Laser radar signal noise reduction method and device, medium and product

Abstract

The invention discloses a laser radar signal noise reduction method and device, a medium and a product, and relates to the field of data processing, and the method comprises the steps: carrying out the empirical mode decomposition of a laser radar signal, and obtaining an IMF component sequence and a remainder component; performing outlier detection on the first k IMF components of the IMF component sequence by adopting an outlier detection algorithm to obtain high-frequency useful information; zero setting processing is carried out on the parts, except for the high-frequency useful information, in the first k IMF components, and processed IMF components are obtained; and adding the processed IMF components, the IMF components except the first k IMF components in the IMF component sequence and the remainder components to obtain a laser radar signal after noise reduction. According to the method, the problem that a traditional empirical mode decomposition noise reduction method is likely to cause distortion of key signals can be solved, detailed information of the key signals is well protected, and then the aerosol detection performance of the laser radar and the data reliability are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a laser radar signal noise reduction method, device, medium and product. Background Art

[0002] LiDAR is a common aerosol monitoring solution. It can provide vertical and horizontal distribution information of aerosol particles in the atmosphere, measure the optical properties of aerosols, help study the physical and chemical characteristics of aerosols, and their impact on atmospheric radiation transmission and climate change. It can also assess air quality by real-time monitoring of changes in aerosol concentration in the atmosphere, and identify and track pollution sources.

[0003] Noise signals are inevitably present in lidar detection, which interferes with the signal quality and measurement accuracy of lidar, leading to signal weakening, inaccurate measurement results, signal distortion, data processing complexity, limited dynamic range, and reduced detection depth and resolution. Therefore, the use of effective noise reduction and filtering technology is of great significance for improving the performance and data reliability of lidar detection of aerosols.

[0004] Currently, the empirical mode decomposition (EMD) denoising method is generally used to denoise the signals collected by the lidar. However, the traditional EMD denoising method is prone to cause distortion of key signals, making it impossible to further improve the performance and data reliability of lidar aerosol detection. Summary of the invention

[0005] The purpose of this application is to provide a lidar signal denoising method, equipment, medium and product, which can solve the problem that the traditional empirical mode decomposition denoising method easily leads to distortion of key signals, and well protect the detailed information of key signals, thereby significantly improving the performance and data reliability of lidar detection of aerosols.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a laser radar signal noise reduction method, comprising:

[0008] Perform empirical mode decomposition on the lidar signal to obtain the intrinsic mode function (IMF) component sequence and the residual component;

[0009] Using an outlier detection algorithm to perform outlier detection on the first k IMF components of the IMF component sequence to obtain high-frequency useful information;

[0010] Performing zero processing on the parts of the first k IMF components except the high-frequency useful information to obtain processed IMF components;

[0011] The processed IMF components, the IMF components other than the first k IMF components in the IMF component sequence, and the residual components are added together to obtain the denoised laser radar signal.

[0012] Optionally, the outlier detection algorithm is a median absolute deviation method, a Quartiles non-normal outlier method, a Tukey's Fences method, a mean absolute deviation method, a Grubbs normal outlier method or a GESD outlier method.

[0013] Optionally, the laser radar signal after noise reduction is expressed as:

[0014]

[0015] Where Y(t) represents the denoised lidar signal, IMF_disposed j (t) represents the processed IMF component, IMF j (t) represents the IMF component, r n (t) represents the remainder component, and n represents the number of IMF component sequences in the IMF component sequence.

[0016] Optionally,

[0017] 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 the laser radar signal denoising method provided above.

[0018] 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 the above-mentioned lidar signal denoising method.

[0019] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned lidar signal denoising method.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application provides a laser radar signal denoising method, device, medium and product, which first performs empirical mode decomposition on the laser radar signal, then performs outlier detection on the high-frequency components in the IMF component sequence obtained by the decomposition, obtains high-frequency useful information corresponding to the signal peak, and finally adds these high-frequency useful information and other components to obtain the denoised laser radar signal. This can effectively suppress noise while well protecting the detailed information of key signals, so as to solve the problem that traditional empirical mode decomposition denoising methods are prone to cause distortion of key signals, thereby significantly improving the performance and data reliability of laser radar detection of aerosols. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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.

[0023] Figure 1 This is an application environment diagram of a laser radar signal noise reduction method in one embodiment of the present application;

[0024] Figure 2 A schematic diagram of a flow chart of a laser radar signal noise reduction method provided in one embodiment of the present application;

[0025] Figure 3 A noise reduction result comparison curve diagram provided in an embodiment of the present application;

[0026] Figure 4 for Figure 3 A magnified view of the key position 1;

[0027] Figure 5 for Figure 3 A magnified view of the key position 2;

[0028] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] 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.

[0030] 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 with reference to the accompanying drawings and specific implementation methods.

[0031] The laser radar signal denoising method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the laser radar signal to the server 104. After the server 104 receives the laser radar signal, the server 104 performs empirical mode decomposition on the laser radar signal to obtain an IMF component sequence and a residual component. The outlier detection algorithm is used to perform outlier detection on the first k IMF components of the IMF component sequence to obtain high-frequency useful information. The parts of the first k IMF components other than the high-frequency useful information are zeroed to obtain the processed IMF components. The processed IMF components, the IMF components other than the first k IMF components in the IMF component sequence, and the residual components are added to obtain the denoised laser radar signal. The server 104 can feed back the obtained denoised laser radar signal to the terminal 102. In addition, in some embodiments, the laser radar signal noise reduction method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform noise reduction processing on the laser radar signal, or the server 104 can obtain the laser radar signal from the data storage system and perform noise reduction processing on the laser radar signal.

[0032] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0033] In an exemplary embodiment, Figure 2 As shown, a laser radar signal denoising method is provided, which is executed by a computer device, and can be 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 Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 200 to 203. Among them:

[0034] Step 200: Perform empirical mode decomposition on the lidar signal to obtain an IMF component sequence and a residual component.

[0035] Step 201: Use an outlier detection algorithm to perform outlier detection on the first k IMF components of the IMF component sequence to obtain high-frequency useful information.

[0036] Step 202: performing zero processing on the parts of the first k IMF components except for the high-frequency useful information to obtain processed IMF components.

[0037] Step 203: Add the processed IMF components, the IMF components other than the first k IMF components in the IMF component sequence, and the residual components to obtain the denoised laser radar signal.

[0038] By implementing the above-mentioned steps 201 to 203, the present application inherits the advantages of the traditional empirical mode decomposition method that does not require basis functions, is highly versatile, and is easy to operate. At the same time, it is combined with the outlier detection algorithm, so that the detailed information of key signals can be well protected while effectively suppressing noise, so as to solve the problem that the traditional empirical mode decomposition denoising method is prone to cause distortion of key signals, thereby significantly improving the performance and data reliability of lidar detection of aerosols.

[0039] In another exemplary embodiment of the present application, Empirical Mode Decomposition (EMD) is a time-frequency domain signal processing algorithm that decomposes signals based on the time scale characteristics of the data itself.

[0040] In theory, EMD can be applied to the decomposition of any type of signal, so it has a very obvious advantage in processing non-stationary and nonlinear data. It is suitable for analyzing non-linear and non-stationary signal sequences and has a high signal-to-noise ratio. Moreover, it overcomes the problem of non-adaptive basis functions. For example, the wavelet transform commonly used in waveform decomposition or filtering and noise reduction requires the pre-selection of a certain wavelet basis. The selection of the wavelet basis has a great influence on the result of the entire wavelet analysis, and once it is determined, it cannot be replaced during the entire analysis process. That is to say, even if the selected wavelet basis is the best globally, it may not be the best choice in some local areas. In contrast, EMD does not need to pre-set any basis function, and can adaptively perform the main component analysis of the signal, which has stronger adaptability.

[0041] The empirical mode decomposition theory believes that any signal is composed of a number of different IMFs. Based on this, the original signal can be expressed as:

[0042]

[0043] Where X(t) represents the original signal, IMFj (t) represents the decomposed intrinsic mode function, i.e., IMF component, r n (t) represents the residual component after decomposition, that is, the monotonic trend term of the signal.

[0044] The IMF components represent the frequency components in the original signal respectively, and are arranged in order from high frequency to low frequency. Therefore, by discarding the IMF components containing different frequency components, that is, discarding the first few IMF components and retaining the remaining parts, the effect of low-pass filtering and noise reduction can be achieved. However, if the first k IMF components are directly discarded, some useful information is likely to be lost, resulting in a large distortion of the filtered signal at a critical position. According to the signal characteristics of the high-frequency intrinsic components (i.e., the first few IMF components), their baselines are all at zero, and there are strong fluctuations where there are signal peaks in the echo data. When processing data, these signal peaks are precisely the key and should be saved as completely as possible. Based on this, this embodiment uses an outlier detection algorithm to process the first k IMF components, and by setting a reasonable outlier judgment threshold, the outliers in the first k IMF components are obtained. These outliers are the high-frequency useful information corresponding to the signal peaks, and the parts other than them are set to zero to obtain the processed IMF components, recorded as IMF_disposed k (t). The denoised LiDAR signal can be obtained by adding all processed IMF components, retained IMF components and residual components. The denoised LiDAR signal is expressed as:

[0045]

[0046] Where Y(t) represents the denoised lidar signal, IMF_disposed j (t) represents the processed IMF component, IMF j (t) represents the IMF component, r n (t) represents the remainder component, and n represents the number of IMF component sequences in the IMF component sequence.

[0047] In another exemplary embodiment of the present application, the outlier detection algorithm used in step 201 can be the median absolute deviation method, the Quartiles non-normal outlier method, Tukey's Fences method, the mean absolute deviation method, the Grubbs normal outlier method, the generalized extreme studentized deviation (GESD) outlier method, etc., but is not limited to these, and other outlier detection algorithms can also be implemented.

[0048] In another exemplary embodiment of the present application, the number of IMF components to be zeroed can be determined according to the use requirements. In actual application, it is recommended to process the first one-third of the total number of IMF components in the IMF component sequence according to the test results, that is, For example, the data in the example is decomposed into 9 IMF components in total, and the outlier detection of the first 3 IMF components is performed using the Quartiles non-normal outlier method.

[0049] In another exemplary embodiment of the present application, the classical EMD decomposition method is used to perform empirical mode decomposition on the laser radar signal, and the implementation process of step 200 may include the following steps:

[0050] 1) Local extreme point detection: Find all local maxima and local minima in the lidar signal.

[0051] 2) Envelope construction: Use the points corresponding to the local maximum to interpolate to obtain the upper envelope, and use the points corresponding to the local minimum to interpolate to obtain the lower envelope.

[0052] 3) Mean calculation: Calculate the mean of the upper envelope and the lower envelope.

[0053] 4) IMF component extraction: Subtract the envelope mean from the lidar signal to obtain the first IMF component.

[0054] 5) Iteration: Treat the remaining lidar signal as a new original signal and repeat the above steps 1) to 4) until the remaining signal no longer contains any meaningful fluctuations. Arrange the obtained IMF components in order from high frequency to low frequency to obtain the IMF component sequence and the residual component.

[0055] In another exemplary embodiment of the present application, a laser radar with horizontal detection is used to collect laser radar signals to obtain measured signals, and the laser radar signal denoising method provided in the present application is used to denoise the collected measured signals. At the same time, in order to more clearly demonstrate the superiority of the present application, a group of signals processed by the traditional EMD denoising method is added to this embodiment as a control group, and the denoised laser radar signals are as follows: Figure 3 shown. Figure 4 and Figure 5 They are Figure 3 From the enlarged images of key positions 1 and 2, we can see that the traditional EMD noise reduction method directly discards the high-frequency components, resulting in distortion of the peak signal, while the present application can effectively ensure that the lidar signal peak is not distorted while reducing noise.

[0056] In summary, the laser radar signal denoising method provided in this application can effectively suppress noise while protecting the detailed information of key signals. It has technical advantages such as sufficient theoretical support, good results, simple practical operation, and strong universality.

[0057] 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 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, 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. Among them, 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 laser radar signal denoising 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, a laser radar signal denoising method is implemented.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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).

[0063] 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.

[0064] 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.

[0065] 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. A laser radar signal denoising method, characterized in that: The laser radar signal noise reduction method comprises: Perform empirical mode decomposition on the lidar signal to obtain the IMF component sequence and the residual component; Using an outlier detection algorithm to perform outlier detection on the first k IMF components of the IMF component sequence to obtain high-frequency useful information; Performing zero processing on the parts of the first k IMF components except the high-frequency useful information to obtain processed IMF components; The processed IMF components, the IMF components other than the first k IMF components in the IMF component sequence, and the residual components are added together to obtain the denoised laser radar signal.

2. The laser radar signal denoising method according to claim 1, characterized in that: The outlier detection algorithm is the median absolute deviation method, the Quartiles non-normal outlier method, the Tukey's Fences method, the mean absolute deviation method, the Grubbs normal outlier method or the GESD outlier method.

3. The laser radar signal denoising method according to claim 1, characterized in that: The de-noised laser radar signal is expressed as: Where Y(t) represents the denoised lidar signal, IMF_disposed j (t) represents the processed IMF component, IMF j (t) represents the IMF component, r n (t) represents the remainder component, and n represents the number of IMF component sequences in the IMF component sequence.

4. The laser radar signal denoising method according to claim 3, characterized in that:

5. 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 laser radar signal denoising method described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the laser radar signal denoising method described in any one of claims 1 to 4 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the laser radar signal denoising method described in any one of claims 1 to 4 is implemented.

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