Signal reconstruction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a signal reconstruction method and device, electronic equipment and a storage medium, and belongs to the technical field of signal processing. The method comprises the steps that linear frequency modulation continuous waves are divided into transmitting signals and reference signals through a beam splitter, and the reference signals are sent into a frequency mixer; receiving the reflected emission signal, sending the emission signal into a frequency mixer for frequency mixing with a reference signal, and performing photoelectric conversion on an output signal of the frequency mixer through a photoelectric detector to obtain a difference frequency signal; sending the difference frequency signal into a frequency mixer to be mixed with the chirp sequence, sending the mixed signal into a sampler to be sampled at a low sampling rate, and obtaining sampling data; and recovering the sampling data through a reconstruction algorithm. According to the embodiment of the invention, signal reconstruction can be carried out by using a small amount of data, and the sampling rate of the data is reduced, so that the pressure of data acquisition, storage and transmission processes is reduced, and the real-time performance of information calculation is improved; and meanwhile, the influence of noise on target detection can be effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a signal reconstruction method and device, an electronic device and a storage medium. Background Art

[0002] The process of achieving autonomous driving for flying cars mainly includes obtaining environmental information, path search, and path optimization. Traditional ground vehicles can plan paths based on two-dimensional planes. If an emergency occurs, they can stop on the roadside or drive to the emergency lane. Flying cars have the process of flying in the air, which requires collecting data in more dimensions and planning paths based on three-dimensional maps. At the same time, it is necessary to focus on the distance between flying cars and high-rise buildings in the city and flight safety.

[0003] However, the massive amount of data collected while a car is driving or flying results in a long data processing time, affecting the real-time nature of information calculation.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to propose a signal reconstruction method and device, an electronic device and a storage medium, aiming to reduce the sampling rate of data and improve the real-time performance of information calculation.

[0006] To achieve the above object, an embodiment of the present application provides a signal reconstruction method, which includes the following steps:

[0007] Splitting the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sending the reference signal to a mixer;

[0008] receiving the reflected transmission signal, sending the transmission signal into a mixer to mix it with the reference signal, and performing photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal;

[0009] Sending the difference frequency signal into the mixer to mix with the chirp sequence, sending the mixed signal into the sampler for low sampling rate sampling to obtain sampling data;

[0010] The sampled data is restored through a reconstruction algorithm.

[0011] In some embodiments, the step of sending the difference frequency signal into the mixer to mix it with the chirp sequence, and sending the mixed signal into the sampler to perform low sampling rate sampling to obtain sampled data includes:

[0012] Sending the difference frequency signal into the mixer to mix with the chirp sequence;

[0013] The mixed signal enters a low-pass filter and a low-speed sampler for sampling. During the sampling process, a measurement matrix is ​​determined, and sampling data is obtained according to the measurement matrix.

[0014] In some embodiments, the method further comprises:

[0015] Get the sparse basis matrix.

[0016] In some embodiments, restoring the sampled data through a reconstruction algorithm includes:

[0017] Obtaining a perception matrix according to the measurement matrix and the sparse basis matrix;

[0018] The sampled data is used as an observation vector and processed with the perception matrix through a reconstruction algorithm to obtain an optimal estimate of the signal.

[0019] In some embodiments, restoring the sampled data through a reconstruction algorithm includes:

[0020] The sampled data is restored by a reconstruction algorithm of compressed sensing theory, wherein the reconstruction algorithm of compressed sensing theory includes any one of a basis pursuit algorithm, an iterative hard threshold algorithm, a regularized least squares algorithm, an iterative weighted least squares algorithm, a compressed sampling matching pursuit algorithm, a subspace pursuit algorithm, an orthogonal matching pursuit algorithm or a regularized adaptive matching pursuit algorithm.

[0021] In some embodiments, restoring the sampled data through a reconstruction algorithm includes:

[0022] Get the control value of the number of iterations and the threshold of stage transition;

[0023] A target atom is obtained according to the iteration number control amount, the stage conversion threshold and the sampling data, and the target atom is used to perform signal reconstruction.

[0024] In some embodiments, the method further comprises:

[0025] Get the linear frequency modulated continuous wave generated by the laser.

[0026] To achieve the above object, another aspect of an embodiment of the present application provides a signal reconstruction device, the device comprising:

[0027] A beam splitting module, used for splitting the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sending the reference signal to a mixer;

[0028] A mixing module, used for receiving the reflected transmission signal, sending the transmission signal into a mixer to mix it with the reference signal, and performing photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal;

[0029] A sampling module, used for sending the difference frequency signal into the mixer to mix it with the chirp sequence, and sending the mixed signal into the sampler for low sampling rate sampling to obtain sampling data;

[0030] The reconstruction module is used to restore the sampled data through a reconstruction algorithm.

[0031] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0032] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0033] The embodiments of the present application include at least the following beneficial effects: the present application provides a signal reconstruction method and device, an electronic device and a storage medium, which divides a linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sends the reference signal to a mixer; receives the reflected transmission signal, sends the transmission signal to a mixer for mixing with the reference signal, and performs photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal, which is beneficial to improving the effectiveness and signal-to-noise ratio of the signal and is helpful for target detection and analysis; sends the difference frequency signal to the mixer for mixing with a chirp sequence, and sends the mixed signal to a sampler for low sampling rate sampling to obtain sampling data, which is helpful to reduce the sampling rate of the data, improve processing efficiency, and improve the real-time nature of information calculation; restores the sampled data through a reconstruction algorithm, reconstructs the signal with a small amount of data, follows the compressed sensing theory, and effectively reconstructs the signal at a low sampling rate, reducing the pressure of the data acquisition, storage, and transmission process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of a signal reconstruction method provided by an embodiment of the present application;

[0035] Figure 2 yes Figure 1 Flow chart of step S103 in FIG.

[0036] Figure 3 yes Figure 1 Flow chart of step S104 in FIG.

[0037] Figure 4 yes Figure 1 Another flow chart of step S104 in ;

[0038] Figure 5 It is a flow chart of the signal reconstruction method provided in the embodiment of the present application applied to the analog information converter system of the laser radar;

[0039] Figure 6 is a schematic diagram of a beam splitter provided in an embodiment of the present application;

[0040] Figure 7 It is a time domain schematic diagram of a linear frequency modulation continuous wave provided in an embodiment of the present application;

[0041] Figure 8 It is a frequency domain schematic diagram of a linear frequency modulation continuous wave provided in an embodiment of the present application;

[0042] Fig. 9 is a schematic diagram of the structure of a signal reconstruction device provided in an embodiment of the present application;

[0043] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0045] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0046] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0048] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0049] 1) Compressed sensing (CS), also known as compressed sampling or sparse sampling, is a signal processing technique that aims to recover signals from a small number of measurements, especially when the signal is sparse or compressible. Compressed sensing theory breaks through the traditional Nyquist sampling theorem and allows signals to be sampled at a rate much lower than the Nyquist rate, thereby reducing the cost of data acquisition and storage.

[0050] 2) Regularized Adaptive Matching Pursuit (RAMP) is an improved sparse signal recovery algorithm that combines Matching Pursuit (MP) and regularization techniques to improve the accuracy and robustness of signal recovery. RAMP is particularly suitable for processing large-scale and complex signals.

[0051] 3) Atom: The "atom" of a matrix refers to a single value in the matrix.

[0052] The process of achieving autonomous driving for flying cars mainly includes obtaining environmental information, path search, and path optimization. Traditional ground vehicles can plan paths based on two-dimensional planes. If an emergency occurs, they can stop on the roadside or drive to the emergency lane. Flying cars have the process of flying in the air, which requires collecting data in more dimensions and planning paths based on three-dimensional maps. At the same time, it is necessary to focus on the distance between flying cars and high-rise buildings in the city and flight safety.

[0053] However, the massive amount of data collected while a car is driving or flying results in a long data processing time, affecting the real-time nature of information calculation.

[0054] In view of this, a signal reconstruction method, device, equipment and medium are provided in the embodiments of the present application. The scheme divides the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sends the reference signal to a mixer; receives the reflected transmission signal, sends the transmission signal to the mixer for mixing with the reference signal, and performs photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal, which is beneficial to improving the effectiveness and signal-to-noise ratio of the signal and is helpful for target detection and analysis; sends the difference frequency signal to the mixer for mixing with the chirp sequence, and sends the mixed signal to a sampler for low sampling rate sampling to obtain sampling data, which is helpful to reduce the sampling rate of the data, improve processing efficiency, and improve the real-time nature of information calculation; restores the sampled data through a reconstruction algorithm, reconstructs the signal with a small amount of data, follows the compressed sensing theory, and effectively reconstructs the signal at a low sampling rate, reducing the pressure of data acquisition, storage, and transmission processes.

[0055] The signal reconstruction method provided in the embodiment of the present application relates to the field of signal processing. The signal reconstruction method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in a blockchain network; the software can be an application that implements the signal reconstruction method, etc., but is not limited to the above forms.

[0056] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0057] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0058] Figure 1 is an optional flowchart of the signal reconstruction method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0059] Step S101, splitting the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sending the reference signal to a mixer.

[0060] Specifically, the transmission signal enters the free space and is transmitted to the target object through an antenna or a transmitter; the reference signal is sent to the mixer.

[0061] In some embodiments, an optical beam splitter is used to split the incident linear frequency modulated continuous wave signal into two paths: one path is used as a transmission signal, and the other path is used as a reference signal.

[0062] Furthermore, a multi-way beam splitter may be used to process multiple signal sources simultaneously.

[0063] Optionally, the linear frequency modulated continuous wave can be combined with a phase modulation technique to improve the signal's anti-interference capability.

[0064] In this embodiment, the linear frequency modulated continuous wave is divided into a transmission signal and a reference signal by a beam splitter, and the reference signal is sent to a mixer, which lays a foundation for subsequent signal processing and helps to improve the flexibility and reliability of the system.

[0065] Step S102, receiving the reflected transmission signal, sending the transmission signal to a mixer to mix it with a reference signal, and performing photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal.

[0066] In some embodiments, a transmit signal that is reflected off a target object is received.

[0067] Furthermore, the received transmission signal and the reference signal are mixed in a mixer to obtain a mixed signal (ie, an output signal of the mixer). Exemplarily, the mixing process includes generating a new frequency component by a multiplication operation.

[0068] Among them, adaptive mixing technology can be used to optimize mixing performance.

[0069] Optionally, the mixed signal (ie, the output signal of the mixer) is converted into an electrical signal by a photodetector to obtain a difference frequency signal. Exemplarily, the photodetector includes a photodiode.

[0070] In this embodiment, the reflected transmission signal is received, the transmission signal is sent to a mixer for mixing with a reference signal, and the output signal of the mixer is photoelectrically converted through a photodetector to obtain a difference frequency signal, which is beneficial to improving the effectiveness and signal-to-noise ratio of the signal, making subsequent processing more accurate and facilitating target detection and analysis.

[0071] Step S103, sending the difference frequency signal to a mixer to be mixed with the chirp sequence, sending the mixed signal to a sampler to be sampled at a low sampling rate to obtain sampled data.

[0072] In some embodiments, the chirp sequence generates a series of signals (ie, chirp signals) whose frequency varies over time.

[0073] Furthermore, the difference frequency signal and the chirp sequence are mixed again in a mixer to generate a remixed signal. It can be understood that the remixed signal is mainly composed of lower frequency components.

[0074] It should be noted that high-frequency noise can be removed by a low-pass filter or Fourier transform.

[0075] Optionally, high-frequency noise in the mixed signal is removed by a low-pass filter, and only low-frequency components are retained.

[0076] In other embodiments, the signal is decomposed into low-frequency components by performing Fourier transform on the remixed signal. For example, in the frequency domain, a cutoff frequency can be set, components above the cutoff frequency can be set to zero, and then the signal can be restored by inverse Fourier transform to remove high-frequency noise.

[0077] It is understandable that the present application does not limit the sampler. For example, the sampler may be a random modulation sampler, a non-uniform sampler, or a low-speed sampler.

[0078] Optionally, a low-speed sampler is used to sample the signal, and signal data is acquired at a lower rate to obtain sampled data.

[0079] It should be noted that the difference frequency signal is a sparse signal, which conforms to the compressed sensing theory.

[0080] In this embodiment, the difference frequency signal is sent to a mixer to be mixed with a chirp sequence, and the mixed signal is sent to a sampler for low sampling rate sampling to obtain sampled data. This can effectively extract characteristic information from the signal, reduce the amount of data, and improve processing efficiency. At the same time, it follows the compressed sensing theory to effectively reconstruct the signal at a low sampling rate.

[0081] Step S104: restore the sampled data through a reconstruction algorithm.

[0082] In some embodiments, the sparsity of the signal is utilized and a reconstruction algorithm based on compressed sensing theory is applied to reconstruct the original signal from the sampled data.

[0083] The present application does not make any specific limitation on the reconstruction algorithm, and it can be flexibly selected in combination with the actual reconstruction needs. For example, the reconstruction algorithm can adopt the L1 norm optimization algorithm or the matching pursuit algorithm.

[0084] In this embodiment, the sampled data is restored through a reconstruction algorithm, which can efficiently restore the original signal at a low sampling rate, improve the accuracy and reliability of signal processing, and enhance the performance of the system.

[0085] In the steps S101 to S104 shown in the embodiment of the present application, a linear frequency modulated continuous wave is divided into a transmission signal and a reference signal through a beam splitter, and the reference signal is sent to a mixer; the reflected transmission signal is received, the transmission signal is sent to a mixer for mixing with the reference signal, and the output signal of the mixer is photoelectrically converted through a photodetector to obtain a difference frequency signal, which is beneficial to improving the effectiveness and signal-to-noise ratio of the signal and is helpful for target detection and analysis; the difference frequency signal is sent to the mixer for mixing with the chirp sequence, and the mixed signal is sent to a sampler for low sampling rate sampling to obtain sampled data, which is helpful to reduce the sampling rate of the data, improve processing efficiency, and improve the real-time nature of information calculation; the sampled data is restored through a reconstruction algorithm, and a small amount of data is used to reconstruct the signal. In accordance with the compressed sensing theory, the signal is effectively reconstructed at a low sampling rate, reducing the pressure of the data acquisition, storage, and transmission process.

[0086] See also Figure 2 In some embodiments, step S103 may include but is not limited to steps S201 to S202:

[0087] Step S201: Send the difference frequency signal to a mixer to be mixed with the chirp sequence.

[0088] In some embodiments, the difference frequency signal and the chirp sequence are input to a mixer, which multiplies the difference frequency signal and the chirp signal to generate a mixed signal. Alternatively, a radio frequency (RF) mixer, such as a diode mixer or an integrated circuit, may be used.

[0089] The difference frequency signal is sent to a mixer to be mixed with the chirp sequence.

[0090] In some embodiments, the difference frequency signal is a sparse signal. In the theory of compressed sensing, the signal to be measured x (ie, the difference frequency signal) needs to be a sparse signal, that is, x can be expressed by the following formula:

[0091] X=Ψc

[0092] Among them, Ψ is a sparse basis, and c is the coefficient vector corresponding to x under the sparse basis.

[0093] It can be understood that, through the sparse basis matrix, the original signal can be converted into a k-sparse signal with at most k non-zero elements, and the remaining element values ​​are 0 or replaced by 0.

[0094] It should be noted that in compressed sensing, the measurement matrix needs to be as uncorrelated as possible with the sparse basis. In the operation of the mixer, the characteristics of the chirp sequence (such as the distribution of its spectrum) can be regarded as one of the factors constituting the measurement matrix. If the selected chirp sequence is random or uncorrelated, it is conducive to meeting the requirements of compressed sensing.

[0095] Step S202: the mixed signal enters a low-pass filter and a low-speed sampler for sampling, a measurement matrix is ​​determined during the sampling process, and sampling data is obtained according to the measurement matrix.

[0096] Specifically, the measurement matrix is ​​defined during the sampling process. The measurement matrix is ​​used to extract discrete data from a continuous signal. The measurement matrix is ​​constructed before sampling and applied during the sampling process.

[0097] It should be noted that in compressed sensing, sparse representation of signals is the basis. After the measurement matrix is ​​used, the low-dimensional observation value obtained is a reduced-dimensional observation (ie, observation vector) of the original signal (ie, difference frequency signal).

[0098] In step S202 of some embodiments, the mixed signal is input into a low-pass filter to filter out high-frequency noise and unnecessary frequency components, and the low-pass filtered signal is input into a sampler for analog-to-digital conversion to generate discrete sampling data.

[0099] In some embodiments, a suitable basis function is selected and a measurement matrix is ​​constructed by the selected basis function.

[0100] It can be understood that determining the measurement matrix may mean selecting and applying the constructed measurement matrix, wherein the construction of the measurement matrix needs to meet the requirement of being as unrelated to the sparse basis as possible, and a random matrix is ​​usually selected and applied.

[0101] It should be noted that in compressed sensing theory, if the signal to be measured x (length is N) is sparse in a sparse representation space, c is the sparse vector of x in Ψ. Then after the measurement matrix Φ (M×N matrix) is used for dimensionality reduction observation, the vector length can be obtained to be M (M<<N), and the expression of the vector is:

[0102] y=Ax=ΦΨx

[0103] Among them, y is the observation vector (i.e., sampling data), x is the difference frequency signal, Φ is the measurement matrix, Ψ is the sparse basis, and A is the perception matrix.

[0104] See also Figure 3 In some embodiments, step S104 may include but is not limited to steps S301 to S302:

[0105] Step S301, obtaining a perception matrix according to a measurement matrix and a sparse basis matrix.

[0106] Specifically, the sensing matrix is ​​used in signal reconstruction to map the signal into the observation space.

[0107] In step S301 of some embodiments, a suitable measurement matrix is ​​selected, a sparse basis suitable for the signal is selected, and a sparse basis matrix is ​​determined.

[0108] Furthermore, the measurement matrix and the sparse basis matrix are combined through matrix multiplication to form a perception matrix.

[0109] In this embodiment, the perception matrix is ​​obtained according to the measurement matrix and the sparse basis matrix. Through the effective measurement matrix, the main features of the signal can be captured with fewer sampling points to achieve data compression. By optimizing the sparse basis selection, the reconstruction quality of the signal can be improved, making the final signal estimation closer to the real signal.

[0110] Step S302: The sampled data is used as an observation vector and processed with the perception matrix through a reconstruction algorithm to obtain the best estimate of the signal.

[0111] In step S302 of some embodiments, the observation vector and the perception matrix are input into a reconstruction algorithm. Exemplarily, the reconstruction algorithm of the compressed sensing theory includes any one of the algorithms such as basis pursuit algorithm, iterative hard threshold algorithm, regularized least squares algorithm, iterative weighted least squares algorithm, compressed sampling matching pursuit algorithm, subspace pursuit algorithm, orthogonal matching pursuit algorithm or regularized adaptive matching pursuit algorithm.

[0112] Optionally, the reconstruction accuracy is further improved by updating the signal estimate and the residual through multiple iterations.

[0113] It will be appreciated that the best estimate of the signal is the estimate of the difference frequency signal.

[0114] In this embodiment, the sampled data is used as the observation vector and processed with the perception matrix through a reconstruction algorithm to obtain the best estimate of the signal, and a high-quality signal is restored from the compressed observation data. At the same time, it can significantly reduce the computational complexity and improve the real-time performance of information calculation during large-scale data processing.

[0115] See also Figure 4 In some embodiments, step S104 may include but is not limited to steps S401 to S402:

[0116] Step S401, obtaining the control amount of the number of iterations and the threshold value of the phase transition.

[0117] In step S401 of some embodiments, a preset iteration number control amount is obtained or an iteration number control amount is set, wherein the iteration number control amount is determined by the complexity of the signal, the requirements of the system and the experience of professionals.

[0118] Optionally, a threshold for phase transition is determined based on signal characteristics during reconstruction, and when the residual or error is less than the threshold, phase transition can be triggered, indicating that the reconstruction process needs to be adjusted. It is understandable that the threshold for phase transition can be predetermined or set in real time.

[0119] Furthermore, during the operation of the algorithm, the signal characteristics are monitored in real time, and the control amount of the number of iterations and the threshold of the stage transition can be dynamically adjusted.

[0120] In this embodiment, obtaining the iteration number control amount and the threshold value of the phase transition is beneficial to improving the efficiency of signal reconstruction through iterative control and improving the robustness of reconstruction.

[0121] Step S402, obtaining target atoms according to the iteration number control amount, the stage conversion threshold and the sampling data, and performing signal reconstruction using the target atoms.

[0122] In step S402 of some embodiments, based on the sampled data, combined with the control amount of the number of iterations and the threshold of the phase transition, signal analysis is performed to extract the target atoms.

[0123] Furthermore, the extracted target atoms are used to reconstruct an approximate value of the signal through a corresponding signal reconstruction algorithm. Exemplarily, the reconstruction algorithm may be a regularized adaptive matching pursuit algorithm.

[0124] It can be understood that the reconstruction algorithm gradually selects the atoms most relevant to the residual and controls the number of selected atoms through regularization conditions, thereby achieving effective recovery of the signal.

[0125] In this embodiment, target atoms are obtained according to the control amount of the number of iterations, the threshold of the stage conversion and the sampling data, and the target atoms are used to reconstruct the signal, which is conducive to restoring the original signal and improving the accuracy and quality of reconstruction.

[0126] Figure 5 is an optional flow chart of the signal reconstruction method provided in the embodiment of the present application applied to the analog information converter system of the laser radar. Figure 5 The method may include but is not limited to the following steps.

[0127] Step 1: The laser generates a linear frequency modulated continuous wave, which is divided into two paths after passing through a beam splitter. One path enters the free space as a transmission signal, and the other path enters the mixer as a reference signal.

[0128] For example, the beam splitter diagram is as follows Figure 6 As shown, signal 1 is a linear frequency modulated continuous wave, and signal 2 and signal 3 are signals divided into two paths by a beam splitter.

[0129] Specifically, the time domain schematic diagram of linear frequency modulation continuous wave is as follows Figure 7 As shown in FIG. 1 , a linear frequency modulated continuous wave (FMCW signal) varies with time. As time goes by, the frequency of the signal gradually increases.

[0130] Furthermore, the frequency domain schematic diagram of the linear frequency modulation continuous wave is as follows Figure 8 As shown, fc represents the center frequency, which is 77 GHz in the figure, B represents the scanning bandwidth, which is 4 GHz in the figure, Tc represents the scanning period, which is 40 μs in the figure, and S represents the speed at which the frequency changes with time.

[0131] The transmission signal is transmitted to the target object through the collimator, and the reflected transmission signal is received by the collimator.

[0132] Step 2: The transmitted signal is reflected after encountering a target or obstacle, and after being received by the radar, it enters the mixer for mixing and photoelectric conversion with the reference signal to obtain a difference frequency signal;

[0133] The transmitted signal and the reference signal pass through a mixer and a photodetector.

[0134] Step 3: The difference frequency signal enters the mixer, is mixed with the chirp sequence, and then enters the low-pass filter and the low-speed sampler for sampling.

[0135] It is understandable that the prerequisite for applying the compressive sensing theory is that the signal to be measured is a sparse signal. In this application, the signal to be measured is a difference frequency signal.

[0136] In the compressive sensing theory, the signal x to be measured needs to be a sparse signal, that is, x can be expressed by the following formula:

[0137] x = Ψc

[0138] Where x is the difference frequency signal, Ψ is the sparse basis, and c is the coefficient vector corresponding to x under this sparse basis. Through the sparse basis matrix, the original signal (i.e., the difference frequency signal) can be transformed into a k-sparse signal with at most k non-zero elements, and the remaining element values are 0 or replaced by 0.

[0139] Furthermore, the compressive sensing theory includes: If the signal x to be measured (with length N) is sparse in a certain sparse representation space, and c is the sparse vector of x in Ψ. Then after dimensionality reduction observation by the measurement matrix Φ (a matrix of M×N), a vector y can be obtained, with length M (M << N), and the expression of vector y is as follows:

[0140] y = Ax = ΦΨx

[0141] Where y is the observation vector (i.e., the sampled data), A is the sensing matrix, x is the difference frequency signal, Ψ is the sparse basis, and Φ is the measurement matrix.

[0142] Specifically, the construction of the measurement matrix needs to meet the requirement of being as uncorrelated as possible with the sparse basis, and usually a random matrix is used.

[0143] It is understandable that under the condition of knowing y and A, the best estimate of x can be obtained according to the reconstruction algorithm.

[0144] Step 4, the sampled data is restored through the reconstruction algorithm.

[0145] In some embodiments, the regularized adaptive matching pursuit (RAMP) algorithm is selected.

[0146] Specifically, the input signals of the RAMP algorithm include the observation vector y and the sensing matrix A = φψ. The output signals of the RAMP algorithm are the best estimate of the signal and the residual r.

[0147] It should be noted that the steps of the RAMP algorithm are:

[0148] (1) Initialize r0 = y, the number of iterations p = 1, the initial step size size ≠ 0, and the index value set A p represents the set of columns of matrix A selected according to the index A p and the stage = 1.

[0149] (2) If ‖r‖2≤ε1, stop the iteration and use the obtained atom (i.e., the target atom) to reconstruct the signal, otherwise go to step (3).

[0150] (3) Find the inner product of the measurement matrix Φ and the residual r, compare the absolute values ​​of the inner products, and sort them in descending order. Select size maximum values ​​and store the corresponding index values ​​in J (J is the set of these column numbers).

[0151] The absolute value of the inner product between r and each atom in the measurement matrix Φ is calculated as follows:

[0152] u={u j |u j =| <r,φ j >,j=1,2,...,N|}

[0153] Among them, u represents the correlation coefficient, u j represents the absolute value of the inner product between r and the jth atom in the measurement matrix Φ, represents the jth atom in the measurement matrix Φ, and r represents the residual.

[0154] It can be understood that the inner product of the residual r and each atom in the measurement matrix Φ is calculated, the absolute values ​​are obtained and arranged in descending order, the size inner products with the largest absolute values ​​are selected, and the corresponding column indexes are recorded in the set J.

[0155] (4) Select atoms according to the regularization rule: Find the subset J0 in J. (J0 represents the column number found in each iteration), and find J0 with the maximum energy among all subsets that meet the requirements. Regularize the atomic correlation coefficient corresponding to the index value in J, and store the regularization result in J0. The atomic correlation coefficient of this set must satisfy the following expression:

[0156] |u(i)≤2|u(j)||,for all i,j∈J0

[0157] Among them, u(i) represents the collective atomic correlation coefficient, u j represents the absolute value of the inner product between r and the jth atom in the measurement matrix Φ, represents the jth atom in the measurement matrix Φ.

[0158] It can be understood that the inner product of the residual and each column of the measurement matrix is ​​obtained. There are N columns in total, that is, there are N inner products. The regularization process is to select the group whose maximum absolute value of the inner product of each column vector and the residual is not more than 2 times greater than the minimum value and has the largest energy. Generally, there is not only one column.

[0159] Specifically, in the set J, a subset that satisfies the regularization condition is sought, which makes the correlation coefficients of atoms in the set relatively uniform and avoids over-selection of certain atoms.

[0160] (5) Store the selected column number, i.e., Λ p =Λ p-1 ∪J0; stores the selected column, i.e. A p =A p-1 ∪a j , where a j Represents the j-th column of matrix A (allj∈J0).

[0161] Specifically, update the index and store the newly selected column index into Λ p =Λ p-1 ∪J0. Update the matrix, update the selected column, and get A p =A p-1 ∪a j .

[0162] (6) Find the best estimate: According to y = Ax, we know that y and A p , find y = A p x p The expression of the least squares solution of is as follows:

[0163]

[0164] in, is the best estimate, y is the observation vector, A p is the set of columns of matrix A selected by index Λ_p.

[0165] At the same time, the residual is updated:

[0166]

[0167] Among them, r p is the updated residual, A p is the set of columns of matrix A selected by index Λ_p, is the best estimate.

[0168] (7) If || r p -r p-1 ||≤ε2, let stage=stage+1, size=size·stage, and go to step (3); otherwise let r=r p , p=p+1, go to step (2).

[0169] Specifically, check the new residual r p and the last residual r p-1 The difference between ||r p -rp-1 || Is it less than or equal to the threshold ε2. If the condition is met, enter the new stage stage = stage + 1 and increase size; otherwise, update the residual r = r p , the number of iterations increases by p=p+1, and returns to step (2).

[0170] Among them, ε1 and ε2 are the control value of the number of iterations and the threshold of stage transition respectively. By changing these two values, higher reconstruction accuracy and control of stage transition can be achieved. When the residual is within the threshold range set by the user, it can be determined that the original signal can be accurately reconstructed.

[0171] In this embodiment, the compressed sensing theory is introduced into the data collection of the flying car sensor network, which can break through the Nyquist sampling theory, greatly reduce the data sampling rate, and accurately restore the original signal. At the same time, it can reduce the pressure of data transmission and storage. Using the compressed sensing theory, the sampling equipment can be simplified and the cost and complexity of the sampling equipment can be reduced.

[0172] In this embodiment, the speed and quality of target distance measurement and imaging are improved. By using the theory of compressed sensing, the number and frequency of sampling can be reduced, and the speed of measuring target information can be accelerated. The algorithm only reconstructs key information and can ignore the influence of noise signals on the spectrum or imaging.

[0173] It is understandable that the present application is mainly aimed at sensor networks, especially the data collection, storage and analysis process of lidar, and adopts an analog information converter system to reduce the sampling frequency.

[0174] Furthermore, the present application adopts the RAMP algorithm, which can adaptively estimate the sparsity of data and adjust the step size of the algorithm, while improving the accuracy of signal reconstruction and accelerating the speed of calculation.

[0175] See also Fig. 9 The embodiment of the present application further provides a signal reconstruction device, which can implement the above-mentioned signal reconstruction method, and the device includes:

[0176] A beam splitting module 901 is used to split the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and send the reference signal to a mixer;

[0177] The mixing module 902 is used to receive the reflected transmission signal, send the transmission signal to the mixer to mix it with the reference signal, and perform photoelectric conversion on the output signal of the mixer through a photoelectric detector to obtain a difference frequency signal;

[0178] The sampling module 903 is used to send the difference frequency signal to the mixer to mix it with the chirp sequence, and send the mixed signal to the sampler for low sampling rate sampling to obtain sampling data;

[0179] The reconstruction module 904 is used to restore the sampled data through a reconstruction algorithm.

[0180] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0181] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above signal reconstruction method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0182] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0183] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0184] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0185] The memory 1002 may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 may store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the signal reconstruction method of the embodiment of this application;

[0186] Input / output interface 1003, used to implement information input and output;

[0187] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0188] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0189] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0190] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned signal reconstruction method is implemented.

[0191] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0192] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0193] The signal reconstruction method, signal reconstruction device, electronic device and storage medium provided in the embodiments of the present application are as follows: a linear frequency modulated continuous wave is divided into a transmission signal and a reference signal through a beam splitter, and the reference signal is sent to a mixer; a reflected transmission signal is received, the transmission signal is sent to a mixer for mixing with the reference signal, and the output signal of the mixer is photoelectrically converted through a photodetector to obtain a difference frequency signal, which is beneficial to improving the effectiveness and signal-to-noise ratio of the signal and is helpful for target detection and analysis; the difference frequency signal is sent to a mixer for mixing with a chirp sequence, and the mixed signal is sent to a sampler for low sampling rate sampling to obtain sampling data, which is helpful to reduce the sampling rate of the data, improve processing efficiency, and improve the real-time nature of information calculation; the sampled data is restored through a reconstruction algorithm, and a small amount of data is used to reconstruct the signal, following the compressed sensing theory, so that the signal is effectively reconstructed at a low sampling rate, reducing the pressure of the data acquisition, storage, and transmission process.

[0194] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0195] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0196] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0197] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0198] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0199] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0200] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0201] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0202] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0203] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0204] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A signal reconstruction method, characterized in that: The method comprises the following steps: Splitting the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sending the reference signal to a mixer; receiving the reflected transmission signal, sending the transmission signal into a mixer to mix it with the reference signal, and performing photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal; Sending the difference frequency signal into the mixer to mix with the chirp sequence, sending the mixed signal into the sampler for low sampling rate sampling to obtain sampling data; The sampled data is restored through a reconstruction algorithm.

2. The method according to claim 1, characterized in that The step of sending the difference frequency signal into the mixer to mix it with the chirp sequence, and sending the mixed signal into the sampler to perform low sampling rate sampling to obtain sampling data includes: Sending the difference frequency signal into the mixer to mix with the chirp sequence; The mixed signal enters a low-pass filter and a low-speed sampler for sampling. During the sampling process, a measurement matrix is ​​determined, and sampling data is obtained according to the measurement matrix.

3. The method according to claim 2, characterized in that The method further comprises: Get the sparse basis matrix.

4. The method according to claim 3, characterized in that The restoring the sampled data by a reconstruction algorithm comprises: Obtaining a perception matrix according to the measurement matrix and the sparse basis matrix; The sampled data is used as an observation vector and processed with the perception matrix through a reconstruction algorithm to obtain an optimal estimate of the signal.

5. The method according to claim 1, characterized in that The restoring the sampled data by a reconstruction algorithm comprises: The sampled data is restored by a reconstruction algorithm of compressed sensing theory, wherein the reconstruction algorithm of compressed sensing theory includes any one of a basis pursuit algorithm, an iterative hard threshold algorithm, a regularized least squares algorithm, an iterative weighted least squares algorithm, a compressed sampling matching pursuit algorithm, a subspace pursuit algorithm, an orthogonal matching pursuit algorithm or a regularized adaptive matching pursuit algorithm.

6. The method according to claim 1, characterized in that The restoring the sampled data by a reconstruction algorithm comprises: Get the control value of the number of iterations and the threshold of stage transition; A target atom is obtained according to the iteration number control amount, the stage conversion threshold and the sampling data, and the target atom is used to perform signal reconstruction.

7. The method according to claim 1, characterized in that The method further comprises: Get the linear frequency modulated continuous wave generated by the laser.

8. A signal reconstruction device, characterized in that: The device comprises: A beam splitting module, used for splitting the linear frequency modulated continuous wave into a transmission signal and a reference signal through a beam splitter, and sending the reference signal to a mixer; A mixing module, used for receiving the reflected transmission signal, sending the transmission signal into a mixer to mix it with the reference signal, and performing photoelectric conversion on the output signal of the mixer through a photodetector to obtain a difference frequency signal; A sampling module, used for sending the difference frequency signal into the mixer to mix it with the chirp sequence, and sending the mixed signal into the sampler for low sampling rate sampling to obtain sampling data; The reconstruction module is used to restore the sampled data through a reconstruction algorithm.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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