CN117597597A

CN117597597A - Signal processing method, device and readable storage medium

Info

Publication number: CN117597597A
Application number: CN202180095655.XA
Authority: CN
Inventors: 牛犇; 汪敬; 朱琳
Original assignee: Suteng Innovation Technology Co Ltd
Current assignee: Suteng Innovation Technology Co Ltd
Priority date: 2021-04-12
Filing date: 2021-04-12
Publication date: 2024-02-23
Also published as: WO2022217407A1

Abstract

A signal processing method, apparatus and readable storage medium. The method comprises the following steps: n-stage analysis is carried out on the signal to be processed to obtain 2 ^N Individual components (S901); wherein N is more than or equal to 2, and the signal to be processed is a signal with noise; determining a target component layer number according to a frequency band to be filtered, and denoising a component positioned in the frequency band to be filtered in the target component layer number by a wavelet threshold value to obtain a processed filter signal (S902); the filtered signal is output (S903). The distance detection accuracy can be improved.

Description

Signal processing method, device and readable storage medium

Technical Field

The present disclosure relates to the field of laser detection technologies, and in particular, to a signal processing method, a signal processing device, and a readable storage medium.

Background

The laser radar is a radar system for detecting the characteristic quantities such as the position and the speed of a target by emitting a laser beam. The working principle is that a detection signal is transmitted to a target, then the received signal reflected from the target is compared with the transmitted signal, and after proper processing, the related information of the target, such as the parameters of the distance, azimuth, altitude, speed, gesture, even shape and the like of the target, can be obtained, so that the targets of an airplane, a missile and the like are detected, tracked and identified.

For a laser radar system, the ranging principle is that continuous waves with the frequency linearly changed are emitted in a sweep frequency period to serve as emergent signals, part of the emergent signals serve as local oscillation signals, the rest of the emergent signals are emitted outwards to be detected, a certain frequency difference exists between echo signals returned after being reflected by an object and the local oscillation signals, and distance information between a detected target and a radar can be obtained through measuring the frequency difference.

In a Frequency Modulated Continuous Wave (FMCW) system, the transmitted signal undergoes diffuse reflection after hitting the target object a distance in space. Because the reflectivity of the target object is limited, and the echo signals after diffuse reflection of the target object are reflected to a large extent in space, the echo signals received in a certain direction are weak, and the signal to noise ratio is poor. Therefore, an efficient signal denoising algorithm is needed to optimally denoise the received echo signal to improve the signal-to-noise ratio, so that the sensitivity of the FMCW system detection can be improved.

Disclosure of Invention

The embodiment of the application provides a signal processing method, a signal processing device and a readable storage medium, which can improve the distance detection precision in the full frequency range.

In a first aspect, an embodiment of the present application provides a signal processing method, including:

n-stage analysis is carried out on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

determining a target component layer number according to a frequency band to be filtered, and denoising a component positioned in the frequency band to be filtered in the target component layer number by a wavelet threshold value to obtain a processed filter signal; the frequency band to be filtered is any one of M frequency bands, wherein the M frequency bands cover the full frequency band range, and M is more than or equal to 2;

outputting the filtering signal under the condition that the filtering signal meets a preset condition;

and under the condition that the filtering signals do not meet the preset conditions, determining the next frequency band to be filtered as the frequency band to be filtered according to a preset sequence, determining the number of target component layers according to the frequency band to be filtered, carrying out wavelet threshold denoising on the components of the target component layers, which are located in the frequency band to be filtered, to obtain the processed filtering signals until the filtering signals corresponding to the last frequency band in the M frequency bands do not meet the preset conditions, and outputting the filtering signals corresponding to the last frequency band.

In a second aspect, an embodiment of the present application provides a signal processing method, including:

determining a target component layer number according to a frequency band to be filtered, and denoising a component positioned in the frequency band to be filtered in the target component layer number by a wavelet threshold value to obtain a processed filter signal;

outputting the filtered signal.

In a third aspect, an embodiment of the present application provides a signal processing apparatus, including:

a first decomposition module for N-decomposing the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

the first denoising module is used for determining the number of target component layers according to the frequency band to be filtered, and denoising the components positioned in the frequency band to be filtered in the number of target component layers by a wavelet threshold value to obtain a processed filtering signal; the frequency band to be filtered is any one of M frequency bands, wherein the M frequency bands cover the full frequency band range, and M is more than or equal to 2;

the first output module is used for outputting the filtering signal under the condition that the filtering signal meets the preset condition;

And the first determining module is used for determining the next frequency band to be filtered as the frequency band to be filtered according to a preset sequence under the condition that the filtered signal does not meet the preset condition, calling the first denoising module to determine the number of target component layers according to the frequency band to be filtered, denoising the component positioned in the target component layers and the frequency band to be filtered by a wavelet threshold value to obtain a processed filtered signal, and calling the first output module to output the filtered signal corresponding to the last frequency band until the filtered signal corresponding to the last frequency band in the M frequency bands does not meet the preset condition.

In a fourth aspect, an embodiment of the present application provides a signal processing apparatus, including:

the second decomposition module is used for performing N-stage decomposition on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

the second denoising module is used for determining the number of target component layers according to the frequency band to be filtered, and denoising the components positioned in the frequency band to be filtered in the number of target component layers by a wavelet threshold value to obtain a processed filtering signal;

and the second output module is used for outputting the filtering signal.

In a fifth aspect, embodiments of the present application provide a signal processing apparatus, including: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by a processor and to perform the method steps provided in the first or second aspect of the embodiments of the present application.

In a sixth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method provided by the first or second aspect of embodiments of the present application.

The technical scheme provided by some embodiments of the present application has the beneficial effects that at least includes:

in one or more embodiments of the present application, denoising processing may be sequentially attempted on a signal to be processed according to a preset frequency band sequence, so as to determine which frequency band the noise signal is specifically located in, thereby implementing accurate denoising of the signal to be processed in a full frequency band range. In addition, the embodiment of the application provides several new filtering algorithms, and the denoising function in different frequency ranges is realized by increasing the precision of wavelet coefficient decomposition and performing threshold processing on components in different frequency ranges.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of distribution of a vehicle-mounted lidar according to an embodiment of the present application;

fig. 2 is a schematic diagram of a signal processing system architecture according to an embodiment of the present application;

fig. 3 is a schematic diagram of the time variation of the frequency of the transmission and echo signals of the triangular wave modulated FMCW system according to the embodiment of the present application;

FIG. 4a is a schematic diagram of an exploded version of a classical wavelet threshold denoising algorithm;

fig. 4b is a schematic diagram of a simulation result of a classical wavelet threshold denoising algorithm when ranging 100 meters;

fig. 4c is a schematic diagram of a simulation result of a classical wavelet threshold denoising algorithm when ranging 200 meters;

fig. 4d is a schematic diagram of a simulation result of a classical wavelet threshold denoising algorithm when ranging 300 meters;

fig. 4e is a schematic diagram of an exploded mode of a wavelet threshold denoising algorithm according to an embodiment of the present application;

Fig. 4f is a schematic diagram of a simulation result of the low-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application when ranging for 100 meters;

fig. 4g is a schematic diagram of a simulation result of a low-pass filtering wavelet threshold denoising algorithm provided in an embodiment of the present application when ranging 200 meters;

fig. 4h is a schematic diagram of a simulation result of the low-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application when ranging 300 meters;

fig. 4i is a schematic diagram of a simulation result of a band-pass filtering wavelet threshold denoising algorithm provided in an embodiment of the present application when ranging 100 meters;

fig. 4j is a schematic diagram of a simulation result of a band-pass filtering wavelet threshold denoising algorithm provided in an embodiment of the present application when ranging 200 meters;

fig. 4k is a schematic diagram of a simulation result of a band-pass filtering wavelet threshold denoising algorithm according to an embodiment of the present application when ranging 300 meters;

fig. 4l is a schematic diagram of a simulation result of the high-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application when ranging 100 meters;

fig. 4m is a schematic diagram of a simulation result of the high-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application when ranging 200 meters;

fig. 4n is a schematic diagram of a simulation result of the high-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application when ranging 300 meters;

Fig. 5 is a schematic flow chart of a signal processing method according to an embodiment of the present application;

fig. 6a is a full-band range division schematic diagram according to an embodiment of the present application;

FIG. 6b is a schematic diagram illustrating another full band range division according to an embodiment of the present disclosure;

fig. 6c is a schematic diagram of detection capability of a signal processing method at different distances according to an embodiment of the present application;

fig. 7a is a schematic diagram of another full-band range division provided in an embodiment of the present application;

fig. 7b is a schematic diagram of another full-band range division provided in an embodiment of the present application;

fig. 7c is a schematic diagram of the detection capability of another signal processing method provided in the embodiment of the present application at different distances;

fig. 8a is a schematic diagram of another full-band range division provided in an embodiment of the present application;

fig. 8b is a schematic diagram of another full-band range division provided in an embodiment of the present application;

FIG. 8c is a schematic diagram illustrating the detection capability of another signal processing method according to the embodiment of the present application at different distances;

fig. 9 is a flow chart of another signal processing method according to an embodiment of the present application;

fig. 10 is a schematic diagram of detection capability of a signal processing method at different distances according to an embodiment of the present application;

FIG. 11 is a schematic diagram showing the detection capability of another signal processing method provided in the embodiment of the present application at different distances;

fig. 12 is a schematic diagram of the detection capability of another signal processing method provided in the embodiment of the present application at different distances;

fig. 13 is a schematic structural diagram of a signal processing device according to an embodiment of the present application;

fig. 14 is a schematic structural diagram of another signal processing device according to an embodiment of the present application;

fig. 15 is a schematic structural diagram of another signal processing device according to an embodiment of the present application;

fig. 16 is a schematic structural diagram of another signal processing device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

The terms "first," second, "" third and the like in the description and in the claims of this application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Fig. 1 shows an exemplary distribution diagram of a vehicle-mounted lidar. As shown in fig. 1, the laser radars (110 a, 110b, 110c, and 110 d) on board the vehicle can be distributed at four corners of the vehicle, and can be used to collect point cloud data in the radiation range (around the vehicle). Of course, the laser radar can also be distributed at any position of the left side, the right side, the front side, the rear side, the top and the like of the vehicle according to the detection requirement. The laser radar in the embodiment of the application is an FMCW laser radar. One of the lidars 110a will be described as an example. The lidar 110a may collect echo signals within its radiation range, obtain denoised difference frequency signals according to the transmit signal and the echo signals, and send the denoised difference frequency signals to the vehicle-mounted terminal 120. Specifically, the signal processing device determines a difference frequency signal with noise according to the transmitting signal and the receiving signal; and performing signal processing on the difference frequency signal to obtain a denoised difference frequency signal. The vehicle-mounted terminal 120 may process the denoised difference frequency signal obtained by the laser radar 110a to obtain point cloud data, identify the type and position of the obstacle around the vehicle and the movement track of the obstacle according to the point cloud data, and make a path planning in combination with the running condition of the vehicle to generate a driving strategy.

Fig. 2 schematically illustrates a signal processing system architecture according to an embodiment of the present application. As shown in fig. 2, the FMCW lidar may emit a transmit signal that is reflected back to an echo signal after hitting a target object over a distance in space, where the echo signal is received by the FMCW radar system. The echo signal is mixed with the transmit signal to generate a difference frequency signal. The signal processing device of the FMCW laser radar can adopt a wavelet threshold denoising algorithm to denoise the difference frequency signal, and output the denoised difference frequency signal to the vehicle-mounted terminal. In a specific implementation, the signal processing device may be disposed on the FMCW lidar, may be independent of the FMCW lidar, or may be disposed on the vehicle terminal 120, which is not limited in this embodiment. In the embodiment of the application, the signal processing device is set in the FMCW laser radar as an example, and the FMCW laser radar adopts a wavelet threshold denoising algorithm to denoise the difference frequency signal.

The FMCW laser radar provided by the embodiment of the application can be applied to equipment such as robots and unmanned aerial vehicles. That is, the difference frequency signal after the FMCW lidar output denoising is not limited to be sent to the vehicle-mounted terminal, but may be sent to a CPU in a robot or an unmanned aerial vehicle in other application scenarios, and the embodiment of the present application is not limited thereto.

Fig. 3 shows a schematic diagram of the frequency of the transmitted signal and the echo signal of the triangular wave modulated FMCW lidar over time. As shown in fig. 3:

assuming that a target with a distance R from the laser radar moves at a constant speed v toward the laser radar (in a positive direction away from the laser radar), and the echo delay of the transmit signal and the echo signal is τ=2 (r+vt)/c, the difference frequency signals are represented by the following formulas (1) and (2), respectively, in the upper-scan frequency band and the lower-scan frequency band:

wherein f ₀ For the initial frequency of each period of the transmitted signal, μ=b/T is the slope of the frequency modulation, T is the signal period, B is the signal bandwidth, a is the amplitude of the difference signal,the phase of the difference frequency signal, c is the speed of light, and t is the time variable.

It can be seen that in FMCW lidar the absolute value of the frequency of the difference signal is in a linear positive correlation with the distance of the detected target, i.e. the further the target is, the higher the frequency of the difference signal and vice versa.

Next, the decomposition of the classical wavelet region denoising algorithm will be described. As shown in fig. 4a, the core of the classical wavelet threshold denoising algorithm is single-sided wavelet decomposition, and the specific process is as follows:

(1) A first-order wavelet decomposition is carried out on the signal with noise to obtain two parts of cA and cD, wherein the cA and the cD respectively represent an approximate part and a detail part. This is a level 1 decomposition.

(2) Detail part cD is not processed, and the approximate part cA is decomposed to obtain two parts, namely an approximate part cAA and a detail part cAD. This is level 2 decomposition.

(3) Detail portion cAD is left untreated and further decomposition of approximation portion cAA continues to yield both cAAA and cAAD portions. This is level 3 decomposition.

(4) And so on, the detail part is kept unchanged when each level of decomposition is performed, and only the approximate part is decomposed until the nth level of decomposition is performed. The specific number of decomposition layers can be selected according to actual needs.

After the wavelet decomposition is completed, the wavelet threshold denoising algorithm carries out threshold processing on each obtained wavelet component part according to a threshold rule.

Because the classical wavelet threshold denoising algorithm has obvious low-pass filtering characteristics, and is combined with the schematic diagram of the frequency change of the transmission signal and the echo signal of the triangular wave modulated FMCW system shown in FIG. 3, it can be seen that the classical wavelet threshold denoising algorithm has the following problems:

(1) When the target distance is far, the echo signal is at a medium-high frequency, and in this case, the low-pass filtering property of classical wavelet threshold denoising leads to weakening or filtering of the signal at the medium-high frequency, so that the algorithm is invalid. That is, if the echo signal is in the middle-high frequency band, the classical wavelet algorithm cannot achieve the denoising effect, but rather weakens or filters the echo signal as noise, resulting in detection failure. Therefore, the classical wavelet algorithm is effective for echo signals detected in a short distance, and the detection capability is deteriorated to cause detection failure in the face of long distance, so that the application of the actual FMCW laser radar is severely limited.

(2) The more decomposition layers the classical wavelet threshold denoises, the larger the frequency range the low pass filtering is filtered out. The echo signal must be in a narrower low frequency range to ensure that it is not attenuated or filtered out. That is, the larger the number of decomposition layers, the closer the effective distance of the classical wavelet threshold denoising algorithm to the echo signal is, and the more seriously the practical FMCW laser radar application is affected.

(3) The minimum filtering range of the low-pass filtering of the classical wavelet threshold denoising algorithm is determined by the condition that the number of decomposition layers is 1, and the filtering range cannot be reduced continuously. That is, when the sampling frequency of the laser radar is fixed, the classical wavelet threshold denoising algorithm has a maximum effective detection distance, and cannot be adjusted to make the maximum effective detection distance larger, which seriously affects the practical FMCW laser radar application.

Fig. 4 b-4 d schematically show simulation results of a classical wavelet threshold denoising algorithm. Fig. 4 b-4 d show simulation results at ranging 100 meters, 200 meters and 300 meters, respectively. There are three sub-graphs in each graph: the upper subplot shows the spectrum of the noise-free signal, the middle subplot is used to show the spectrum of the noisy signal (signal to noise ratio of-15 dB), and the lower subplot shows the spectrum of the signal after classical wavelet denoising with a number of decomposition layers of 1. It can be seen that the classical wavelet threshold denoising algorithm has obvious low-pass filtering special effects. At ranging 100 meters, the signal frequency is outside the filtered range. While at 200 meters of ranging, the signal strength is attenuated by the wavelet threshold denoising algorithm. And when the distance is 300 meters, the signal strength is filtered by a wavelet threshold denoising algorithm. (4) If the maximum effective detection distance is to be increased on the basis of not changing the classical wavelet threshold denoising algorithm, the sampling frequency of the laser radar needs to be increased. That is, faster hardware coordination is required. This can greatly increase the overall hardware requirements and cost of the FMCW signal processing portion, limiting the application of FMCW lidar.

Therefore, the embodiment of the application provides several novel wavelet threshold denoising algorithms, and compared with the classical wavelet threshold denoising algorithm, the method can improve the middle-long distance detection performance.

Fig. 4e schematically illustrates an exploded view of a wavelet threshold denoising algorithm according to an embodiment of the present application. As shown in fig. 4e, the specific process of the wavelet threshold denoising algorithm for wavelet decomposing a signal is as follows:

(1) And carrying out primary wavelet coefficient decomposition on the signal with noise to obtain a cA low-frequency part and a cD high-frequency part.

(2) Using the decomposition method of (1), cA was continued to decompose to obtain a cAA low frequency portion and a cAD high frequency portion, and cD was continued to decompose to obtain a cDD high frequency portion and a cDA low frequency portion. This is level 2 decomposition.

(3) The four parts of cAA, cAD, cDA, cDD obtained by the decomposition of the previous stage are respectively decomposed to obtain cAAA, cAAD, cADA, cADD, cDAA, cDAD, cDDA, cDDD. This is level 3 decomposition.

(4) And so forth. The specific number of decomposition layers can be selected according to actual needs.

It is understood that the three-level wavelet coefficient decomposition shown in fig. 4e is not limited, and that there may be fewer or more levels of wavelet coefficient decomposition in a specific implementation, which is not limited in the embodiments of the present application.

Based on the decomposition mode of the wavelet threshold denoising algorithm provided in fig. 4e, the embodiment of the application provides three wavelet threshold denoising algorithms, which respectively realize low-pass filtering, band-pass filtering and high-pass filtering. These three wavelet threshold denoising algorithms are described below, respectively.

Low pass filtered wavelet threshold denoising algorithm: specifically, the high-frequency component in the multiple components obtained after the wavelet coefficient is decomposed can be subjected to threshold processing, and other components remain unchanged. Taking the second-level decomposition as an example, four components can be obtained after the second-level decomposition: after cAA, cAD, cDA, cDD, only the resulting high frequency portion cDD is thresholded. The other parts remain unchanged.

Fig. 4 f-4 h schematically illustrate simulation results of the low-pass filtering wavelet threshold denoising algorithm provided in an embodiment of the present application. Fig. 4 f-4 h show simulation results at ranging 100 meters, 200 meters and 300 meters, respectively. There are three sub-graphs in each graph: the upper subplot shows the spectrum of the noise-free signal, the middle subplot is used for showing the spectrum of the noise-free signal (the signal to noise ratio is-15 dB), and the lower subplot shows the spectrum of the signal after denoising by adopting the low-pass filtering wavelet threshold denoising algorithm provided by the embodiment of the application. As can be seen by comparing fig. 4b to fig. 4d, the low-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application is significantly reduced in the frequency range of low-pass filtering. In particular, it can be seen that the signal strength is not impaired when the distance is measured at 200 meters.

Band-pass filter wavelet threshold denoising algorithm: specifically, the low-frequency component and the high-frequency component in the multiple components obtained after the wavelet coefficient is decomposed can be subjected to threshold processing, and other components remain unchanged. Taking the second-level decomposition as an example, four components can be obtained after the second-level decomposition: after cAA, cAD, cDA, cDD, only the low frequency portion cAA and the high frequency portion cDD are thresholded. The other parts remain unchanged.

Fig. 4 i-4 k schematically illustrate simulation results of the band-pass filtering wavelet threshold denoising algorithm according to an embodiment of the present application. Fig. 4 i-4 k show simulation results at ranging 100 meters, 200 meters and 300 meters, respectively. There are three sub-graphs in each graph: the upper subplot shows the spectrum of the noise-free signal, the middle subplot is used for showing the spectrum of the noise-free signal (the signal to noise ratio is-15 dB), and the lower subplot shows the spectrum of the signal after denoising by adopting the band-pass filtering wavelet threshold denoising algorithm provided by the embodiment of the application. As can be seen by comparing fig. 4b to fig. 4d, the band-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application has a distinct band-pass filtering property. In particular, it can be seen that the signal strength is not weakened when ranging to 200 meters, but weakened when ranging to 100 meters and 300 meters. High-pass filtering wavelet threshold denoising algorithm: specifically, the low-frequency component in the multiple components obtained after the wavelet coefficient is decomposed can be subjected to threshold processing, and other components remain unchanged. Taking the second-level decomposition as an example, four components can be obtained after the second-level decomposition: after cAA, cAD, cDA, cDD, only the resulting low frequency portion cAA is thresholded. The other parts remain unchanged.

Fig. 4 l-4 n are schematic diagrams illustrating simulation results of the high-pass filtering wavelet threshold denoising algorithm according to embodiments of the present application. Fig. 4 l-4 n show simulation results at ranging 100 meters, 200 meters and 300 meters, respectively. There are three sub-graphs in each graph: the upper subplot shows the spectrum of the noise-free signal, the middle subplot is used for showing the spectrum of the noise-free signal (the signal to noise ratio is-15 dB), and the lower subplot shows the spectrum of the signal after denoising by adopting the high-pass filtering wavelet threshold denoising algorithm provided by the embodiment of the application. As can be seen by comparing fig. 4b to fig. 4d, the high-pass filtering wavelet threshold denoising algorithm provided in the embodiment of the present application has a significant high-pass filtering property. In particular, it can be seen that the signal strength is not weakened when ranging to 300 meters, but weakened when ranging to 100 meters and 200 meters.

It is known that the above-mentioned thresholding is specifically: the wavelet coefficients of the components having magnitudes less than a preset threshold are set to zero. The preset threshold is a preset critical value. Specifically, if the magnitude of a certain component is smaller than a preset threshold, it is determined that the component is mainly caused by noise, and the wavelet coefficient of the component is set to zero, thereby removing the noise.

Next, a signal processing method provided in an embodiment of the present application is described with reference to fig. 1 to fig. 4 n. Fig. 5 schematically illustrates a flow chart of a signal processing method according to an embodiment of the present application. As shown in fig. 5, the signal processing method may include the following steps:

s501: n-stage analysis is carried out on the signal to be processed to obtain 2 ^N A component.

Wherein N is more than or equal to 2, and the signal to be processed is a signal with noise. The manner of N-splitting the signal to be processed may refer to the splitting manner shown in fig. 4e, which is not described herein. Each component obtained by decomposition corresponds to a different frequency range.

Prior to S501, the method may further include: and acquiring a signal to be processed. The signal to be processed may be a difference frequency signal as mentioned in the embodiment of fig. 2.

S502: and determining the number of target component layers according to the frequency band to be filtered, and carrying out wavelet threshold denoising on the components of the target component layers, the components of which are positioned in the frequency band to be filtered, so as to obtain the processed filter signal.

Specifically, the frequency band to be filtered is any one of M frequency bands, wherein the M frequency bands cover the full frequency band range, and M is more than or equal to 2.

Optionally, the M frequency bands are contiguous. I.e. adjacent ones of the M frequency bands do not overlap.

Optionally, adjacent frequency bands in the M frequency bands partially overlap.

Specifically, the signal to be processed is the difference frequency signal mentioned in the embodiment of fig. 2. After N-phase separation of the difference frequency signal, 2 can be obtained ^N Each component corresponds to a frequency range. As can be seen from the above-mentioned analysis of the correlation description of the linear positive correlation between the frequency of the difference signal and the target distance in the embodiment of fig. 3, the full-band range mentioned in the embodiment of the present application is the frequency range of the difference signal corresponding to the ranging range of the lidar. Illustratively, the difference frequency signal has a frequency of about 400MHz at a range of 300 meters. The range of the laser radar is defined as 0-300 m, and the full frequency range is 0-400MHz.

Specifically, the wavelet threshold denoising method may include, but is not limited to, the following: mode maximum denoising, correlation denoising, wavelet shrinkage threshold denoising, translation invariant wavelet denoising and the like.

S503: judging whether the frequency band to be filtered is the last frequency band in the M frequency bands; if yes, executing S506; if not, execution proceeds to S504 or S505.

In particular, the M frequency bands may be divided into at least two frequency bands. The signal processing method provided by the embodiment of the application can perform wavelet threshold denoising processing on the at least two frequency bands according to a preset sequence. The last frequency band in the M frequency bands is the only frequency band which is not subjected to wavelet threshold denoising processing in the M frequency bands, namely other frequency bands in the M frequency bands are subjected to wavelet threshold denoising processing.

It is known that the signal to be processed may include a noise signal and a difference frequency signal. If the noise signal is completely filtered or partially filtered in S502, the processed filtered signal may include a difference signal.

S504: and outputting the filtering signal under the condition that the filtering signal meets the preset condition.

Specifically, between S502 and S504, the method further includes: and extracting a difference frequency signal in the filtered signal. And the preset condition is that the difference frequency signal is successfully extracted.

Specifically, if the frequency of the difference frequency signal is in the frequency band to be filtered, the corresponding component is subjected to wavelet threshold denoising treatment, and the difference frequency signal is filtered, so that the difference frequency signal extraction fails; if the frequency of the difference frequency signal is not in the frequency band to be filtered, after the wavelet threshold denoising treatment, the noise is filtered or partially filtered, and the difference frequency signal is easy to extract.

A specific way to extract the difference frequency signal from the filtered signal may be to perform a fast fourier transform (Fast Fourier Transform, FFT) on the filtered signal.

S505: and under the condition that the filtering signal does not meet the preset condition, determining the next frequency band to be filtered as the frequency band to be filtered according to a preset sequence, and executing the step S502.

Specifically, if the filtered signal does not meet the preset condition, which indicates that the noise signal and the difference frequency signal are both filtered, it is indicated that filtering the frequency band to be filtered cannot achieve filtering of only the noise signal without weakening the difference frequency signal, and then the original signal to be processed needs to be subjected to filtering processing of the next frequency band.

In this embodiment of the present application, the above full frequency band range is divided into M frequency bands in multiple manners, which may be 2 frequency bands, 3 frequency bands or even more, and the number of divided frequency bands is selected according to the hardware computing capability of signal processing, the precision requirement of signal processing, and the like, and each division manner may correspond to multiple preset sequences. When the full frequency range is divided into 2 frequency ranges, the preset sequence is simpler; when the full frequency range is divided into 3 or more frequency ranges, the preset sequence is relatively more complex. For convenience of description, the full frequency range is divided into 3 frequency ranges for illustration; when the full frequency band range is divided into more than 3 frequency bands, the processing logic is the same as that of the frequency band range divided into 3 frequency bands.

The three decomposition modes are described below.

Mode one: low pass + band pass + high pass (M=3)

Specifically, the M frequency bands may be a low-pass frequency band (which may be referred to as a third low-pass frequency band in the embodiment of the present application), a band-pass frequency band, and a high-pass frequency band (which may be referred to as a third high-pass frequency band in the embodiment of the present application). The third low-pass frequency band is a frequency band with the frequency smaller than the fifth threshold, the band-pass frequency band is a frequency band with the frequency larger than the sixth threshold and smaller than the seventh threshold, and the third high-pass frequency band is a frequency band with the frequency larger than the eighth threshold. Wherein the fifth threshold is greater than or equal to the sixth threshold and the seventh threshold is greater than or equal to the eighth threshold.

Fig. 6a shows an exemplary diagram of a full band range split into 3 bands. As shown in fig. 6a, the full band range can be divided into a low-pass band, a band-pass band, and a high-pass band. Adjacent frequency bands are continuously connected. I.e. the fifth threshold value is equal to the sixth threshold value and the seventh threshold value is equal to the eighth threshold value. The bandwidths of each frequency band may be the same or different.

Fig. 6b schematically shows another decomposition of the full band range into 3 bands. As shown in fig. 6b, the full band range can be divided into a low-pass band, a band-pass band, and a high-pass band. Adjacent frequency bands partially overlap. I.e. the fifth threshold is greater than the sixth threshold and the seventh threshold is greater than the eighth threshold. The bandwidths of each frequency band may be the same or different.

The decomposition mode can correspond to 6 preset sequences:

first kind: the third low-pass band, the band-pass band, the third high-pass band.

Second kind: the third high-pass band, the band-pass band, and the third low-pass band.

Third kind: the band-pass frequency band, the third low-pass frequency band and the third high-pass frequency band.

Fourth kind: the third low-pass frequency band, the third high-pass frequency band and the band-pass frequency band.

Fifth: the third high-pass frequency band, the third low-pass frequency band and the band-pass frequency band.

Sixth: the band-pass frequency band, the third high-pass frequency band and the third low-pass frequency band.

Taking the first as an example, the embodiment of the present application may first determine a third low-pass band as a to-be-filtered band, determine the number of layers of a target component according to the third low-pass band, and perform wavelet threshold denoising on a component located in the to-be-filtered band in the number of layers of the target component to obtain a processed filtered signal; if a difference signal can be extracted from the filtered signal, the filtered signal is output. If the difference frequency signal cannot be extracted from the filtering signal, determining a band-pass frequency band as a frequency band to be filtered, determining the number of target component layers according to the band-pass frequency band, and carrying out wavelet threshold denoising on components positioned in the frequency band to be filtered in the target component layers to obtain a processed filtering signal; if a difference signal can be extracted from the filtered signal, the filtered signal is output. If the difference frequency signal cannot be extracted from the filtering signal, determining a third high-pass frequency band as a frequency band to be filtered, determining the number of target component layers according to the third high-pass frequency band, and carrying out wavelet threshold denoising on the components positioned in the frequency band to be filtered in the target component layers to obtain the processed filtering signal, wherein the filtering signal can be output no matter whether the difference frequency signal can be extracted from the filtering signal or not.

It can be known that the wavelet threshold denoising is performed on the component located in the low-pass band in the target component layer number, namely the low-pass band part of the difference frequency signal is kept unchanged, and the other part of the difference frequency signal is subjected to noise filtering, so that the filtering covering the low-pass band can be realized; wavelet threshold denoising is carried out on the components in the band-pass frequency band in the number of target component layers, namely the band-pass frequency band part of the difference frequency signal is kept unchanged, and the rest part of the difference frequency signal is filtered, so that the filtering of the band-pass frequency band can be realized; and (3) carrying out wavelet threshold denoising on the components positioned in the high-pass frequency band in the target component layer number, namely keeping the high-pass frequency band part of the difference frequency signal unchanged, and filtering the rest part of the difference frequency signal to realize the filtering covering the high-pass frequency band.

And the wavelet threshold denoising is carried out on the components corresponding to different frequency bands, so that the detection probability corresponding to different detection distances can be improved. The detection probability in the whole detection range can be improved through the combined filtering of the low-pass band, the band-pass band and the high-pass band, so that the effective detection distance of the FMCW laser radar is improved, and the application of the FMCW laser radar is promoted.

Fig. 6c schematically illustrates the detection capability of the signal processing method corresponding to the decomposition mode at different distances. As shown in fig. 6c, the signal processing method corresponding to the decomposition mode has the detection capability in a short distance, a middle distance and a long distance. And the wavelet threshold denoising processing is carried out on the component corresponding to the low-pass frequency range of a certain signal, so that low-pass filtering can be realized, and further short-distance detection is realized. Meanwhile, wavelet threshold denoising processing is carried out on components corresponding to a certain signal low-pass frequency range and a certain signal high-pass frequency range, so that band-pass filtering can be realized, and medium-distance detection is further realized. And the wavelet threshold denoising processing is carried out on the component corresponding to the high-pass frequency range of a certain signal, so that high-pass filtering can be realized, and further, long-distance detection is realized.

Specifically, the thresholding of the components of the low-pass band range may refer to the low-pass filtering wavelet threshold denoising algorithm mentioned in the embodiment of fig. 4e, and will not be described herein.

Specifically, the method of thresholding the components in the low-pass band range and the high-pass band range may refer to the band-pass filtering wavelet threshold denoising algorithm mentioned in the embodiment of fig. 4e, which is not described herein.

Specifically, the thresholding of the components in the high-pass band range may refer to the high-pass filtering wavelet threshold denoising algorithm mentioned in the embodiment of fig. 4e, and will not be described herein.

Mode two: broadband low-pass + narrowband high-pass (m=2)

Specifically, the M frequency bands may be a low-pass band (which may be referred to as a first low-pass band in the embodiment of the present application) and a high-pass band (which may be referred to as a first high-pass band in the embodiment of the present application). The first low-pass frequency band is a frequency band with a frequency smaller than a first threshold value, and the first high-pass frequency band is a frequency band with a frequency larger than a second threshold value. Wherein the first threshold is greater than or equal to the second threshold. The bandwidth of the first low-pass band (i.e., the range length) and the bandwidth of the first high-pass band may be set according to the signal processing requirements. The bandwidth of the first low-pass band is larger than the bandwidth of the first high-pass band.

Fig. 7a schematically shows a diagram of a decomposition of a full band range into 2 bands. As shown in fig. 7a, the full band range can be divided into a low-pass band and a high-pass band. Adjacent frequency bands are continuously connected. I.e. the first threshold value is equal to the second threshold value.

Fig. 7b schematically shows another decomposition of the full band range into 2 bands. As shown in fig. 7b, the full band range can be divided into a low-pass band and a high-pass band. Adjacent frequency bands partially overlap. I.e. the first threshold is greater than the second threshold.

This decomposition may correspond to 2 preset orders:

first kind: the first low-pass band and the first high-pass band.

Second kind: the first high-pass band and the first low-pass band.

Fig. 7c illustrates a diagram of the detection capability of the signal processing method corresponding to the second decomposition mode over different distances. As shown in fig. 7c, the signal processing method corresponding to the second decomposition mode has detection capability at a short distance, a middle distance and a long distance. In the embodiment of the application, the bandwidth of the low-pass band is larger, the bandwidth of the high-pass band is smaller, and the high-pass filtering can be realized by denoising the wavelet threshold value of the low-pass band range of a certain signal, so that the remote detection is realized. The wavelet threshold denoising processing is carried out on the component in the high-pass frequency range of a certain signal, so that low-pass filtering can be realized, and further detection of short distance and medium distance is realized.

Mode three: narrowband low-pass + wideband high-pass (m=2)

Specifically, the M frequency bands may be a low-pass frequency band (which may be referred to as a second low-pass frequency band in the embodiment of the present application) and a high-pass frequency band (which may be referred to as a second high-pass frequency band in the embodiment of the present application). The second low-pass frequency band is a frequency band with the frequency smaller than a third threshold value, and the second high-pass frequency band is a frequency band with the frequency larger than a fourth threshold value. Wherein the third threshold is greater than or equal to the fourth threshold. Wherein the bandwidth (i.e., range length) of the second low-pass band is greater than the bandwidth of the second high-pass band.

Fig. 8a illustrates a diagram of a full band range split into 2 bands. As shown in fig. 8a, the full band range can be divided into a low-pass band and a high-pass band. Adjacent frequency bands are continuously connected. I.e. the third threshold value is equal to the fourth threshold value.

Fig. 8b schematically shows another decomposition of the full band range into 2 bands. As shown in fig. 8b, the full band range can be divided into a low-pass band and a high-pass band. Adjacent frequency bands partially overlap. I.e. the third threshold is larger than the fourth threshold.

This decomposition may correspond to 2 preset orders:

first kind: the second low-pass band and the second high-pass band.

Second kind: the second high-pass band and the second low-pass band.

Fig. 8c illustrates a diagram of the detection capabilities of the signal processing method corresponding to the decomposition mode three over different distances. As shown in fig. 8c, the signal processing method corresponding to the third decomposition mode has detection capability in a short distance, a middle distance and a long distance. In the embodiment of the application, the bandwidth of the low-pass band is smaller, the bandwidth of the high-pass band is larger, and the high-pass filtering can be realized by denoising the wavelet threshold value of the low-pass band range of a certain signal, so that the detection of long distance and medium distance is realized. The wavelet threshold denoising processing is carried out on the component in the high-pass frequency range of a certain signal, so that low-pass filtering can be realized, and further short-distance detection is realized.

It should be noted that, as the number of decomposition layers increases, the bandwidth of the frequency band corresponding to each component is narrower. In this embodiment of the present application, the decomposition of the full frequency band range may refer to the result of component decomposition, so that any one of M frequency bands may be consistent with a frequency band corresponding to one or several components in a certain component layer number. The M frequency bands obtained by adopting the decomposition mode of a certain characteristic can be respectively located in a certain component in different component layers. For example, if a wideband low-pass band and a narrowband high-pass band are obtained by decomposition in a manner, the wideband low-pass band may correspond to a component corresponding to the low-pass band in the second component layer (e.g., the cDDD component obtained by three-stage decomposition in fig. 4 e); the narrowband high-pass frequency point may correspond to a component corresponding to the high-pass frequency band in the second component layer (e.g., a cAA component obtained after three-stage decomposition in fig. 4 e).

In this application embodiment, can be through attempting in proper order according to the frequency channel order of predetermineeing and carry out denoising processing to treating the signal of handling to confirm which frequency channel the noise signal is specifically in, thereby realize treating the accurate denoising of signal in full frequency channel within range to this, handle the noise of low frequency channel only in prior art, promoted the denoising precision, and expanded effective detection's scope, and then increased FMCW system's detection distance.

Fig. 9 shows a flowchart of another signal processing method according to an embodiment of the present application. As shown in fig. 9, the signal processing method may include the following steps:

s901: n-stage analysis is carried out on the signal to be processed to obtain 2 ^N A component.

Specifically, S901 corresponds to S501, and will not be described here again.

S902: and determining the number of target component layers according to the frequency band to be filtered, and carrying out wavelet threshold denoising on the components of the target component layers, the components of which are positioned in the frequency band to be filtered, so as to obtain the processed filter signal.

Alternatively, the frequency band to be filtered may be a low-pass frequency band, in particular, a frequency band with a frequency smaller than the ninth threshold, for implementing low-pass filtering.

Specifically, the thresholding of the components corresponding to the low-pass band range may refer to the low-pass filtering wavelet threshold denoising algorithm mentioned in the embodiment of fig. 4e, which is not described herein. Fig. 10 illustrates diagrams of detection capability at different distances for embodiments of the present application after thresholding the components of the low-pass band range. As shown in fig. 10, wavelet threshold denoising is performed on the components in the low-pass frequency range, that is, the low-frequency part of the difference frequency signal remains unchanged, and the rest high-frequency part of the difference frequency signal is filtered, so that low-pass filtering can be implemented after processing, and therefore, the embodiment of the application has excellent detection capability in a short distance.

Alternatively, the frequency band to be filtered may be a high-pass frequency band, specifically, a frequency band with a frequency greater than the tenth threshold, for implementing high-pass filtering.

Specifically, the thresholding of the components in the high-pass band range may refer to the high-pass filtering wavelet threshold denoising algorithm mentioned in the embodiment of fig. 4e, and will not be described herein. Fig. 11 illustrates diagrams of detection capability at different distances for embodiments of the present application after thresholding the components of the high-pass band range. As shown in fig. 11, the components in the high-pass band range are subjected to wavelet threshold denoising, that is, the high-frequency part of the difference frequency signal remains unchanged, the rest low-frequency parts of the difference frequency signal are filtered, and high-pass filtering can be realized after processing.

Optionally, the frequency band to be filtered may be a low-pass frequency band and a high-pass frequency band, specifically, a frequency band with a frequency smaller than the eleventh threshold and a frequency band with a frequency greater than the twelfth threshold, for implementing band-pass filtering. Wherein the eleventh threshold is less than the twelfth threshold.

Specifically, the method of thresholding the components in the low-pass band range and the high-pass band range may refer to the band-pass filtering wavelet threshold denoising algorithm mentioned in the embodiment of fig. 4e, which is not described herein. Fig. 12 illustrates a diagram of the detection capability of an embodiment of the present application over different distances after thresholding the components of the band pass range. As shown in fig. 12, wavelet threshold denoising is performed on components in the band-pass frequency range, that is, the intermediate frequency band of the difference frequency signal remains unchanged, and the rest of the low-frequency part and the high-frequency part of the difference frequency signal are filtered, so that band-pass filtering can be implemented after processing.

It is known that the short distance, the medium distance and the long distance mentioned in the embodiments of the present application are distances between the obstacle and the lidar. The distance between the obstacle and the lidar determines the frequency of the difference signal. The short distance, the medium distance and the long distance are relative concepts, and specific numerical values of the three are not limited in the embodiments of the present application.

S903: outputting the filtered signal.

It is known that the signal to be processed may include a noise signal and a difference frequency signal. If the frequency of the difference frequency signal is not in the frequency band to be filtered, the noise is filtered or partially filtered after the wavelet threshold denoising treatment, and the difference frequency signal can be extracted.

The embodiment of the application provides several new filtering algorithms, and can realize the denoising function in different frequency ranges by increasing the precision of wavelet coefficient decomposition and carrying out threshold processing on components in different frequency ranges, so that the application scene of the filtering algorithm provided by the embodiment of the application is enlarged.

The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

Referring to fig. 13, a schematic structural diagram of a signal processing apparatus according to an exemplary embodiment of the present application is shown. The signal processing means may be implemented by software, hardware or a combination of both. The signal processing device 130 includes: the first decomposition module 1310, the first denoising module 1320, the first output module 1330, and the first determination module 1340. Wherein:

A first decomposition module 1310 for performing N-decomposition on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

a first denoising module 1320, configured to determine a number of target component layers according to a frequency band to be filtered, and denoise a component located in the frequency band to be filtered in the number of target component layers by using a wavelet threshold to obtain a processed filtered signal; the frequency band to be filtered is any one of M frequency bands, wherein the M frequency bands cover the full frequency band range, and M is more than or equal to 2;

a first output module 1330 for outputting the filtered signal if the filtered signal meets a preset condition;

the first determining module 1340 is configured to determine, in a preset order, a next frequency band to be filtered as the frequency band to be filtered when the filtered signal does not meet the preset condition, invoke the first denoising module 1320 to determine a number of target component layers according to the frequency band to be filtered, denoise a wavelet threshold for a component located in the target component layers in the frequency band to be filtered, and obtain a processed filtered signal, until a filtered signal corresponding to a last frequency band in the M frequency bands does not meet the preset condition, invoke the first output module 1330 to output a filtered signal corresponding to the last frequency band.

In some possible embodiments, the M frequency bands are contiguous.

In some possible embodiments, adjacent frequency bands of the M frequency bands partially overlap.

In some possible embodiments, the signal processing device 130 further comprises: the extracting module is configured to extract a difference frequency signal in the filtered signal after the first denoising module 1320 obtains the processed filtered signal, and before the first output module 1330 outputs the filtered signal if the filtered signal meets a preset condition; the preset condition is that the difference frequency signal is successfully extracted.

In some possible embodiments, m=2;

the M frequency bands are first low-pass frequency bands and first high-pass frequency bands; the first low-pass frequency band is a frequency band with the frequency smaller than a first threshold value, and the first high-pass frequency band is a frequency band with the frequency larger than a second threshold value;

the bandwidth of the first low-pass band is greater than the bandwidth of the first high-pass band; the first threshold is greater than or equal to the second threshold.

In some possible embodiments, the preset sequence is:

the first low-pass band and the first high-pass band; or alternatively

The first high-pass band and the first low-pass band.

In some possible embodiments, m=2;

the M frequency bands are second low-pass frequency bands and second high-pass frequency bands; the second low-pass frequency band is a frequency band with the frequency smaller than a third threshold value, and the second high-pass frequency band is a frequency band with the frequency larger than a fourth threshold value;

the bandwidth of the second low-pass band is smaller than the bandwidth of the second high-pass band; the third threshold is greater than or equal to the fourth threshold.

In some possible embodiments, the preset sequence is:

the second low-pass band and the second high-pass band; or alternatively

The second high-pass band and the second low-pass band.

In some possible embodiments, m=3;

the M frequency bands are a third low-pass frequency band, a band-pass frequency band and a third high-pass frequency band; the third low-pass frequency band is a frequency band with the frequency smaller than a fifth threshold value, the band-pass frequency band is a frequency band with the frequency larger than a sixth threshold value and smaller than a seventh threshold value, and the third high-pass frequency band is a frequency band with the frequency larger than an eighth threshold value;

the fifth threshold is greater than or equal to the sixth threshold, and the seventh threshold is greater than or equal to the eighth threshold.

In some possible embodiments, the preset sequence is:

The third low-pass band, the band-pass band, the third high-pass band; or alternatively

The third high-pass band, the band-pass band, the third low-pass band; or alternatively

The band-pass frequency band, the third low-pass frequency band and the third high-pass frequency band; or alternatively.

The third low-pass frequency band, the third high-pass frequency band and the band-pass frequency band; or alternatively

The third high-pass frequency band, the third low-pass frequency band and the band-pass frequency band; or alternatively

The band-pass frequency band, the third high-pass frequency band and the third low-pass frequency band.

Referring to fig. 20, a schematic structural diagram of a signal processing apparatus according to another exemplary embodiment of the present application is shown. The signal processing means may be implemented by software, hardware or a combination of both. The signal processing device 140 includes: a second decomposition module 1410, a second denoising module 1420, and a second output module 1430. Wherein:

A second decomposition module 1410 for performing N-decomposition on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

the second denoising module 1420 is configured to determine a target component layer number according to a frequency band to be filtered, and perform wavelet threshold denoising on a component located in the frequency band to be filtered in the target component layer number to obtain a processed filtered signal;

and a second output module 1430 for outputting the filtered signal.

In some possible embodiments, the target frequency band is a frequency band having a frequency less than a ninth threshold.

In some possible embodiments, the target frequency band is a frequency band having a frequency greater than a tenth threshold.

In some possible embodiments, the target frequency band is a frequency band having a frequency less than an eleventh threshold and a frequency band having a frequency greater than a twelfth threshold; wherein the eleventh threshold is less than the twelfth threshold.

Referring to fig. 15, a schematic structural diagram of another signal processing apparatus is provided in an embodiment of the present application. As shown in fig. 15, the signal processing apparatus 150 may include: at least one processor 1501, at least one network interface 1504, a user interface 1503, a memory 1505, at least one communication bus 1502.

Wherein a communication bus 1502 is used to enable connected communications between these components.

The user interface 1503 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1503 may further include a standard wired interface and a standard wireless interface.

The network interface 1504 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 1501 may include one or more processing cores. The processor 1501 uses various interfaces and lines to connect various portions of the overall signal processing apparatus 150, and performs various functions of the signal processing apparatus 150 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1505, and invoking data stored in the memory 1505. Alternatively, the processor 1501 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1501 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1501 and may be implemented on a single chip.

The Memory 1505 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1505 includes a non-transitory computer-readable medium (non-transitory computer-readable storage medium). Memory 1505 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1505 may include a stored program region that may store instructions for implementing an operating system, instructions for at least one function (e.g., touch function, sound play function, image play function, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. Memory 1505 may also optionally be at least one memory device located remotely from the processor 1501. As shown in fig. 15, an operating system, a network communication module, a user interface module, and a signal processing application may be included in the memory 1505, which is one type of computer storage medium.

In the signal processing apparatus 150 shown in fig. 15, the user interface 1503 is mainly used as an interface for providing input for a user, and acquires data input by the user; and processor 1501 may be operative to invoke the signal processing application stored in memory 1505 and to perform the following operations in particular:

and under the condition that the filtering signals do not meet the preset conditions, determining the next frequency band to be filtered as the frequency band to be filtered according to a preset sequence, determining the number of target component layers according to the frequency band to be filtered, denoising the components positioned in the frequency band to be filtered in the target component layers by a wavelet threshold value to obtain processed filtering signals until the filtering signals corresponding to the last frequency band in the M frequency bands do not meet the preset conditions, and outputting the filtering signals corresponding to the last frequency band.

In some possible embodiments, the M frequency bands are contiguous.

In some possible embodiments, after the processor 1501 obtains the processed filtered signal, before outputting the filtered signal if the filtered signal meets a preset condition, the method is further used to perform: extracting a difference frequency signal in the filtered signal;

the preset condition is that the difference frequency signal is successfully extracted.

In some possible embodiments, m=2;

In some possible embodiments, the preset sequence is:

the first low-pass band and the first high-pass band; or alternatively

The first high-pass band and the first low-pass band.

In some possible embodiments, m=2;

In some possible embodiments, the preset sequence is:

the second low-pass band and the second high-pass band; or alternatively

The second high-pass band and the second low-pass band.

In some possible embodiments, m=3;

In some possible embodiments, the preset sequence is:

Embodiments of the present application also provide a computer-readable storage medium having instructions stored therein, which when executed on a computer or processor, cause the computer or processor to perform one or more of the steps of the embodiment shown in fig. 5 described above. The respective constituent modules of the above-described signal processing apparatus may be stored in the computer-readable storage medium if implemented in the form of software functional units and sold or used as independent products.

Referring to fig. 16, a schematic structural diagram of another signal processing apparatus is provided in an embodiment of the present application. As shown in fig. 16, the signal processing device 160 may include: at least one processor 1601, at least one network interface 1604, a user interface 1603, a memory 1605, at least one communication bus 1602.

Wherein a communication bus 1602 is used to enable connected communication between these components.

The user interface 1603 may include a Display screen (Display), a Camera (Camera), and the optional user interface 1603 may further include a standard wired interface, a wireless interface, among others.

The network interface 1604 may optionally comprise a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 1601 may include one or more processing cores. The processor 1601 connects various portions of the overall signal processing device 160 using various interfaces and lines, and performs various functions of the signal processing device 160 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1605, and invoking data stored in the memory 1605. Alternatively, the processor 1601 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1601 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1601 and may be implemented by a single chip.

The Memory 1605 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1605 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 1605 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1605 may include a stored program area that may store instructions for implementing an operating system, instructions for at least one function (e.g., a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc., and a stored data area; the storage data area may store data or the like referred to in the above respective method embodiments. Memory 1605 may also optionally be at least one storage device located remotely from the aforementioned processor 1601. As shown in fig. 16, an operating system, a network communication module, a user interface module, and a signal processing application program may be included in the memory 1605, which is one type of computer storage medium.

In the signal processing device 160 shown in fig. 16, the user interface 1603 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 1601 may be configured to invoke signal processing applications stored in memory 1605 and to specifically perform the following operations:

N-stage analysis is carried out on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the rest isProcessing the signal into a noisy signal;

outputting the filtered signal.

Embodiments of the present application also provide a computer-readable storage medium having instructions stored therein, which when executed on a computer or processor, cause the computer or processor to perform one or more of the steps of the embodiment shown in fig. 9, described above. The respective constituent modules of the above-described signal processing apparatus may be stored in the computer-readable storage medium if implemented in the form of software functional units and sold or used as independent products.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (Digital Subscriber Line, DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a digital versatile Disk (Digital Versatile Disc, DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.

Those skilled in the art will appreciate that implementing all or part of the above-described embodiment methods may be accomplished by way of a computer program, which may be stored in a computer-readable storage medium, instructing relevant hardware, and which, when executed, may comprise the embodiment methods as described above. And the aforementioned storage medium includes: a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk or an optical disk, or the like. The technical features in the present examples and embodiments may be arbitrarily combined without conflict.

The above-described embodiments are merely illustrative of the preferred embodiments of the present application and are not intended to limit the scope of the present application, and various modifications and improvements made by those skilled in the art to the technical solutions of the present application should fall within the protection scope defined by the claims of the present application without departing from the design spirit of the present application.

Claims

A signal processing method, comprising:

n-stage analysis is carried out on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

Determining a target component layer number according to a frequency band to be filtered, and denoising a component positioned in the frequency band to be filtered in the target component layer number by a wavelet threshold value to obtain a processed filter signal;

outputting the filtered signal.
The method of claim 1, wherein the target frequency band is a frequency band having a frequency less than a ninth threshold.
The method of claim 1, wherein the target frequency band is a frequency band having a frequency greater than a tenth threshold.
The method of claim 1, wherein the target frequency band is a frequency band having a frequency less than an eleventh threshold and a frequency band having a frequency greater than a twelfth threshold; wherein the eleventh threshold is less than the twelfth threshold.
A signal processing apparatus, comprising:

the second decomposition module is used for performing N-stage decomposition on the signal to be processed to obtain 2 ^N A component; wherein N is more than or equal to 2, and the signal to be processed is a signal with noise;

the second denoising module is used for determining the number of target component layers according to the frequency band to be filtered, and performing wavelet threshold denoising on the components of the target component layers, the components of which are positioned in the frequency band to be filtered, so as to obtain a processed filtering signal;

And the second output module is used for outputting the filtering signal.
The apparatus of claim 5, wherein the target frequency band is a frequency band having a frequency less than a ninth threshold.
The apparatus of claim 5, wherein the target frequency band is a frequency band having a frequency greater than a tenth threshold.
The apparatus of claim 5, wherein the target frequency band is a frequency band having a frequency less than an eleventh threshold and a frequency band having a frequency greater than a twelfth threshold; wherein the eleventh threshold is less than the twelfth threshold.
A signal processing apparatus, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by a processor and to perform the method steps of any of claims 1-4.
A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-4.