Optimized laser radar echo signal waveform decomposition method and device, medium and product
By using the parameters of the maximum value as the initial value in the lidar echo signal waveform decomposition algorithm, the waveform decomposition is iteratively performed to solve the problem of difficulty in selecting the initial value, and the accuracy and reliability of the decomposition are improved.
Patent Information
- Application Number
- CN202411675814.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-21
- Publication Date
- 2025-02-21
AI Technical Summary
In the existing lidar echo signal waveform decomposition algorithm, difficulty in selecting initial values causes the fitting to converge to a local minimum value, and the decomposition error is large, so waveform decomposition cannot be accurately realized.
By determining the maximum value point of the waveform to be decomposed, bring its corresponding parameters as initial values into the classic waveform decomposition algorithm, remove the invalid components, iterate until the stop condition is met, and the final waveform decomposition result is obtained.
It effectively avoids the problem of difficulty in selecting initial values, reduces decomposition errors, improves the accuracy of waveform decomposition, and is simple to operate and easy to use.
Smart Images
Figure CN119493096A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of waveform decomposition, and in particular to an optimized laser radar echo signal waveform decomposition method, equipment, medium and product. Background Art
[0002] LiDAR is an active remote sensing technology that uses laser as a light source. LiDAR can detect the concentration, spatial distribution, particle size, shape, and vertical structure of aerosols in the atmosphere by emitting laser pulses and receiving and analyzing reflected or scattered signals. LiDAR technology has important applications in atmospheric science, environmental monitoring, and climate change research due to its high precision, anti-interference, and three-dimensional perception capabilities.
[0003] In a lidar system, a laser pulse is emitted to the surface of a target object. When it encounters different object characteristics (such as air masses, vegetation, etc.), the laser will produce different reflection and scattered signals. These reflected signals will return to the sensor in the form of continuous waveforms. Each waveform contains detailed information about the distance, physical characteristics, and overlap between the target objects.
[0004] The waveform decomposition algorithm can decompose and extract multiple single echo components from the original lidar signal, which is very helpful for the analysis of the structural and distribution characteristics of the detected target. The algorithm currently widely used in waveform decomposition is mainly the LM (Levenberg-Marquard) algorithm, the accuracy of which depends largely on the initial value. If the difference between the input initial parameters and the expected value is too large, the fitting will converge to the local minimum, or even cause the algorithm to diverge, which will lead to a larger decomposition error and the inability to accurately achieve waveform decomposition. Summary of the invention
[0005] The purpose of this application is to provide an optimized laser radar echo signal waveform decomposition method, equipment, medium and product, which avoids the problem of difficult initial value selection in the waveform decomposition algorithm, reduces the decomposition error, improves the accuracy of waveform decomposition, and is simple to operate and easy to use.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides an optimized laser radar echo signal waveform decomposition method, comprising:
[0008] Obtaining a waveform to be decomposed based on the original signal waveform;
[0009] Determine the maximum point of the waveform to be decomposed;
[0010] The parameters corresponding to the maximum point are used as initial values, and are introduced into the classical waveform decomposition algorithm to obtain a decomposition result, and invalid waveform components in the decomposition result are eliminated to obtain a waveform decomposition result; the initial value includes at least one or more of amplitude, center position and scale factor;
[0011] Subtract all components of the waveform decomposition result from the waveform to be decomposed to obtain a new decomposed waveform;
[0012] The new decomposed waveform is used as the waveform to be decomposed, and the step of determining the maximum point of the waveform to be decomposed is returned to obtain all waveform decomposition results when the set stop condition is met;
[0013] The result obtained by subtracting the new decomposed waveform corresponding to each component in all the waveform decomposition results from the waveform to be decomposed is used as the new waveform to be decomposed, and the parameter value of each component in all the waveform decomposition results is used as the initial value, and brought into the classical waveform decomposition algorithm to obtain the fitting result;
[0014] The fitting result is taken as the final waveform decomposition result.
[0015] Optionally, obtaining a waveform to be decomposed based on the original signal waveform includes:
[0016] Acquire the original signal waveform;
[0017] The original signal waveform is filtered and denoised to obtain the waveform to be decomposed.
[0018] Optionally, the filtering and denoising process includes at least one or more of sliding filtering, mean filtering, wavelet denoising and empirical mode decomposition denoising.
[0019] Optionally, the method used to determine the maximum point of the waveform to be decomposed includes at least a derivative method or a Fourier transform method.
[0020] Optionally, the classical waveform decomposition algorithm includes at least LM algorithm, expectation-maximization algorithm or Gauss-Newton algorithm.
[0021] Optionally, the decomposition model of the LM algorithm includes at least a Gaussian model, a generalized Gaussian model or a quasi-exponential model.
[0022] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-described optimized lidar echo signal waveform decomposition methods.
[0023] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described optimized lidar echo signal waveform decomposition methods.
[0024] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-described optimized lidar echo signal waveform decomposition methods.
[0025] According to the specific embodiments provided in this application, this application has the following technical effects:
[0026] The present application provides an optimized laser radar echo signal waveform decomposition method, device, medium and product. By bringing the parameters corresponding to the maximum point as initial values into the classical waveform decomposition algorithm, and bringing the parameter values of each component in all the waveform decomposition results as initial values into the classical waveform decomposition algorithm to obtain the waveform decomposition result, the problem of the difficulty in selecting the initial values of the waveform decomposition algorithm can be avoided, and the influence of inaccurate input initial values on the waveform decomposition effect can be effectively improved. The waveform components of the laser radar echo signal can be well described and extracted to obtain good waveform decomposition results, and the error between the restored waveform (obtained by superposition of all waveform components obtained by the waveform decomposition algorithm) and the original waveform can be reduced, thereby improving the accuracy of the waveform decomposition while being able to reduce the decomposition error. The operation is simple and easy to use. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0028] Figure 1 A schematic flow chart of an optimized laser radar echo signal waveform decomposition method provided in one embodiment of the present application;
[0029] Figure 2 A schematic diagram of a waveform decomposition result obtained by using an optimized laser radar echo signal waveform decomposition method provided in an embodiment of the present application;
[0030] Figure 3 A schematic diagram of a waveform decomposition result obtained by using a classical waveform decomposition method provided in an embodiment of the present application;
[0031] Figure 4A schematic diagram of another waveform decomposition result obtained by using an optimized laser radar echo signal waveform decomposition method provided in an embodiment of the present application;
[0032] Figure 5 A schematic diagram of another waveform decomposition result obtained by using a classical waveform decomposition method provided in an embodiment of the present application;
[0033] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0034] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0036] In an exemplary embodiment, the optimized laser radar echo signal waveform decomposition method provided by the present application can effectively improve the influence of inaccurate input initial value on the waveform decomposition effect by adding two iterative steps on the basis of the classical algorithm, thereby greatly improving the accuracy of waveform decomposition and reducing the error between the restored waveform (obtained by superposition of all waveform components obtained by the waveform decomposition algorithm) and the original waveform. Based on this, according to the laser radar detection principle, the pulse emitted by the laser interacts with the target to form an echo signal, and the waveform decomposition method considers the echo signal to be the sum of the responses of each ground object in the laser path to the laser pulse. Therefore, a lot of information about the detected target can be obtained by decomposing the laser radar echo.
[0037] The classic method of decomposing lidar echoes usually involves filtering and fitting the original waveform, where the fitting process includes initial parameter estimation and waveform fitting. This application adds two iterative steps on this basis to optimize the initial value. Figure 1 As shown, an optimized laser radar echo signal waveform decomposition method is provided, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, including:
[0038] Step 100: Obtain a waveform to be decomposed based on the original signal waveform, wherein the waveform to be decomposed is denoted as F(x).
[0039] Step 101: Determine the maximum point of the waveform to be decomposed.
[0040] Step 102: Take the parameters corresponding to the maximum point as the initial value, bring them into the classic waveform decomposition algorithm to obtain the decomposition result, and remove the invalid waveform components in the decomposition result to obtain the waveform decomposition result. The waveform decomposition result is recorded as P 1 ={p 1 ,p 2 ,…,p n}, p i is the waveform decomposition result P 1 The i-th component of .
[0041] The initial value refers to the function parameters required by the decomposition model of the classical waveform decomposition algorithm, such as amplitude, center position and scale factor. For a certain maximum value in step 102, the corresponding initial value can be selected in the following way: the amplitude of the maximum value is used as the initial value of the amplitude, the position coordinates of the maximum value are used as the initial value of the center position, and the coordinate distance between the maximum value and the inflection point closest to it is used as the initial value of the scale factor. In addition, it is not limited to using various other initial value selection methods according to actual needs.
[0042] Step 103: Subtract all components in the waveform decomposition result from the waveform to be decomposed to obtain a new decomposed waveform.
[0043] Step 104: Take the new decomposed waveform as the waveform to be decomposed, and return to step 101 until the set stop condition is met, and obtain all the waveform decomposition results. Among them, all the waveform decomposition results are recorded as P = {P 1 ,P 2 ,…},P i is the decomposition result obtained by executing the third step for the ith time, and the number of all waveform components is num. This step 104 is the first iterative step.
[0044] Step 105: Subtract each component (i.e., P) of all waveform decomposition results from the waveform to be decomposed. i ) is used as the new waveform to be decomposed, and the parameter value of each component in all the waveform decomposition results is used as the initial value, and is brought into the classic waveform decomposition algorithm to obtain the fitting result. Among them, the new waveform to be decomposed is recorded as ff(x), and the fitting result is recorded as pp i .
[0045] Step 106: Use the fitting result as the final waveform decomposition result.
[0046] In step 105 and step 106, it is actually the second iteration step, which is mainly to calculate each component P of the decomposition result. i, subtract P from the original signal waveform i Get the new waveform to be decomposed ff(x), with P i The parameter value of is the initial value, and the classical waveform decomposition algorithm is used to obtain the fitting result pp i , and repeat this process until the set stop condition is reached, and the final waveform decomposition result PP = {pp 1 ,pp 2 ,…,pp num}, i=1,2,…num.
[0047] For example, the number of cycles reaches 10num or the error between the restored waveform and the original waveform is less than 10 -5 As the set stop condition.
[0048] Decompose all waveforms into pp i Superposition, that is, FF(x) = pp 1 +pp 2 +…+pp num To restore the waveform, which may be used in actual data processing, the present application uses the difference between it and the original signal waveform to measure the error of waveform decomposition.
[0049] In another exemplary embodiment of the present application, the implementation process of the above step 100 may include:
[0050] Step 1001: Obtain an original signal waveform, wherein the original signal waveform may be a measured signal obtained by using a horizontal detection laser radar.
[0051] Step 1002: Filter and de-noise the original signal waveform to obtain a waveform to be decomposed. The filtering and de-noising method may be one or more of sliding filtering, mean filtering, wavelet de-noising and empirical mode decomposition de-noising, but is not limited thereto. For example, a sliding average filtering method may be used to filter and de-noise the original signal waveform to obtain a waveform to be decomposed.
[0052] In another exemplary embodiment of the present application, the method used to determine the maximum point and the inflection point of the waveform to be decomposed in step 101 may include but is not limited to the derivative method or the Fourier transform method.
[0053] For example, the derivative method is used to find the maximum and inflection points of the waveform, and the threshold is set to the signal average noise μ noise Add three times the noise standard deviation σ noise , remove all points less than the threshold, and record the number of maximum values n and the number of inflection points m. The threshold used here generally refers to the intensity threshold, which can be determined according to actual usage or the noise level of the signal.
[0054] In another exemplary embodiment of the present application, the classical waveform decomposition algorithm used above may be a variety of nonlinear least squares methods such as the LM algorithm, the Expectation-Maximum algorithm (EM) or the Gauss-Newton algorithm (Gauss-Newton).
[0055] In another exemplary embodiment of the present application, the decomposition model of the LM algorithm at least includes a Gaussian model, a generalized Gaussian model or a quasi-exponential model.
[0056] In another exemplary embodiment of the present application, in order to further improve the algorithm accuracy, in the first iteration step of the above step 104, the maximum point and the inflection point can be replaced by the maximum value and the nearest inflection point.
[0057] In another exemplary embodiment of the present application, in order to more clearly demonstrate the superiority of the present application, a group of classical waveform decomposition methods are added as a control group. Figure 2 to Figure 5 It can be seen that the number of waveform components obtained by the method provided by this application and the classical waveform decomposition method is the same, and the center position is not much different, but from the perspective of the error between the restored waveform and the original waveform, the effect of this application is better, especially for the case where multiple components overlap. That is to say, the waveform decomposition method provided by this application can more accurately describe the internal structure of the lidar signal than the classical waveform decomposition method. In practical applications, it can provide a starting point with smaller errors and better results for subsequent multiple data processing. Figure 2 to Figure 5 In the figure, the numbers (1), (2), (3) and (4) represent extreme points.
[0058] Based on the above description, the method provided in this application has technical advantages such as good effect, simple practical operation, strong universality, and strong scalability.
[0059] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store optimized laser radar echo signal waveform decomposition data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an optimized laser radar echo signal waveform decomposition method is implemented.
[0060] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0061] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0062] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0063] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0064] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0065] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.
[0066] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.
[0067] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. An optimized laser radar echo signal waveform decomposition method, characterized in that: The optimized laser radar echo signal waveform decomposition method comprises: Obtaining a waveform to be decomposed based on the original signal waveform; Determine the maximum point of the waveform to be decomposed; The parameters corresponding to the maximum point are used as initial values, and are introduced into the classical waveform decomposition algorithm to obtain a decomposition result, and invalid waveform components in the decomposition result are eliminated to obtain a waveform decomposition result; the initial value includes at least one or more of amplitude, center position and scale factor; Subtract all components of the waveform decomposition result from the waveform to be decomposed to obtain a new decomposed waveform; The new decomposed waveform is used as the waveform to be decomposed, and the step of determining the maximum point of the waveform to be decomposed is returned to obtain all waveform decomposition results when the set stop condition is met; The result obtained by subtracting the new decomposed waveform corresponding to each component in all the waveform decomposition results from the waveform to be decomposed is used as the new waveform to be decomposed, and the parameter value of each component in all the waveform decomposition results is used as the initial value, and brought into the classical waveform decomposition algorithm to obtain the fitting result; The fitting result is taken as the final waveform decomposition result.
2. The optimized laser radar echo signal waveform decomposition method according to claim 1, characterized in that: The waveform to be decomposed is obtained based on the original signal waveform, including: Acquire the original signal waveform; The original signal waveform is filtered and denoised to obtain the waveform to be decomposed.
3. The optimized laser radar echo signal waveform decomposition method according to claim 2 is characterized in that: The filtering and denoising process includes at least one or more of sliding filtering, mean filtering, wavelet denoising and empirical mode decomposition denoising.
4. The optimized laser radar echo signal waveform decomposition method according to claim 1, characterized in that: The method used to determine the maximum point of the waveform to be decomposed includes at least a derivative method or a Fourier transform method.
5. The optimized laser radar echo signal waveform decomposition method according to claim 1, characterized in that: The classical waveform decomposition algorithm at least includes LM algorithm, expectation maximization algorithm or Gauss-Newton algorithm.
6. The optimized laser radar echo signal waveform decomposition method according to claim 5, characterized in that: The decomposition model of the LM algorithm at least includes a Gaussian model, a generalized Gaussian model or a quasi-exponential model.
7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the optimized laser radar echo signal waveform decomposition method described in any one of claims 1 to 6.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the optimized laser radar echo signal waveform decomposition method described in any one of claims 1 to 6 is implemented.
9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the optimized laser radar echo signal waveform decomposition method described in any one of claims 1 to 6 is implemented.