Beat frequency signal analysis methods, apparatus, electronic devices and computer-readable storage media

Abstract

This invention provides a beat frequency signal analysis method, apparatus, electronic device, and computer-readable storage medium, relating to the field of lidar technology. The method includes: performing multi-channel delay and multiplexing processing on the signal to be processed to obtain multiple beat frequency signals with delay differences between channels and cascaded channels. Windowing is applied to the beat frequency signal of each channel, and an FFT operation is performed on each windowed beat frequency signal to obtain multiple FFT result data streams. Then, based on each FFT result data stream, the channel result corresponding to the beat frequency signal of each channel is analyzed, and the channel results are processed according to the delay timing relationship to obtain the time-frequency data of the signal to be processed, thereby achieving reliable and comprehensive analysis of the beat frequency signal.

Description

Technical Field

[0001] This invention relates to the field of lidar technology, and more specifically, to a beat frequency signal analysis method, apparatus, electronic device, and computer-readable storage medium. Background Art

[0002] Frequency Modulated Continuous Wave (FMCW) lidar is a type of lidar that emits light continuously with a periodically changing optical carrier frequency. Through coherent detection, the reflected light from the target and the emitted laser light coherently generate a beat frequency signal. The beat frequency signal is characterized by the modulation frequency difference introduced by the target distance delay and the Doppler frequency difference introduced by the relative velocity. By measuring the beat frequency signal, the target's distance and velocity information can be demodulated. Current research primarily uses the Direct Fast Fourier Transform (FFT) to perform signal spectrum analysis on the beat frequency signal, but the analysis results are limited and cannot meet application requirements. Summary of the Invention

[0003] One of the objectives of this invention includes, for example, providing a beat frequency signal analysis method, apparatus, electronic device, and computer-readable storage medium to at least partially improve the comprehensiveness of beat frequency signal analysis results and meet application requirements.

[0004] The embodiments of the present invention can be implemented as follows:

[0005] In a first aspect, embodiments of the present invention provide a beat frequency signal analysis method, comprising:

[0006] The signal to be processed is subjected to multi-channel delay and multiplexing to obtain multiple beat frequency signals with delay differences in each channel and cascaded channels.

[0007] Windowing is applied to the beat frequency signal of each channel;

[0008] An FFT operation is performed on each beat frequency signal after windowing to obtain multiple FFT operation result data streams; wherein, each FFT operation result data stream contains the real part and imaginary part of the frequency domain amplitude of each beat frequency signal after windowing, as well as the corresponding frequency index value in time sequence;

[0009] Based on the data stream of each FFT operation result, the channel result corresponding to the beat frequency signal of each channel is obtained by analysis;

[0010] The results of each channel are processed according to the delay sequence relationship to obtain the time-frequency data of the signal to be processed.

[0011] In an optional implementation, the method further includes the step of obtaining the signal to be processed, which includes:

[0012] The beat frequency signal is obtained by coherently beating the emitted light signal and the reflected light signal of the FMCW lidar; wherein, the emitted light signal is the frequency-modulated continuous wave laser signal emitted by the FMCW lidar, and the reflected light signal is the light signal received by the FMCW lidar and reflected after the frequency-modulated continuous wave laser reaches the set target;

[0013] The intermediate frequency digital signal obtained by analog-to-digital conversion of the beat frequency signal is used as the signal to be processed.

[0014] In an optional implementation, the step of performing multi-channel delay and multiplexing processing on the signal to be processed to obtain multiple beat frequency signals with delay differences in each channel and cascaded among the channels includes:

[0015] The signal to be processed is input into the FPGA, and based on the multi-channel parallel dual-port Block RAM in the FPGA, multiple beat frequency signals with stepped timing delay difference and parallel operation of each channel are output.

[0016] The dual-port Block RAM has two ports with different operating addresses, and the outputs of the two ports have a specified delay difference based on the different operating addresses.

[0017] In an optional implementation, the step of windowing the beat frequency signal of each channel includes:

[0018] Generate the data sequence for the Hamming window;

[0019] After adjusting the data bit width of the data sequence to a specified bit width, it is used as the initialization file for the ROM within the FPGA;

[0020] The ROM is read cyclically in the FPGA, and the output data of the ROM is used as a Hamming window data stream;

[0021] The Hamming window data streams are multiplied by the beat frequency signals of each channel to obtain the beat frequency signal of each channel after windowing.

[0022] In an optional implementation, the step of performing an FFT operation on each beat frequency signal after windowing processing to obtain multiple FFT operation result data streams includes:

[0023] For the beat frequency signal of each channel after windowing processing, based on the multiplier added to the corresponding channel in the FPGA, the square of the real part and the square of the imaginary part of the frequency domain amplitude of the beat frequency signal are calculated respectively.

[0024] The squares of the real part signal and the imaginary part signal are added together to obtain the squared magnitude of the beat frequency signal of each channel after windowing.

[0025] The step of analyzing and obtaining the channel result corresponding to the beat frequency signal of each channel based on the data stream of each FFT operation result includes:

[0026] Peak detection is performed on the squared values ​​of each modulus to obtain the maximum frequency domain amplitude and the maximum frequency index corresponding to each channel.

[0027] The step of processing the results of each channel according to the delay sequence relationship to obtain the time-frequency data of the signal to be processed includes:

[0028] The maximum frequency domain amplitude and the maximum frequency index corresponding to each channel are serially converted according to the delay timing relationship to obtain a one-dimensional frequency index value sequence.

[0029] In an optional implementation, the FPGA is provided with an incrementing cyclic counter and a state machine including the same number of states as the number of channels, wherein each state of the state machine corresponds to each channel arranged in a time sequence.

[0030] The step of serially converting the maximum frequency domain amplitude and the maximum frequency index corresponding to each channel according to the delay timing relationship to obtain a one-dimensional frequency index value sequence includes:

[0031] When the counter is at its maximum value, state transition is performed based on the state machine, and the maximum frequency index value corresponding to the channel corresponding to the current state is taken to obtain a one-dimensional serial frequency index value sequence.

[0032] The beat frequency is calculated based on the FFT points and the running clock frequency.

[0033] Based on the beat frequency, the one-dimensional serial frequency index value sequence is converted into a time-frequency curve.

[0034] In an optional implementation, when the target is stationary, the method further includes calculating the distance between the FMCW lidar and the target according to the following formula:

[0035] d = cTf iF / (4BW)

[0036] Among them, f iF BW represents the stable frequency in the time-frequency data; T represents the frequency modulation bandwidth range of the transmitted optical signal; and c represents the frequency modulation period of the transmitted optical signal.

[0037] When the set target is in motion, the method further includes calculating the motion speed of the set target according to the following formula:

[0038] fd = 2vf / c

[0039] Where fd is the Doppler frequency obtained from the time-frequency data analysis; v is the velocity of the target; f is the laser carrier frequency; and c is the speed of light.

[0040] Secondly, embodiments of the present invention provide a beat frequency signal analysis device, comprising:

[0041] The signal processing module is used to perform multi-channel delay and multiplexing processing on the signal to be processed, to obtain multiple beat frequency signals with delay differences in each channel and cascaded channels, and to perform windowing processing on the beat frequency signal of each channel.

[0042] The signal analysis module is used to perform FFT operation on each beat frequency signal after windowing processing to obtain multiple FFT operation result data streams; based on each FFT operation result data stream, analyze to obtain the channel result corresponding to the beat frequency signal of each channel; process each channel result according to the delay timing relationship to obtain the time-frequency data of the signal to be processed;

[0043] Each FFT operation result data stream contains the real and imaginary part of the frequency domain amplitude of each beat frequency signal after windowing processing, as well as the corresponding frequency index value in time.

[0044] Thirdly, the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the beat frequency signal analysis method described in any of the foregoing embodiments.

[0045] Fourthly, the present invention provides a computer-readable storage medium comprising a computer program, wherein the computer program, when executed, controls the electronic device in which the computer-readable storage medium is located to perform the beat frequency signal analysis method described in any of the foregoing embodiments.

[0046] The beneficial effects of the embodiments of the present invention include, for example, the analysis of time-frequency data of beat frequency signals is realized through a clever processing flow, which improves the comprehensiveness of beat frequency signal analysis results to meet various application requirements, such as providing data support for distance measurement, speed measurement, etc. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 The diagram illustrates an application scenario provided by an embodiment of the present invention.

[0049] Figure 2 The diagram shows a flowchart of a beat frequency signal analysis method provided by an embodiment of the present invention.

[0050] Figure 3 This diagram illustrates another flowchart of a beat frequency signal analysis method provided by an embodiment of the present invention.

[0051] Figure 4 The diagram illustrates a logic block diagram of a multi-channel delay multiplexing method provided by an embodiment of the present invention.

[0052] Figure 5 The diagram illustrates a logic block diagram of multiplying a beat frequency signal with a window function according to an embodiment of the present invention.

[0053] Figure 6 The diagram shows a logic block diagram of a multi-channel beat frequency signal FFT frequency measurement according to an embodiment of the present invention.

[0054] Figure 7 The diagram illustrates a logic block diagram for frequency domain amplitude modulo calculation provided by an embodiment of the present invention.

[0055] Figure 8 The diagram illustrates a logic block diagram of peak search after frequency domain amplitude modulo taking provided by an embodiment of the present invention.

[0056] Figure 9 This diagram illustrates a logic block diagram of a serial conversion based on the timing relationship between channels, provided by an embodiment of the present invention.

[0057] Figure 10 This diagram illustrates another flowchart of a beat frequency signal analysis method provided by an embodiment of the present invention.

[0058] Figure 11 This invention illustrates a coherent beat frequency model of emitted light and reflected light from a relatively stationary target, provided by an embodiment of the present invention.

[0059] Figure 12 This invention illustrates a coherent beat frequency model of emitted light and reflected light from a relatively moving target, provided by an embodiment of the present invention.

[0060] Figure 13 An exemplary structural block diagram of a beat frequency signal analysis device provided in an embodiment of the present invention is shown.

[0061] Icons: 100 - Electronic device; 110 - Memory; 120 - Processor; 130 - Communication module; 140 - Beat frequency signal analysis device; 141 - Signal processing module; 142 - Signal analysis module. Detailed Implementation

[0062] Compared to time-of-flight (TOF) lidar, FMCW lidar boasts advantages such as strong anti-interference capabilities, high detection sensitivity, and real-time speed measurement, which can promote the development of next-generation autonomous driving technology. Therefore, real-time and accurate measurement and analysis of beat frequency signals are of great significance for improving the performance of FMCW lidar.

[0063] The main method currently used for beat frequency signal measurement and analysis is the direct Fast Fourier Transform (FFT). The direct FFT method is a widely used signal spectrum analysis method that can effectively measure the signal spectrum in a large number of application scenarios. The basic principle is to extract a segment of the signal and perform a Fast Fourier Transform to obtain all the frequency components in the extracted signal.

[0064] Research has revealed that the direct FFT method can only acquire the overall frequency domain information of the captured signal, but cannot obtain the time information of each frequency component within the signal. However, the frequency domain information of the beat frequency signal generated by the received and emitted light of an FMCW lidar changes over time, and this trend is crucial for analyzing the beat frequency signal. In other words, measuring and analyzing the beat frequency signal requires considering both its time and frequency domain information. Therefore, the direct FFT method has certain limitations in analyzing beat frequency signals and cannot meet application requirements, such as providing reliable support for FMCW lidar's ranging and velocity measurement functions.

[0065] Therefore, for FMCW lidar beat frequency signals, how to ensure that the time domain characteristics are recorded while measuring and analyzing the frequency domain characteristics of the beat frequency signals, so as to provide reliable parameter support for the realization of FMCW lidar ranging, velocity measurement and other functions, is a problem that needs to be solved.

[0066] Based on the above research, this invention provides a beat frequency signal analysis scheme, which performs multi-channel delay multiplexing on the beat frequency signal, processes each channel separately, and analyzes the time-frequency information of the beat frequency signal, such as the time-frequency curve, thereby providing reliable parameter support for the realization of FMCW lidar ranging, speed measurement and other functions.

[0067] The shortcomings of the above solutions are the result of the inventors' practical experience and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present invention in the following text should be considered as contributions made by the inventors during the invention process.

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0070] It should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0071] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0072] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0073] Please refer to Figure 1This is a block diagram of an electronic device 100 provided in this embodiment. The electronic device 100 in this embodiment can be a server, processing device, processing platform, etc., capable of data interaction and processing. For example, it can be independent of an FPGA, or it can be the FPGA itself, or it can integrate an FPGA. The electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, processor 120, and communication module 130 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0074] The memory 110 is used to store programs or data. The memory 110 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0075] The processor 120 is used to read / write data or programs stored in the memory 110 and to perform corresponding functions.

[0076] The communication module 130 is used to establish a communication connection between the electronic device 100 and other communication terminals through the network, and to send and receive data through the network.

[0077] It should be understood that, Figure 1 The structure shown is only a schematic diagram of the electronic device 100. The electronic device 100 may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0078] Please refer to Figure 2 This is a flowchart illustrating a beat frequency signal analysis method provided in an embodiment of the present invention. It can be derived from... Figure 1 The electronic device 100 performs the operation, for example, by the processor 120 within the electronic device 100. The beat frequency signal analysis method includes steps S110 to S150.

[0079] S110 performs multi-channel delay and multiplexing processing on the signal to be processed to obtain multiple beat frequency signals with delay differences between channels and cascaded channels.

[0080] S120 performs windowing processing on the beat frequency signal of each channel.

[0081] S130 performs FFT operation on each beat frequency signal after windowing processing to obtain multiple FFT operation result data streams.

[0082] Each FFT operation result data stream contains the real and imaginary part of the frequency domain amplitude of each beat frequency signal after windowing processing, as well as the corresponding frequency index value in time.

[0083] S140, based on the data stream of each FFT operation result, analyze and obtain the channel result corresponding to the beat frequency signal of each channel.

[0084] S150, the results of each channel are processed according to the delay timing relationship to obtain the time-frequency data of the signal to be processed.

[0085] In this embodiment of the invention, by performing multi-channel delay, multiplexing, windowing, and FFT operations on the signal to be processed, the time-frequency data of the signal to be processed is obtained through analysis, thereby improving the comprehensiveness of the beat frequency signal analysis results and meeting various application requirements. For example, it can provide data support for ranging, speed measurement, etc.

[0086] In S110, the signal to be processed can be obtained in a variety of ways. As long as multi-channel delay and multiplexing processing is performed, multiple beat frequency signals with delay differences between channels and cascaded channels can be obtained.

[0087] For example, the signal to be processed can be obtained by: obtaining a beat frequency signal generated by coherently beating the emitted light signal and the reflected light signal of the FMCW lidar. The emitted light signal is a frequency-modulated continuous wave laser signal emitted by the FMCW lidar, and the reflected light signal is the light signal received by the FMCW lidar and reflected after the frequency-modulated continuous wave laser reaches a set target. The intermediate frequency digital signal obtained by analog-to-digital conversion of the beat frequency signal is used as the signal to be processed.

[0088] The signal to be processed can also be obtained in the following ways: the reflected light signal can be received by other photodetectors, such as silicon photomultiplier tubes (SiPMs), to generate a beat frequency signal, which is then converted from analog to digital to obtain an intermediate frequency digital signal as the signal to be processed.

[0089] Please refer to Figure 3An FMCW lidar may include an FMCW laser and a photodetector. The FMCW lidar emits a continuous frequency-modulated laser beam. When the emitted light reaches the target (the set target), it generates reflected light. The reflected light and the emitted light are then processed by the photodetector to generate a beat frequency signal. This beat frequency signal is then converted into an intermediate frequency (time domain) digital signal via analog-to-digital conversion (ADC), thus obtaining the signal to be processed in S110.

[0090] In S110, multiple beat frequency signals can be obtained by multiplexing the signal to be processed through multi-channel delay parallel multiplexing. For example, the signal to be processed can be input into an FPGA, and based on the multi-channel parallel dual-port Block RAM within the FPGA, multiple beat frequency signals with stepped timing delay differences for each channel are output in parallel. The two ports of the dual-port Block RAM are configured with different operating addresses, and the outputs of the two ports have a specified delay difference based on these different operating addresses.

[0091] For example, please continue reading Figure 3 The intermediate frequency digital signal is input to the FPGA, which performs multi-channel delay parallel multiplexing of the intermediate frequency digital signal to obtain multiple beat frequency signals.

[0092] In one implementation, please refer to [the relevant documentation / reference]. Figure 4 The number of channels can be n_channel, and the delay module can be implemented by the FPGA's on-chip dual-port Block RAM. By setting different operating addresses for the two ports, the outputs of the two ports can be made to have a specified delay difference n_delay. One beat frequency signal is input into multiple parallel RAMs, each RAM is set to output with a delay of n_delay and cascaded, thus obtaining a multi-channel parallel beat frequency signal with a stepped timing delay difference for each channel.

[0093] The number of parallel multiplexed channels, n_channel, and the number of clock cycles for delay, n_delay, can be flexibly configured according to requirements.

[0094] In one implementation, multi-channel parallel signals with stepped delay differences can be implemented by an off-chip RAM chip of the FPGA, or by a FIFO designed inside the FPGA.

[0095] Please continue reading. Figure 3 After obtaining the multi-channel delayed multiplexed beat frequency signal based on S110, a windowing operation is performed on the beat frequency signal of each channel. Optionally, to prevent spectral leakage, a cosine window can be added to each channel.

[0096] For example, in S120, windowing the beat frequency signal of each channel can be achieved in the following way: generating a Hamming window data sequence, adjusting the data bit width of the data sequence to a specified bit width, using it as the initialization file of the ROM in the FPGA, cyclically reading the ROM in the FPGA, using the output data of the ROM as a Hamming window data stream, and multiplying each Hamming window data stream with the beat frequency signal of each channel to obtain the windowed beat frequency signal of each channel.

[0097] In one implementation, please refer to [the relevant documentation / reference]. Figure 5 A set of Hamming window data sequences with a length of N can be generated using Matlab. The data bit width of this sequence can be adjusted to a specified width and then used as the initialization file (data) for the FPGA's on-chip ROM. The FPGA can then cyclically read from this ROM, and the data output from the ROM is the Hamming window data stream with a certain data bit width. Multiplying this Hamming window data stream by the beat frequency signals of each channel in S110 above yields the windowed n_channel multi-channel beat frequency signal. The length N of the Hamming window data sequence can be flexibly configured according to requirements.

[0098] In one implementation, the windowing method can be implemented by storing data using an external RAM chip, an external ROM chip, or an on-chip instantiated ROM of the FPGA.

[0099] In S130, the FFT operation result data stream can be obtained as follows: For the beat frequency signal of each channel after windowing processing, based on the multiplier added to the corresponding channel in the FPGA, the square of the real part and the square of the imaginary part of the frequency domain amplitude of the beat frequency signal are calculated respectively. The squares of the real part and the imaginary part are added together to obtain the square of the magnitude of the beat frequency signal of each channel after windowing processing.

[0100] Accordingly, the channel results in S140 can be achieved by performing peak detection on the squared values ​​of each modulus to obtain the maximum frequency domain amplitude and the maximum frequency index corresponding to each channel. The time-frequency data in S150 can be achieved by serially converting the maximum frequency domain amplitude and the maximum frequency index corresponding to each channel according to the delay timing relationship to obtain a one-dimensional frequency index value sequence.

[0101] In one implementation, please refer to [the relevant documentation / reference]. Figure 3 and Figure 6The FFT result data stream in S130 can be obtained by performing FFT frequency measurement on each channel of the windowed multi-channel beat frequency signal in S120 within each N-point window period. For example, an FFT operation core is added for each channel. The beat frequency signal of each channel is multiplied by the window function and then fed into the FFT operation core. The number of FFT operation points is N, which is the same as the number of points of the windowing mentioned above, so that the FFT operation is performed on the beat frequency signal within each window. In this way, each channel outputs a set of FFT result data streams. The FFT result data streams contain the real part signal data_real and the imaginary part signal data_imag of the frequency domain amplitude of the data in each window, as well as the corresponding frequency index value index in the time sequence. Among them, the frequency index value index is a data stream that increments sequentially from 0 to N-1 and cycles.

[0102] Please refer to Figure 3 and Figure 7 After implementing FFT frequency measurement, the amplitude of the frequency measurement results for each channel is calculated modulowise. The real and imaginary parts of the frequency domain amplitude for each channel constitute a complex signal of the frequency domain amplitude. Two multipliers are added to each channel in the FPGA to calculate the squares of the real and imaginary parts of the signal, respectively. The squares of the real and imaginary parts are then added together to obtain the squared value of the frequency domain amplitude modulus of each channel, amp_square. Since the modulus of the frequency domain amplitude is positive, its trend is the same as that of the squared modulus. Therefore, the squared value of the modulus can be directly used for subsequent processing to obtain the squared value of the frequency domain amplitude modulus of the n_channel multi-channel, amp_square, and the corresponding frequency index value, index.

[0103] Please refer to Figure 8 Peak detection is performed on the squared value of the frequency domain amplitude magnitude of each channel, amp_suqare. The peak value is detected by the squared value of the frequency domain amplitude magnitude corresponding to the frequency index value in the range of index∈[0,N / 2-1]. This allows us to obtain the maximum frequency domain amplitude value amp_max and the corresponding frequency index value index_max of each window in each channel of n_channel.

[0104] In an FPGA, an incrementally increasing counter and a state machine with the same number of states as the number of channels are configured. Each state of the state machine corresponds to a channel arranged in a time sequence. In step S150, the step of serially converting the maximum frequency domain amplitude and maximum frequency index value corresponding to each channel according to a delay sequence to obtain a one-dimensional frequency index value sequence can be implemented as follows: When the counter is at its maximum value, a state transition is performed based on the state machine, and the maximum frequency index value corresponding to the channel in the current state is taken to obtain a one-dimensional serial frequency index value sequence. The beat frequency is calculated based on the FFT points and the running clock frequency. Based on the beat frequency, the one-dimensional serial frequency index value sequence is converted into a time-frequency curve.

[0105] In one implementation, please refer to [the relevant documentation / reference]. Figure 9 The channel results in S140 are serially converted according to the inter-channel delay timing relationship to obtain a one-dimensional frequency index value sequence, which is the time-frequency curve of the beat frequency signal. A state machine (state_machine) is designed in the FPGA, with the number of states equal to the number of parallel channels, which is n_channel. Each state corresponds to each channel arranged in timing sequence, and performs the extraction operation of the maximum frequency index value of that channel. An incrementing cyclic counter (counter) is designed, ranging from 0 to n_delay-1. A state transition occurs when counter = n_delay-1. The state machine runs cyclically to obtain the one-dimensional frequency index value sequence. Since this sequence is obtained according to the timing sequence of each channel and each window, it represents the time-frequency index value curve of the beat frequency signal.

[0106] Based on this, the beat frequency can be calculated using the FFT point count N and the FPGA operating clock frequency fs. The aforementioned time-frequency index curve can then be converted into a time-frequency curve, and the stable frequency value in this curve is the stable frequency of the beat frequency signal.

[0107] Understandably, all critical signals in the computational process of an FPGA can be captured and monitored online using the FPGA's internal ILA (In-line Logic Analyzer).

[0108] Based on the obtained time-frequency data, such as the time-frequency curve mentioned above, various applications can be performed. For example, when the target is stationary, the distance between the FMCW lidar and the target can be calculated using the following formula:

[0109] d = cTf iF / (4BW)

[0110] Among them, f iF The stable frequency in the time-frequency data can be a value within a stable interval of the time-frequency data, and the value within that stable interval can be the maximum value of the time-frequency data at that distance; BW is the frequency modulation bandwidth range of the transmitted optical signal; T is the frequency modulation period of the transmitted optical signal; and c is the speed of light.

[0111] For example, when the target is in motion, the speed of the target can be calculated using the following formula:

[0112] fd = 2vf / c

[0113] Where fd is the Doppler frequency obtained from the time-frequency data analysis; v is the velocity of the target; f is the laser carrier frequency; and c is the speed of light.

[0114] To more clearly illustrate the implementation process of the embodiments of the present invention, the following scenario will be used as an example.

[0115] Please see Figure 10 This is a flowchart illustrating the ranging and velocity measurement based on an FMCW lidar. The FMCW lidar emits a frequency-modulated continuous wave laser, which is reflected off the target. The FMCW lidar receives the reflected light signal and performs coherent beat frequency analysis between the reflected and emitted light signals to obtain the beat frequency signal.

[0116] Real-time calculation of time-frequency data is implemented using FPGA (Field Programmable Gate Array). For example:

[0117] Let x(t) be the function of the beat frequency signal. Define a window function w(t). Shift the window function to the beginning of the beat frequency signal and multiply it with the beat frequency signal to obtain the windowed function expression y(t) = x(t)·w(ta). Here, a is the function shift.

[0118] The following formula is used to perform a Fourier transform on each windowed beat frequency signal:

[0119]

[0120] This yields the spectral distribution X(ω) of the segmented sequence. Since signals in practical engineering applications are discrete point sequences, the resulting sequence is a spectral sequence S[N]. We define S(ω, a) as the result of the Fourier transform of the original function with the window function center at a, i.e.:

[0121]

[0122] In the discrete scenario, S[ω, a] represents the result sequence after performing a Fourier transform on the obtained segmented sequence. It is a two-dimensional matrix, and each column represents the windowing of the signal at different positions.

[0123] After performing the Fourier transform on each segment, the window function is moved to the next segment. For example, after performing the Fourier transform on the first segment, the window function is moved to a1. The interval between a0 and a1 is called the sliding window interval. The sliding window interval is smaller than the window width, thus ensuring that there is a certain overlap between the two windows. With a fixed window size, the smaller the sliding window interval, the larger the overlap, and the higher the time resolution of the final time-frequency analysis measurement results.

[0124] For each beat frequency signal, the steps of multiplying the beat frequency signal by a function and a window function, performing a Fourier transform on the windowed result, and moving the window function are performed to obtain a two-dimensional matrix sequence of S([ωn, an]).

[0125] In the S([ωn, an]) two-dimensional matrix sequence, the energy of the spectral components of the results in each window is analyzed. The frequency point with the highest energy is obtained by peak search. Then, the frequency points with the highest energy in all windows are arranged in the time order of the corresponding windows to obtain the time-frequency curve of the beat frequency signal, and thus the stable frequency of the beat frequency signal is obtained.

[0126] When the target is relatively stationary, the coherent beat frequency model of the emitted light and the reflected light from the relatively stationary target is as follows:

[0127] like Figure 11 The figure shows a theoretical model based on the time-frequency curve of the beat frequency signal of an FMCW lidar (relatively stationary target). The coherent beat frequency of the emitted light and the reflected light from the relatively stationary target generates the beat frequency signal. The functional expression of the laser signal emitted by the FMCW lidar is defined as follows: Where f0 is the starting frequency of the frequency-modulated laser signal. Let τ be the frequency modulation slope, T be the frequency modulation period, BW be the frequency modulation bandwidth, and τ = Tf. IF / (2BW) represents the time it takes for the emitted light to return after being reflected from the target (time of flight), i.e., the reflected light from the target has a time delay difference of τ relative to the emitted light, f IF This is the stable frequency of the beat frequency signal.

[0128] Due to the optical path difference between the emitted and reflected light, there is a time delay when the emitted and reflected light reach the photodetector inside the FMCW lidar. Their frequency function curves are as follows: Figure 11As shown in the figure, the solid line represents the optical frequency function curve of the emitted light, and the dashed line represents the optical frequency function curve of the target reflected light. It can be seen that the beat frequency is a stable value f in the time interval (i-1)T+τ<t<(iT-T / 2) of the i-th period. IF According to the relationship between optical path length, speed of light, and time, it can be known that Calculate distance Figure 11 The curve in the lower middle section is the time-frequency curve of the beat frequency signal. This time-frequency curve is related to f. IF The value can be obtained through the time-frequency analysis method described above.

[0129] When the target is in a state of relative motion, such as Figure 12 The figure shows a theoretical model based on the time-frequency curve of the beat frequency signal from an FMCW lidar (for a relatively moving target). The beat frequency signal is obtained by coherently beating the emitted light with the reflected light from a relatively moving target. The difference between the model for a relatively moving target and the model for a relatively stationary target is that the target's motion causes a Doppler frequency shift in the reflected light. The Doppler frequency f... d = 2*v*f / c, where v is the relative velocity of the target, f is the laser carrier frequency, and c is the speed of light. The corresponding beat frequency stable frequency f is the beat frequency of the first half of the modulation period (0~T / 2) of the beat frequency signal. IF1 Beat frequency stability f relative to a relatively stationary target IF The relation is f IF1 =f IF -f d The beat frequency stabilization frequency f during the second half of the modulation period (T / 2 to T) IF2 with f IF The relation is f IF2 =f IF +f d f is measured using the time-frequency analysis measurement method described in this scheme. IF1 and f IF2 The value of f can be further calculated using the formula above. IF And fd, and then calculate the target distance and speed.

[0130] To perform the corresponding steps in the above embodiments and various possible methods, an implementation of a beat frequency signal analysis device is given below. Please refer to... Figure 13 , Figure 13 This is a functional block diagram of a beat frequency signal analysis device 140 provided in an embodiment of the present invention. The beat frequency signal analysis device 140 can be applied to... Figure 1The electronic device 100 is shown. It should be noted that the beat frequency signal analysis device 140 provided in this embodiment has the same basic principle and technical effects as the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments. The beat frequency signal analysis device 140 includes a signal processing module 141 and a signal analysis module 142.

[0131] The signal processing module 141 is used to perform multi-channel delay and multiplexing processing on the signal to be processed, to obtain multiple beat frequency signals with delay difference between each channel and cascaded between each channel, and to perform windowing processing on the beat frequency signal of each channel.

[0132] The signal analysis module 142 is used to perform FFT operation on each beat frequency signal after windowing processing to obtain multiple FFT operation result data streams; based on each FFT operation result data stream, analyze to obtain the channel result corresponding to the beat frequency signal of each channel; process each channel result according to the delay timing relationship to obtain the time-frequency data of the signal to be processed.

[0133] Each FFT operation result data stream contains the real and imaginary part of the frequency domain amplitude of each beat frequency signal after windowing processing, as well as the corresponding frequency index value in time.

[0134] Based on the above, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a computer program, wherein the computer program, when running, controls the electronic device in which the computer-readable storage medium is located to execute the above-described beat frequency signal analysis method.

[0135] By employing the above-described scheme in this embodiment of the invention, time-frequency synchronous analysis and measurement of the beat frequency signal of an FMCW lidar can be performed, thereby solving the problem that the results of traditional direct FFT frequency measurement methods cannot reflect the time domain information of the beat frequency signal. The beat frequency signal time-frequency analysis method used in this embodiment of the invention can flexibly adjust parameters such as the number of parallel channels, inter-channel delay, window width, and number of points according to the different modulation bandwidths and modulation periods of the FMCW lidar. This allows the time-frequency curve of the beat frequency signal to take into account both time resolution and frequency resolution, providing effective data support for the FMCW lidar to achieve ranging and velocity measurement functions.

[0136] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0137] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0138] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing beat frequency signals, characterized in that, include: The signal to be processed is subjected to multi-channel delay and multiplexing to obtain multiple beat frequency signals with delay differences in each channel and cascaded channels. Windowing is applied to the beat frequency signal of each channel; An FFT operation is performed on each beat frequency signal after windowing to obtain multiple FFT operation result data streams; wherein, each FFT operation result data stream contains the real part and imaginary part of the frequency domain amplitude of each beat frequency signal after windowing, as well as the corresponding frequency index value in time sequence; Based on the data stream of each FFT operation result, the channel result corresponding to the beat frequency signal of each channel is obtained by analysis; The results of each channel are processed according to the delay sequence relationship to obtain the time-frequency data of the signal to be processed.

2. The beat frequency signal analysis method according to claim 1, characterized in that, The method further includes a step of obtaining the signal to be processed, which includes: The beat frequency signal is obtained by coherently beating the emitted light signal and the reflected light signal of the FMCW lidar; wherein, the emitted light signal is the frequency-modulated continuous wave laser signal emitted by the FMCW lidar, and the reflected light signal is the light signal received by the FMCW lidar and reflected after the frequency-modulated continuous wave laser reaches the set target; The intermediate frequency digital signal obtained by analog-to-digital conversion of the beat frequency signal is used as the signal to be processed.

3. The beat frequency signal analysis method according to claim 1 or 2, characterized in that, The step of performing multi-channel delay and multiplexing processing on the signal to be processed to obtain multiple beat frequency signals with delay differences in each channel and cascaded among the channels includes: The signal to be processed is input into the FPGA, and based on the multi-channel parallel dual-port Block RAM in the FPGA, multiple beat frequency signals with stepped timing delay difference and parallel operation of each channel are output. The dual-port Block RAM has two ports with different operating addresses, and the outputs of the two ports have a specified delay difference based on the different operating addresses.

4. The beat frequency signal analysis method according to claim 3, characterized in that, The step of windowing the beat frequency signal of each channel includes: Generate the data sequence for the Hamming window; After adjusting the data bit width of the data sequence to a specified bit width, it is used as the initialization file for the ROM within the FPGA; The ROM is read cyclically in the FPGA, and the output data of the ROM is used as a Hamming window data stream; The Hamming window data streams are multiplied by the beat frequency signals of each channel to obtain the beat frequency signal of each channel after windowing.

5. The beat frequency signal analysis method according to claim 4, characterized in that, The step of performing FFT operations on each beat frequency signal after windowing processing to obtain multiple FFT operation result data streams includes: For the beat frequency signal of each channel after windowing processing, based on the multiplier added to the corresponding channel in the FPGA, the square of the real part and the square of the imaginary part of the frequency domain amplitude of the beat frequency signal are calculated respectively. The squares of the real part signal and the imaginary part signal are added together to obtain the squared magnitude of the beat frequency signal of each channel after windowing. The step of analyzing and obtaining the channel result corresponding to the beat frequency signal of each channel based on the data stream of each FFT operation result includes: Peak detection is performed on the squared values ​​of each modulus to obtain the maximum frequency domain amplitude and the maximum frequency index corresponding to each channel. The step of processing the results of each channel according to the delay sequence relationship to obtain the time-frequency data of the signal to be processed includes: The maximum frequency domain amplitude and the maximum frequency index corresponding to each channel are serially converted according to the delay timing relationship to obtain a one-dimensional frequency index value sequence.

6. The beat frequency signal analysis method according to claim 5, characterized in that, The FPGA is equipped with an incrementing cyclic counter and a state machine with the same number of states as the number of channels, wherein each state of the state machine corresponds to each channel arranged in a time sequence. The step of serially converting the maximum frequency domain amplitude and the maximum frequency index corresponding to each channel according to the delay timing relationship to obtain a one-dimensional frequency index value sequence includes: When the counter is at its maximum value, state transition is performed based on the state machine, and the maximum frequency index value corresponding to the channel corresponding to the current state is taken to obtain a one-dimensional serial frequency index value sequence. The beat frequency is calculated based on the FFT points and the running clock frequency. Based on the beat frequency, the one-dimensional serial frequency index value sequence is converted into a time-frequency curve.

7. The beat frequency signal analysis method according to claim 2, characterized in that, When the target is stationary, the method further includes calculating the distance between the FMCW lidar and the target according to the following formula: d=cTf iF / (4BW) Among them, f iF BW represents the stable frequency in the time-frequency data; T represents the frequency modulation bandwidth range of the transmitted optical signal; and c represents the frequency modulation period of the transmitted optical signal. When the set target is in motion, the method further includes calculating the motion speed of the set target according to the following formula: fd = 2vf / c Where fd is the Doppler frequency obtained from the time-frequency data analysis; v is the velocity of the target; f is the laser carrier frequency; and c is the speed of light.

8. A beat frequency signal analysis device, characterized in that, include: The signal processing module is used to perform multi-channel delay and multiplexing processing on the signal to be processed, to obtain multiple beat frequency signals with delay differences in each channel and cascaded channels, and to perform windowing processing on the beat frequency signal of each channel. The signal analysis module is used to perform FFT operations on each beat frequency signal after windowing processing to obtain multiple FFT operation result data streams; Based on the data stream of each FFT operation result, the channel result corresponding to the beat frequency signal of each channel is analyzed and obtained; the channel results are processed according to the delay timing relationship to obtain the time-frequency data of the signal to be processed; Each FFT operation result data stream contains the real and imaginary part of the frequency domain amplitude of each beat frequency signal after windowing processing, as well as the corresponding frequency index value in time.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the beat frequency signal analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program, which, when executed, controls the electronic device containing the computer-readable storage medium to perform the beat frequency signal analysis method according to any one of claims 1 to 7.

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