Data processing method and device for FMCW laser radar

Abstract

The invention relates to the technical field of optical detection, in particular to a data processing method and device for an FMCW laser radar, and the method comprises the steps: obtaining a beat frequency signal, selecting a signal of one period in the beat frequency signal as target data, and analyzing the target data to obtain an upper edge beat frequency signal and a lower edge beat frequency signal; obtaining the phase of the end time of the upper edge beat frequency signal and the phase of the start time of the lower edge beat frequency signal; selecting a target starting point from the lower edge beat frequency signal, so that the phase of the end time of the upper edge beat frequency signal is continuous with the phase of the target starting point; and selecting lower edge data from the lower edge beat frequency signal according to the target starting point, obtaining upper edge data, cascading the lower edge data and the upper edge data, and processing the cascaded data. The upper edge data and the lower edge data are cascaded and processed to obtain the signal-to-noise ratio of the beat frequency signal, and the signal-to-noise ratio of the beat frequency signal can be increased by 3db. The detection distance and the detection precision of the FMCW laser radar can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical detection, and particularly to a data processing method and device for an FMCW lidar. Background Art

[0002] Compared with lidars based on Time of Flight (TOF), lidars based on Frequency Modulated Continuous Wave (FMCW) are considered the main direction of the next generation of optical detection and ranging because they have the advantages of strong anti-interference ability and the ability to directly obtain radial velocity. FMCW lidars are based on frequency modulation and coherent detection technologies. By detecting the beat signal between the transmitted signal and the reflected signal, the distance and radial velocity information of the target object can be obtained. The signal-to-noise ratio of the beat signal directly affects the detection ability and accuracy.

[0003] There are many factors affecting the signal-to-noise ratio of FMCW lidars, such as the received power of the reflected signal, laser linewidth, photodetector responsivity, data processing signal bandwidth, etc. Therefore, in order to maximize the signal-to-noise ratio, a large number of designs are required for FMCW lidars. For example, increasing the received power of the reflected signal, increasing the transmitted optical power, and increasing the receiving lens aperture are the most effective methods. However, the transmitted optical power and the receiving lens aperture are respectively limited by the first-level eye safety standard and the system size, and these parameters are determined by the design of the optoelectronic components.

[0004] In addition to hardware design to improve the signal-to-noise ratio, the backend data processing method is considered a key path. Usually, the Fast Fourier Transformation (FFT) is a conventional signal processing method for obtaining the beat signal. However, different backend data processing methods based on the FFT algorithm still bring obvious performance differences to the signal-to-noise ratio. In the prior art, usually only the upper-edge beat signal in the beat signal is subjected to correlation calculation to improve the signal-to-noise ratio, and the amplitude of improving the signal-to-noise ratio still needs to be improved.

[0005] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in this technical field. Summary of the Invention

[0006] The technical problem to be solved by the present invention is: how to provide a processing method for the backend data of an FMCW lidar to better improve the signal-to-noise ratio.

[0007] The present invention adopts the following technical solutions:

[0008] In a first aspect, a data processing method for an FMCW lidar is provided, including:

[0009] Obtain a beat frequency signal, select a signal of one period in the beat frequency signal as target data, and analyze the target data to obtain an upper-edge beat frequency signal and a lower-edge beat frequency signal;

[0010] Obtain the phase at the end time of the upper-edge beat frequency signal;

[0011] Select a target starting point from the lower-edge beat frequency signal to make the phase at the end time of the upper-edge beat frequency signal continuous with the phase of the target starting point;

[0012] Select lower-edge data from the lower-edge beat frequency signal according to the target starting point, and obtain upper-edge data, cascade the lower-edge data and the upper-edge data, and process the cascaded data.

[0013] Preferably, the obtaining the beat frequency signal and selecting a signal of one period in the beat frequency signal as target data includes:

[0014] In an FMCW lidar system, obtain the beat frequency signal by mixing the transmitted frequency-modulated signal with the signal reflected from the target object; select data of one period in the beat frequency signal as the target data.

[0015] Preferably, the analyzing the target data to obtain an upper-edge beat frequency signal and a lower-edge beat frequency signal includes:

[0016] The upper-edge beat frequency signal of the target data is:

[0017] I bu (t,τ)=|A bu |cos{2πτγt+θ(t)-θ(t-τ)}t up_start ≤t≤t up_end ;

[0018] The lower-edge beat frequency signal of the target data is:

[0019] I bd (t,τ)=|A bd |cos{2πτγt+θ(t)-θ(t-τ)}t down_start ≤t≤t down_end ;

[0020] Wherein, τ is the round-trip time after the outgoing optical signal reaches the target object; t up_start is the start time of the uplink effective detection area; t up_end is the end time of the uplink effective detection area; t down_startis the start time of the downlink valid detection time period; t down_end is the end time of the downlink valid detection time period; A bu is the constant amplitude of the upper edge beat frequency signal; |A bd | is the constant amplitude of the lower edge beat frequency signal; θ(t) is the phase noise of the laser; γ is the tuning rate of the scanning frequency; θ(t) - θ(t - τ) represents the phase difference between the transmitted signal and the received signal caused by the laser linewidth.

[0021] Preferably, when τ is less than the coherence time of the laser, the upper edge beat frequency signal and the lower edge beat frequency signal can also be expressed as:

[0022]

[0023] where φ0 is the initial phase of the upper edge beat frequency signal, Δφ is the phase difference between the end time of the upper edge beat frequency signal and the start time of the lower edge beat frequency signal, and Δφ is expressed as:

[0024] Δφ = 2πτγ(t down_start -t up_end );

[0025] where τ is the round-trip time after the outgoing optical signal reaches the target object; γ is the tuning rate of the scanning frequency, that is, the slope of the scanning frequency signal.

[0026] Preferably, obtaining the phase of the end time of the upper edge beat frequency signal specifically includes:

[0027] Extracting the phase at the end of the upper edge beat frequency signal from the target data through Hilbert transform.

[0028] Preferably, selecting a target starting point from the lower edge beat frequency signal to make the phase of the end time of the upper edge beat frequency signal and the phase of the target starting point continuous includes:

[0029] Determining the starting point of the lower edge beat frequency signal according to Δφ = 2πN, where N is an integer.

[0030] Preferably, selecting lower edge data from the lower edge beat frequency signal according to the target starting point, obtaining upper edge data, cascading the lower edge data and the upper edge data, and processing the cascaded data includes:

[0031] Obtaining the lower edge data according to the target starting point in the lower edge beat frequency signal, and obtaining the upper edge data according to the upper edge beat frequency signal;

[0032] Cascading the upper edge data and the lower edge data to obtain a cascaded beat frequency signal;

[0033] The beat frequency and signal-to-noise ratio of the cascaded beat frequency signal are obtained through fast Fourier transform.

[0034] Preferably, the cascading of the upper edge data and the lower edge data to obtain the cascaded beat frequency signal includes:

[0035] Connect the upper edge data and the lower edge data together in chronological order to obtain the cascaded beat frequency signal with a complete beat frequency signal period.

[0036] Preferably, the obtaining of the beat frequency and signal-to-noise ratio of the cascaded beat frequency signal through fast Fourier transform specifically includes:

[0037] Use fast Fourier transform to transform the time-domain data of the cascaded beat frequency signal and convert it into frequency-domain data;

[0038] In the fast Fourier transform result in the frequency domain, obtain the beat frequency by comparing the positions of the signal peaks in the frequency domain;

[0039] The signal-to-noise ratio is obtained according to Formula 1:

[0040] Formula 1 is: SNR = 10 * log 10 P s / P n ;

[0041] where SNR is the signal-to-noise ratio; P s is the average power of the signal; P n is the average power of the noise.

[0042] In a second aspect, a data processing device for an FMCW lidar is provided. The data processing device for an FMCW lidar includes: a processor and a memory for storing instructions executable by the processor;

[0043] wherein the processor is configured to execute the data processing method for an FMCW lidar as described above.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] The present invention first obtains a beat frequency signal, selects the signal of one period in the beat frequency signal as target data, analyzes the target data to obtain an upper-edge beat frequency signal and a lower-edge beat frequency signal; obtains the phase at the end time of the upper-edge beat frequency signal and the phase at the start time of the lower-edge beat frequency signal; selects a target starting point from the lower-edge beat frequency signal to make the phase at the end time of the upper-edge beat frequency signal and the phase of the target starting point continuous; selects lower-edge data from the lower-edge beat frequency signal according to the target starting point, and obtains upper-edge data, cascades the lower-edge data and the upper-edge data, and processes the cascaded data. The signal-to-noise ratio of the beat frequency signal obtained by cascading and processing the upper-edge data and the lower-edge data is 3 dB higher than that of only processing the upper-edge beat frequency signal, which can better improve the detection distance and detection accuracy of the FMCW lidar. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 is a schematic flowchart of a data processing method for an FMCW lidar provided by an embodiment of the present invention;

[0048] Figure 2 is a waveform schematic diagram of a beat frequency signal of a data processing method for an FMCW lidar provided by an embodiment of the present invention;

[0049] Figure 3 is another schematic flowchart of a data processing method for an FMCW lidar provided by an embodiment of the present invention;

[0050] Figure 4 is a waveform comparison schematic diagram of the upper-edge processing result and the upper-edge cascaded with the lower-edge processing result of a data processing method for an FMCW lidar provided by an embodiment of the present invention;

[0051] Figure 5 is a schematic structural diagram of a data processing device for an FMCW lidar provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] In the present invention, terms such as "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0054] In the present invention, unless otherwise clearly specified and defined, the term "connection" shall be understood in a broad sense. For example, "connection" may be a fixed connection, a detachable connection, or integrated; it may be directly connected or indirectly connected through an intermediate medium. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] Embodiment 1:

[0056] Due to the complexity of FMCW lidar technology and the diversity of applications, there is currently no unified and detailed guide to cover all backend data processing methods. In this embodiment, a data processing method for FMCW lidar is proposed, including: acquiring a beat signal, selecting a signal of one period in the beat signal as target data, analyzing the target data to obtain an upper-edge beat signal and a lower-edge beat signal; acquiring the phase at the end time of the upper-edge beat signal and the phase at the start time of the lower-edge beat signal; selecting a target starting point from the lower-edge beat signal to make the phase at the end time of the upper-edge beat signal and the phase of the target starting point continuous; selecting lower-edge data from the lower-edge beat signal according to the target starting point, and obtaining upper-edge data, cascading the lower-edge data and the upper-edge data, and processing the cascaded data.

[0057] First, obtain a continuous beat frequency signal from the FMCW lidar. Then, select a specific period from this continuous signal as the target data for analysis. This period represents a complete process of the interaction between the radar wave and the target object. Use the first algorithm to analyze the beat frequency signal in the selected period. The purpose of this algorithm is to find the phase at the end time point of the upper edge of the beat frequency signal and the phase at the start time point of the lower edge. According to the obtained phase information, determine the precise starting point of the lower-edge beat frequency signal. The aim is to ensure that the phase at the end of the upper edge is continuous with the phase at the start of the lower edge, as it affects the accuracy of subsequent beat frequency signal analysis. Next, according to the determined starting point of the lower edge, select the data of the upper edge and the lower edge respectively. Concatenate these two parts of data, that is, combine the upper-edge data and the lower-edge data to form a complete data sequence. Use the second algorithm to process the concatenated data sequence to obtain the beat frequency and the signal-to-noise ratio. This algorithm involves Fourier transform (such as FFT) to analyze the frequency components and specific calculation methods to evaluate the signal-to-noise ratio.

[0058] In this embodiment, the signal-to-noise ratio of the beat frequency signal obtained by concatenating the upper-edge data and the lower-edge data is 3 dB higher than that of only processing the upper-edge beat frequency signal, which can better improve the detection range and detection accuracy of the FMCW lidar.

[0059] In the preferred embodiment, the details of each step will be described below, and the specific implementation details of the first algorithm and the second algorithm will be specifically described below.

[0060] Next, the specific process of the data processing method for the FMCW lidar will be described. As Figure 1 shown, it includes:

[0061] Step 101: Obtain the beat frequency signal, select the signal of one period in the beat frequency signal as the target data, and analyze the target data to obtain the upper-edge beat frequency signal and the lower-edge beat frequency signal.

[0062] Among them, in the FMCW lidar system, the beat frequency signal is obtained by mixing the transmitted frequency modulation signal with the signal reflected from the target object; select the data of one period in the beat frequency signal as the target data.

[0063] The FMCW lidar system emits a signal whose frequency changes continuously over time. Such a signal usually has a linearly increasing or decreasing frequency and is called a chirp signal. The emitted signal is reflected by the target object, and the radar system receives these reflected signals. Since there is a time delay in the round-trip of the laser to the target object, the frequency of the reflected signal will have a certain difference from the frequency of the emitted signal. The radar system mixes (or beats) the received reflected signal with a reference signal (usually a copy of the original emitted signal). The mixing process generates two signals, one is the sum of the two signal frequencies, and the other is the difference between the two signal frequencies. In FMCW lidar, usually the part of the frequency difference is concerned because it contains information related to the distance of the target object.

[0064] The beat signal obtained by mixing is the part of interest, and its frequency is directly related to the distance of the target object. The higher the beat frequency, the farther the target object is from the radar. The beat signal is periodic, and each period represents a complete frequency modulation process. To perform accurate distance and speed measurements, a specific period needs to be selected from the continuous beat signal as the target data for analysis. The selected period should contain sufficient information for subsequent processing and analysis. This usually involves the selection of a time window for the signal to ensure that the window contains a complete and clear period of the beat signal.

[0065] In a preferred embodiment, as Figure 2 shown, the target data is represented as:

[0066] The upper-edge beat signal of the target data is:

[0067] I bu (t,τ) = |A bu |cos{2πτγt + θ(t) - θ(t - τ)}t up_start ≤t≤t up_end ;

[0068] The lower-edge beat signal of the target data is:

[0069] I bd (t,τ) = |A bd |cos{2πτγt + θ(t) - θ(t - τ)}t down_start ≤t≤t down_end ;

[0070] where τ is the round-trip time after the outgoing optical signal reaches the target object; t up_start is the start time of the uplink effective detection region; t up_end is the end time of the uplink effective detection region; t down_start is the start time of the downlink effective detection time period; tdown_end is the end time of the downlink valid detection time period; A bu is the constant amplitude of the upper-edge beat frequency signal; |A bd | is the constant amplitude of the lower-edge beat frequency signal; θ(t) is the phase noise of the laser; γ is the tuning rate of the scanning frequency; θ(t) - θ(t - τ) represents the phase difference between the transmitted signal and the received signal caused by the laser linewidth.

[0071] When τ is much smaller than the coherence time of the laser, the phase noise of the laser only slightly reduces the power of the beat frequency signal and does not cause broadening of the beat frequency signal. Therefore, the upper-edge beat frequency signal and the lower-edge beat frequency signal can also be expressed as:

[0072]

[0073] where φ0 is the initial phase of the upper-edge beat frequency signal, Δφ is the phase difference of the beat frequency signal between the end time of the upper-edge beat frequency signal and the start time of the lower-edge beat frequency signal, and Δφ is expressed as:

[0074] Δφ = 2πτγ(t down_start -t up_end );

[0075] where τ is the round-trip time after the outgoing optical signal reaches the target object; γ is the tuning rate of the scanning frequency, that is, the slope of the scanning frequency signal.

[0076] Since τ is much smaller than the coherence time of the laser, the broadening of the peak spectrum can be ignored, so the beat frequency signal can be regarded as a single-frequency signal. Therefore, the calculated signal-to-noise ratio will be determined by the sampling time.

[0077] Step 102: Obtain the phase at the end time of the upper-edge beat frequency signal.

[0078] Extract the phase at the end of the upper-edge beat frequency signal from the target data through Hilbert transform.

[0079] where the first algorithm can be Hilbert transform. Hilbert transform is a mathematical tool widely used in the field of signal processing. It can be used to construct an analytic signal from a real-valued signal, so as to conveniently extract the instantaneous phase and instantaneous frequency information of the signal. In the FMCW lidar system, Hilbert transform can be used to extract the phase at the end of the upper-edge beat frequency signal and the phase at the start of the lower-edge beat frequency signal from the target data.

[0080] First, for the upper-edge beat-frequency signal and the lower-edge beat-frequency signal of the target data, Hilbert transforms are respectively applied to construct their analytic signals. An analytic signal is a complex signal, whose real part is the original signal and the imaginary part is the Hilbert transform of the original signal. For the analytic signals of the upper-edge beat-frequency signal and the lower-edge beat-frequency signal, the instantaneous phase information can be directly extracted from their complex forms.

[0081] At the effective end time of the upper-edge beat-frequency signal, calculate the instantaneous phase of its analytic signal. Similarly, at the effective start time of the lower-edge beat-frequency signal, calculate the instantaneous phase of its analytic signal. The phase difference information is obtained by comparing the phases at the end of the upper edge and the start of the lower edge, and the phase difference information can be used to ensure the continuity and correctness of the phase.

[0082] Step 103: Select a target starting point from the lower-edge beat-frequency signal to make the phase at the end time of the upper-edge beat-frequency signal and the phase at the target starting point continuous.

[0083] When Δφ = 0, the phase at the end time of the upper-edge beat-frequency signal and the phase at the start time of the lower-edge beat-frequency signal are continuous; that is, determine the starting point of the lower-edge beat-frequency signal according to Δφ = 2πN, where N is an integer.

[0084] When Δφ = 2πN, the phases of the upper-edge beat-frequency signal and the lower-edge beat-frequency signal are continuous. The two signals can be seamlessly connected, thereby effectively combining energy and improving the signal-to-noise ratio. Theoretically, when the sampling time of the signal is doubled, if the signal is phase continuous, the signal power will increase by two times, while the increase in noise power is less than the increase in signal power (because noise is random), so that the signal-to-noise ratio is increased by about 3 dB. An increase of 3 dB is equivalent to doubling the signal power. If the phases of the upper-edge beat-frequency signal and the lower-edge beat-frequency signal are not continuous, the phase misalignment of the upper-edge beat-frequency signal and the lower-edge beat-frequency signal will lead to unsatisfactory signal combination, and in this case, the improvement of the signal-to-noise ratio will not reach the ideal 3 dB.

[0085] Therefore, although the sampling time of the upper-edge beat-frequency signal cascaded with the lower-edge beat-frequency signal is twice that of the single upper-edge beat-frequency signal, the signal-to-noise ratio does not necessarily increase by 3 dB. The increase in the signal-to-noise ratio is determined by Δφ. Only when Δφ = 2πN, that is, when the phases of the upper-edge and lower-edge beat-frequency signals are continuous, the signal-to-noise ratio increases by 3 dB.

[0086] When Δφ = 2πN, N being an integer, that is, according to the formula Δφ = 2πτγ(t down_start -t up_end ), we get: N = τγ(t down_start -t up_end), the phase at the end time of the upper-edge beat frequency signal and the phase at the start time of the lower-edge beat frequency signal are continuous. The phase difference between the two signals is an integer multiple of 2π, so they are continuous in phase, which is equivalent to having no phase difference. The phase difference does not affect the continuity of the signal because 2π is equivalent to a complete cycle, and the shape of the signal is the same at the end of one cycle and the start of the next cycle. Ensuring the phase continuity of the two signals can maximize the synthesis effect of the signals and improve the signal-to-noise ratio.

[0087] Step 104: Select lower-edge data from the lower-edge beat frequency signal according to the target start point, obtain upper-edge data, concatenate the lower-edge data and the upper-edge data, and process the concatenated data.

[0088] Among them, the second algorithm can be the fast Fourier transform algorithm. The understanding of concatenation can be that the lower-edge data is spliced after the upper-edge data to form a complete and phase-continuous beat frequency signal. For example: According to the Hilbert transform, obtain the end time of the effective detection time period of the upper-edge data, so as to obtain the upper-edge data including: 1, 2, 3, 4, 5; the lower-edge data is 4, 5, 6, 7. After determining the start point of the lower-edge data as 6, 7 according to the method in step 103, then concatenate 1, 2, 3, 4, 5 and 6, 7. In this way, the signal-to-noise ratio of the beat frequency signal after the Fourier transform of the data 1, 2, 3, 4, 5, 6, 7 is higher than that of the beat frequency signal after the Fourier transform of the data 1, 2, 3, 4, 5.

[0089] Among them, in the preferred embodiment, as Figure 3 shown, the specific steps of step 104 include:

[0090] Step 1041: Obtain the lower-edge data according to the target start point in the lower-edge beat frequency signal, and obtain the upper-edge data according to the upper-edge beat frequency signal.

[0091] Among them, based on the requirement of phase continuity, determine the starting point of the lower-edge beat frequency signal. Ensure that the lower-edge beat frequency signal starts at an appropriate time point after the end of the upper-edge beat frequency signal. Once the start point of the lower-edge beat frequency signal is determined, the lower-edge data can be collected. These data reflect the signal characteristics from the start time of the lower-edge beat frequency signal.

[0092] Next, apply the Hilbert transform to the upper-edge beat frequency signal, that is, perform the Hilbert transform on the upper-edge signal to construct an analytic signal. Extract the instantaneous phase from the analytic signal, especially at the end time point of the upper-edge beat frequency signal. Using the phase information extracted from the Hilbert transform, the upper-edge data can be obtained. These data will contain important information about the end time of the upper-edge beat frequency signal, such as instantaneous phase, frequency, etc. Obtain the upper-edge data and the lower-edge data according to the above steps.

[0093] Step 1042: Concatenate the upper edge data and the lower edge data to obtain a concatenated beat signal.

[0094] Connect the upper edge data and the lower edge data together in chronological order to obtain a concatenated beat signal with a complete beat signal period.

[0095] Among them, compare the timestamps of the upper edge and the lower edge data one by one, and merge them into a new data sequence in chronological order. After merging, check whether the new data sequence reflects a complete beat signal period. Ensure that no data is lost during the merging process and the chronological order is correct.

[0096] Step 1043: Obtain the beat frequency and signal-to-noise ratio of the concatenated beat signal through fast Fourier transform.

[0097] Among them, use fast Fourier transform to transform the time-domain data of the concatenated beat signal, and convert it into frequency-domain data; in the fast Fourier transform result in the frequency domain, obtain the beat frequency by comparing the positions of the signal peaks in the frequency domain; according to Formula 1, the signal-to-noise ratio is obtained as:

[0098] Formula 1 is: SNR = 10 * log 10 P s / P n ;

[0099] Among them, SNR is the signal-to-noise ratio; P s is the average power of the signal; P n is the average power of the noise.

[0100] First, through fast Fourier transform, convert the time-domain data of the concatenated beat signal into frequency-domain data. In this way, the frequency distribution of the signal can be seen, so as to find the beat frequency characteristics. To obtain the signal-to-noise ratio, it is necessary to calculate the average power of the signal and the average power of the noise. Then, calculate the signal-to-noise ratio through the above Formula 1. More specifically, it is not elaborated much in this embodiment.

[0101] Actual processing comparison is as Figure 4 shown. Compared with only performing upper edge processing, the signal-to-noise ratio obtained by using the concatenated processing of the upper edge and the lower edge can be increased by about 3 dB. Therefore, the signal-to-noise ratio of the concatenated upper edge beat signal and the lower edge beat signal is optimal, which can improve the detection distance and detection accuracy of the FMCW lidar system.

[0102] Embodiment 2:

[0103] In Embodiment 1, a data processing method for an FMCW lidar is provided. In this embodiment, a data processing device for an FMCW lidar will be proposed. The data processing device for an FMCW lidar includes: a processor and a memory for storing instructions executable by the processor; wherein, the processor is configured to execute the data processing method for an FMCW lidar described in Embodiment 1.

[0104] As Figure 5 shown, there are a processor 21 and a memory 22, where the processor 21 and the memory 22 can be connected through a bus or other means.

[0105] The processor 21 can be a Central Processing Unit (CPU for short). The processor 21 can also be other general-purpose processors, Digital Signal Processors (DSP for short), Application Specific Integrated Circuits (ASIC for short), Field-Programmable Gate Arrays (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips.

[0106] The memory 22, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the data processing method for an FMCW lidar in Embodiment 1 of the present invention. The processor executes various functional applications and training processes of the processor by running the non-transitory software programs, instructions, and modules stored in the memory.

[0107] The memory 22 can include a program storage area and a training storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the training storage area can store trainings created by the processor, etc. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 22 optionally includes a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The one or more modules are stored in the memory 22 and, when executed by the processor 21, execute as Figure 1The data processing method for FMCW lidar in the illustrated embodiment.

[0109] For the specific details of the above data processing device for FMCW lidar, reference can be made correspondingly to Figure 1 , Figure 2 , Figure 3 and Figure 4 the relevant descriptions and effects corresponding to those in the illustrated embodiment, which will not be elaborated here.

[0110] This embodiment also provides a computer storage medium, which stores a computer program that can be executed by a processor to complete the data processing method for FMCW lidar described in Embodiment 1.

[0111] The computer storage medium stores computer-executable instructions that can execute the data processing method for FMCW lidar in any of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0112] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A data processing method for an FMCW lidar, characterized in that, Including: Obtain a beat frequency signal, select a signal of one period in the beat frequency signal as target data, and analyze the target data to obtain an upper-edge beat frequency signal and a lower-edge beat frequency signal; Obtain the phase at the end time of the upper-edge beat frequency signal; Select a target starting point from the lower-edge beat frequency signal so that the phase at the end time of the upper-edge beat frequency signal and the phase of the target starting point are continuous; Select lower-edge data from the lower-edge beat frequency signal according to the target starting point, and obtain upper-edge data, cascade the lower-edge data and the upper-edge data, and process the cascaded data.

2. The data processing method for FMCW lidar according to claim 1, characterized in that, The obtaining the beat frequency signal and selecting a signal of one period in the beat frequency signal as target data includes: In an FMCW lidar system, obtain the beat frequency signal by mixing the transmitted frequency-modulated signal and the signal reflected from the target object; select data of one period in the beat frequency signal as the target data.

3. The data processing method for an FMCW lidar according to claim 2, wherein The analyzing the target data to obtain an upper-edge beat frequency signal and a lower-edge beat frequency signal includes: The upper-edge beat frequency signal of the target data is: I bu (t,τ) = |A bu | cos{2πτγt + θ(t) - θ(t - τ)} t up_start ≤ t ≤ t up_end ; The lower-edge beat frequency signal of the target data is: I bd (t,τ) = |A bd | cos{2πτγt + θ(t) - θ(t - τ)} t down_start ≤ t ≤ t down_end ; Among them, τ is the round-trip time after the emitted optical signal reaches the target object; t up_start is the start time of the uplink effective detection region; t up_end is the end time of the uplink effective detection region; t down_start is the start time of the downlink effective detection time period; t down_end is the end time of the downlink effective detection time period; A bu is the constant amplitude of the upper-edge beat-frequency signal; |A bd | is the constant amplitude of the lower-edge beat-frequency signal; θ(t) is the phase noise of the laser; γ is the tuning rate of the scanning frequency; θ(t) - θ(t - τ) represents the phase difference between the transmitted signal and the received signal caused by the laser linewidth.

4. The data processing method for FMCW lidar according to claim 3, characterized in that, When τ is less than the coherence time of the laser, the upper-edge beat frequency signal and the lower-edge beat frequency signal can also be expressed as: Where φ0 is the initial phase of the upper-edge beat frequency signal, Δφ is the phase difference between the end time of the upper-edge beat frequency signal and the start time of the lower-edge beat frequency signal, and Δφ is expressed as: Δφ = 2πτγ(t down_start - t up_end ); Where τ is the round-trip time after the emitted optical signal reaches the target object; γ is the tuning rate of the scanning frequency, that is, the slope of the scanning frequency signal.

5. The data processing method for FMCW lidar according to claim 4, wherein The obtaining the phase at the end time of the upper-edge beat frequency signal, specifically: Extract the phase at the end of the upper-edge beat frequency signal from the target data through Hilbert transform.

6. The data processing method for FMCW lidar according to claim 4, wherein The selecting a target starting point from the lower-edge beat frequency signal so that the phase at the end time of the upper-edge beat frequency signal and the phase of the target starting point are continuous includes: Determine the starting point of the lower-edge beat frequency signal according to Δφ = 2πN, where N is an integer.

7. The data processing method for FMCW lidar according to claim 1, characterized in that, The selecting lower-edge data from the lower-edge beat frequency signal according to the target starting point, obtaining upper-edge data, cascading the lower-edge data and the upper-edge data, and processing the cascaded data includes: Obtain the lower-edge data according to the target starting point in the lower-edge beat frequency signal, and obtain the upper-edge data according to the upper-edge beat frequency signal; Cascade the upper-edge data and the lower-edge data to obtain a cascaded beat frequency signal; Obtain the beat frequency and the signal-to-noise ratio of the cascaded beat frequency signal through fast Fourier transform.

8. The data processing method for an FMCW lidar according to claim 7, wherein The cascading the upper-edge data and the lower-edge data to obtain a cascaded beat frequency signal includes: Connect the upper-edge data and the lower-edge data together in chronological order to obtain a cascaded beat frequency signal with a complete beat frequency signal period.

9. The data processing method for an FMCW lidar according to claim 7, characterized in that, The obtaining the beat frequency and the signal-to-noise ratio of the cascaded beat frequency signal through fast Fourier transform specifically includes: Use fast Fourier transform to transform the time-domain data of the cascaded beat frequency signal and convert it into frequency-domain data; In the fast Fourier transform result in the frequency domain, by comparing the positions of the signal peaks in the frequency domain, the beat frequency is obtained; The signal-to-noise ratio is obtained according to Formula 1: Formula 1 is: SNR = 10 * log 10 P s / P n ; where SNR is the signal-to-noise ratio; P s is the average power of the signal; P n is the average power of the noise.

10. A data processing device for an FMCW lidar, characterized in that, The data processing device for the FMCW lidar includes: a processor and a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the data processing method for the FMCW lidar according to any one of claims 1-9.

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