracts the target echo peak point and the point with the maximum positive slope on the rising edge; in case the interface echo may be saturated, a saturation plateau is detected first, and then the point with the maximum positive slope is extracted on the rising edge before the saturation plateau.
[0098] S5.1, Target echo peak point location:
[0099] Within the final target echo range, locate the peak index:
[0100] ,
[0101] Among them, Ω t k represents the set of sampling points for the target echo search interval; t,p is the index of the target echo peak point, W is the length of the truncation window, and argmax represents the independent variable that makes the objective function reach its maximum value.
[0102] S5.2, the point of maximum positive slope on the rising edge of the target echo:
[0103] Calculate the discrete slope sequence within the rising edge interval of the target echo according to the sampling interval Δt:
[0104] ,
[0105] Among them, s i,m [k] represents the discrete slope of the k-th sampling point given wavelength index i and group number index m; [k] a ,k b [k] represents the index interval of the rising edge of the target echo. a k b These are the start and end indices of the rising edge interval, respectively.
[0106] Take the point with the maximum positive slope as the target echo time feature point:
[0107] ,
[0108] Where, kt,s Index the point with the maximum positive slope on the rising edge of the target echo.
[0109] S5.3, Interface echo saturation plateau detection and maximum slope point:
[0110] For the interface echo range Ω I Saturation plateau detection: When there exists an index k0 such that L consecutive sampling points satisfy:
[0111] ,
[0112] Then determine [k0,k 0+L-1 [ ] represents a saturated platform.
[0113] Among them, V sat L is the system saturation amplitude threshold; L is the preset continuous point threshold.
[0114] The rising edge interval [k] before the saturation plateau I,a ,k I,b The slope sequence is also calculated within [the same area]. And extract the point with the maximum positive slope on the interface:
[0115] ,
[0116] Where, k I,s This is the index of the point with the maximum positive slope on the rising edge of the interface echo.
[0117] S6, Peak Estimation and Distance Calculation
[0118] To maintain a consistent ranging benchmark across wavelengths even when interface echo saturation prevents direct and reliable positioning of the peak, this embodiment uses the sampling offset between the target echo peak point and the maximum slope point as the system offset to back-calculate the equivalent peak point of the interface echo. It also combines the sampling interval to convert the sampling point difference between the equivalent peak point of the interface and the target peak point into a time difference, and calculates the target distance according to the time-of-flight ranging relationship.
[0119] S6.1, System offset estimation and interface equivalent peak back-conversion:
[0120] Calculate the sampling offset between the peak point of the target echo and the point with the maximum positive slope of the target echo:
[0121] ,
[0122] Where △k is the sampling offset (in units of sampling points), which reflects the relative positional relationship between the slope feature point and the peak point under the same system conditions.
[0123] Using this offset, the point of maximum slope of the interface echo is used to deduce the equivalent peak point of the interface echo:
[0124] ,
[0125] in, Index of the equivalent peak points of the interface echo.
[0126] S6.2, Time Difference Conversion:
[0127] Calculate the sampling point difference between the target echo peak point and the equivalent peak point of the interface echo:
[0128] ,
[0129] Where, △k T This represents the difference in sampling points between two feature points.
[0130] Converted to time difference:
[0131] ,
[0132] Where △T is the time difference between the target echo and the interface echo; △t is the sampling interval.
[0133] S6.3, Time-of-Flight Ranging:
[0134] The target distance can be calculated based on the time-of-flight ranging relationship:
[0135] ,
[0136] Where D is the one-way distance from the interface to the target, and v is the equivalent propagation speed of light in the medium.
[0137] In underwater scenarios, if propagation primarily occurs within the water medium, then the following can be considered:
[0138] ,
[0139] Where c0 is the speed of light in vacuum, n w Let be the refractive index of water. In cross-wavelength ranging, the refractive index can be further written as n. w (λ i This allows us to obtain the distances at various wavelengths.
[0140] ,
[0141] Among them, D i Indicates wavelength λ i The distance calculated below; △T i Indicates wavelength λ i The time difference obtained.
[0142] Using the above method, consistency between target echo detection and time reference extraction can be maintained under different wavelengths, noise levels, and interface saturation conditions, thereby achieving robust ranging. Figure 3 As shown, the distance measurement results of Gaussian fitting, exponential Gaussian fitting, linear fitting methods, and the method of this invention are presented at a real distance of 20m, with 200 sets of repeated measurements at wavelengths of 450-580nm (10nm intervals). Figure 4 As shown, the average absolute distance deviation of Gaussian fitting, exponential Gaussian fitting, linear fitting methods and the method of the present invention is illustrated in 200 sets of repeated measurements at different real distances of 20m, 22m, 24m, 26m, 28m, and 30m, at wavelengths of 450-580nm (10nm intervals).
[0143] The specific quantitative evaluation results are shown in Table 1, which compares the performance of Gaussian fitting, exponential Gaussian fitting, linear fitting methods, and the method of this invention. The average absolute distance deviation of the method of this invention is 0.1792 m, which is a reduction of 0.3039 m, 0.2572 m, and 0.3184 m compared to Gaussian fitting, exponential Gaussian fitting, and linear fitting methods, respectively. The average relative distance deviation of the method of this invention is 0.0090, which is a reduction of 0.0152 m, 0.0128 m, and 0.0159 m compared to Gaussian fitting, exponential Gaussian fitting, and linear fitting methods, respectively. The root mean square error of the method of this invention is 0.1806 m, which is a reduction of 0.3035 m, 0.2574 m, and 0.3182 m compared to Gaussian fitting, exponential Gaussian fitting, and linear fitting methods, respectively.
[0144] Table 1. Comparison of ranging errors of Gaussian fitting, exponential Gaussian fitting, linear fitting, and the method of this invention.
[0145]
[0146] See Figure 5 The present invention provides a robust ranging device for underwater multispectral full-waveform lidar, comprising one or more processors for implementing a robust ranging method for underwater multispectral full-waveform lidar as described in the above embodiments.
[0147] The robust ranging device for underwater multispectral full-waveform lidar of this invention can be applied to any device with data processing capabilities, such as a computer. The device can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 5The diagram shown is a hardware structure diagram of any device with data processing capabilities, including the robust ranging device of the underwater multispectral full-waveform lidar of the present invention. (Except for...) Figure 5 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing-capable manufacturing process in which the device is located in the embodiment may also include other hardware depending on the actual function of the data processing-capable device, which will not be described in detail here.
[0148] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.
[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0150] This invention also provides a readable storage medium storing a program that, when executed by a processor, implements a robust underwater multispectral full-waveform lidar ranging method described in the above embodiments.
[0151] The readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the readable storage medium can include both internal storage units of any data processing device and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.
[0152] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A robust ranging method for underwater multispectral full-waveform lidar, characterized in that, Includes the following steps: S1, Data Acquisition: Repeatedly acquire multiple sets of multi-wavelength echo data within a preset wavelength range and store them in a storage medium for subsequent reading and processing, and acquire calibration waveforms at at least one known distance; S2, Waveform preprocessing: Gaussian smoothing filter is applied to the original echo to suppress noise and obtain a smooth echo; S3, Target echo model construction: Extract the target echo segment within the preset time window from the calibration waveform, perform peak alignment and amplitude normalization on each echo segment and calculate the average to generate a shared target echo reference model; S4, Target echo detection: Template matching is performed using sliding window normalized cross-correlation to obtain a similarity sequence. The echo segments corresponding to the highest preset number of local peaks are selected as candidates. After removing noise by amplitude significance judgment, the latest peak is selected as the target echo arrival point in chronological order. S5, Time Feature Extraction: Locate the peak point within the target echo interval and calculate the slope on the rising edge to extract the maximum positive slope point. Perform saturation plateau detection on the interface echo and extract its maximum positive slope point on the rising edge. S6, Peak Estimation and Distance Calculation: The sampling offset between the target echo peak point and the maximum slope point is used as the system offset. The equivalent peak point of the interface echo is then calculated by back-calculating the sampling point difference between the interface equivalent peak point and the target peak point, combined with the sampling interval. The target distance is then calculated according to the time-of-flight ranging relationship.
2. The robust ranging method for underwater multispectral full-waveform lidar as described in claim 1, characterized in that, S1 includes the following steps: Set the wavelength set according to the preset wavelength interval within the preset wavelength range: ， where Λ is a set of wavelengths, λ i is the ith wavelength, and N is the number of wavelengths. for each wavelength λ i a preset number of echo sequences are collected at preset sampling intervals, and stored in a three-dimensional structure of wavelength index, group number index and time sampling point index, and at least one known calibration distance d cal Under the condition, the calibration waveform is collected for template construction.
3. The robust ranging method for underwater multispectral full-waveform lidar as described in claim 1, characterized in that, S2 includes the following steps: S2.1, for the original echo x i,m [k] Perform Gaussian smoothing filtering to obtain the smoothed echo y i,m [k], where x i,m [k] represents the original echo amplitude of the k-th sampling point under the condition that the wavelength index is i and the group number index is m; y i,m [k] represents the smoothed echo amplitude of the kth sampling point under the condition that the wavelength index is i and the group number index is m; S2.2, smooth the echo y i,m [k] serves as the input for subsequent sliding window cross-correlation matching, peak localization, and slope calculation.
4. The robust ranging method for underwater multispectral full-waveform lidar as described in claim 1, characterized in that, S3 includes the following steps: S3.1, at the known calibration distance d cal Under these conditions, for each wavelength λ i Extract the target echo band within a fixed time window of length W; S3.2, denoted as k as the peak index of the i-th target echo band. p,i And shift each segment so that its peak index is unified with the reference peak index k. p ; S3.3, average the normalized echo bands of the entire wavelength to generate a shared target echo reference template r[g], where g is the index of the sampling point within the target echo reference template.
5. The robust ranging method for underwater multispectral full-waveform lidar as described in claim 1, characterized in that, S4 includes the following steps: S4.1, for smooth echo y i,m [k] is subjected to sliding window normalized cross-correlation calculation with the shared target echo reference template r[g] to obtain the similarity sequence c[τ], where τ is the sliding window start position index; Select the P echo bands corresponding to the highest local peaks in the similarity sequence as a candidate set; S4.2 Calculate the noise baseline mean μ within the preset noise baseline interval. n Standard deviation of noise baseline σ n And use the amplitude determination threshold T=μ n +ασ n The significance of the amplitude is determined, where α is a preset coefficient; S4.3 Sort the candidate echo set that has passed the judgment in chronological order, and select the peak with the latest arrival time as the final target echo arrival point.
6. The robust ranging method for underwater multispectral full-waveform lidar as described in claim 1, characterized in that, S5 includes the following steps: S5.1, Calculate the discrete slope sequence within the rising edge interval of the target echo according to the sampling interval Δt: ， Among them, s i,m [k] represents the discrete slope of the k-th sampling point given wavelength index i and group number index m. i,m [k+1], y i,m [k-1] represents the smoothed echo, k+1 represents the index of the next sampling point after the k-th sampling point, and k-1 represents the index of the previous sampling point after the k-th sampling point. a ,k b [ ] represents the index interval of the rising edge of the target echo; The point with the maximum positive slope is taken as the robust time delay characteristic point of the target echo; S5.2, perform saturation plateau detection on the interface echo, when L consecutive sampling points satisfy y[k]≥V sat When a point is identified as a saturation plateau, L is a preset threshold for the number of consecutive points, and V... sat This is the system saturation amplitude threshold; Calculate the discrete slope s within the rising edge interval before the saturation plateau. i,m [k], extracts the point with the maximum positive slope on the interface.
7. The robust ranging method for underwater multispectral full-waveform lidar as described in claim 1, characterized in that, S6 includes the following steps: S6.1 Calculate the sampling offset Δk between the target echo peak point and the target echo maximum positive slope point, and use the sampling offset Δk to back-calculate the interface echo equivalent peak point; S6.2, based on the sampling point difference Δk between the target echo peak point and the equivalent peak point of the interface echo. T Converting time difference The distance is calculated based on the time-of-flight ranging relationship, where Δt is the sampling interval.
8. A robust ranging device for underwater multispectral full-waveform lidar, characterized in that, It includes one or more processors and a memory, wherein the memory stores instructions that, when executed by the processor, implement the robust ranging method of underwater multispectral full-waveform lidar as described in any one of claims 1-7.
9. A readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the robust ranging method for underwater multispectral full-waveform lidar as described in any one of claims 1-7.