Pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting

Abstract

The invention belongs to the technical field of laser ranging, and discloses a pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting, and the method comprises the steps: collecting a pseudo-random modulation signal and an echo signal through a hardware system; carrying out the preprocessing of the collected pseudo-random modulation signal and echo signal through cross-correlation calculation and waveform interception; through K-means clustering iteration, rising edge and falling edge data points of the preprocessed cross-correlation signals are extracted; and fitting the obtained data points of the rising edge and the falling edge into a triangular model through a least square method, and resolving a target distance. According to the pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting, centimeter-level laser ranging under the condition of low signal-to-noise ratio is achieved.

Description

Technical Field

[0001] This invention relates to the field of laser ranging technology, and more particularly to a pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting. Background Technology

[0002] Laser ranging is a non-contact active distance measurement technology with advantages such as long measurement distance, high distance resolution, and insensitivity to changes in ambient light. It is the core technology of lidar 3D imaging and is widely used in fields such as autonomous driving, atmospheric detection, and topographic mapping.

[0003] Common lidar ranging methods are based on the time-of-flight (TOF) method, which involves emitting short laser pulses towards the target and calculating the target distance by the time difference between the pulse's round trip between the target and the ranging system. However, due to limitations such as laser power consumption and human eye safety, the power of short-pulse lasers is difficult to increase, making it difficult for traditional laser pulse-time-of-flight ranging methods to simultaneously achieve both detection range and ranging accuracy.

[0004] To address this issue, pseudo-random modulation laser ranging technology has emerged. However, existing pseudo-random modulation ranging technology struggles to further improve ranging accuracy; simultaneously, it is susceptible to atmospheric absorption and attenuation at long distances, resulting in low echo signal-to-noise ratios and hindering long-distance detection.

[0005] To overcome atmospheric attenuation and achieve long-distance, high-precision pseudo-random modulation continuous wave ranging, this invention discloses a pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting. Summary of the Invention

[0006] The purpose of this invention is to provide a pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting. The system uses a 2-micron band laser to illuminate the target, which has high atmospheric transmittance and is less affected by factors such as turbulence, thus improving the signal-to-noise ratio of long-distance echoes. By fitting the cross-correlation signal peaks into a triangular model to determine the laser flight time, the system's distance calculation accuracy is improved.

[0007] To achieve the above objectives, this invention provides a pseudo-random modulation ranging method based on K-means clustering and triangular fitting, comprising the following steps:

[0008] S1. Acquire pseudo-random modulation signals and echo signals through a hardware system;

[0009] S2. Preprocess the acquired pseudo-random modulation signal and echo signal through cross-correlation calculation and waveform truncation;

[0010] S3. Extract the rising and falling edge data points of the preprocessed cross-correlation signal through K-means clustering iteration;

[0011] S4. Using the least squares method, fit the rising and falling edge data points obtained in S3 into a triangular model to calculate the target distance.

[0012] Preferably, S1 is as follows:

[0013] S11. The M-sequence pseudo-random modulation signal generated by the signal generator is loaded onto the 2-micron band laser output by the semiconductor laser through the electro-optic modulator to obtain pseudo-random modulated continuous laser.

[0014] S12. The pseudo-random modulated continuous laser is amplified by an optical fiber amplifier, then emitted and received by an optical unit, and converted into an echo signal by a photodetector.

[0015] S13. Use a high-speed data acquisition card to synchronously acquire pseudo-random modulation signals and echo signals.

[0016] Preferably, S2 is as follows:

[0017] S21. The collected pseudo-random modulation signal and echo signal are cross-correlated to obtain the cross-correlation signal, as shown below:

[0018] y j =∑v j m i+ j;

[0019] Among them, y j It is a cross-correlation signal; v j It is the echo signal at the j-th sampling point; m i+j The pseudo-random modulated signal at the (i+j)th sampling point;

[0020] S22. Truncate the cross-correlation signal obtained in S21 to remove invalid noise waveforms. The truncation length N is determined by the modulation frequency of the signal generator and the electro-optic modulator, and the truncation is performed on the left side. Data points, cropped from the right. From the data points, the truncated cross-correlation signal is obtained.

[0021] Preferably, S3 is as follows:

[0022] S31. Divide the cross-correlation signal data points obtained in S22 into four categories, and set the initial value of the center point of each category;

[0023] S32. Calculate the sampling time and local slope of each data point in the truncated cross-correlation signal, and the distance between each data point and the center point of the four categories; classify the data points into the category with the smallest distance.

[0024] S33. Based on each category formed in S32, calculate the average sampling time and the average local slope of the data points within each category, and use them as the new center point of the corresponding category.

[0025] S34. Determine whether the four category centers obtained in S33 have changed. If they have changed, repeat steps S32 to S33; otherwise, the clustering ends, the classification results are stored, and the data points in the rising edge and falling edge categories are filtered out.

[0026] Preferably, S31 is as follows:

[0027] S311, with sampling time t j With local slope s j Based on the characteristics, the data points of the truncated cross-correlation signal are divided into four categories: left basis, right basis, rising edge, and falling edge, with the center points being (t) and (t') respectively. lb s lb ), (t rb s rb ), (t r s r ), (t f s f );

[0028] S312. Determine the initial values ​​of the center points of the four categories, according to the following rules:

[0029] (1) Initial value at sampling time:

[0030] Left base t lb The minimum sampling time of the truncated cross-correlation signal;

[0031] Right base t rb : The maximum sampling time of the truncated cross-correlation signal;

[0032] rising edge t r Falling edge t f : The average value of the cross-correlation signal at each sampling time after truncation;

[0033] (2) Initial slope value:

[0034] Left base s lb , right base s rb Set to 0;

[0035] rising edge s r Falling edge s f Set as experience value.

[0036] Preferably, S4 is as follows:

[0037] S41. Fit the rising and falling edge data points obtained in S3 into a triangular model, as shown below:

[0038]

[0039] Where y(t) is the triangular model of cross-correlation signals; k r It is the rising slope; -k r It is the slope of the falling edge; b r It is the rising edge intercept; b f It is the falling edge intercept; t is time; t p It is the peak position of the cross-correlation;

[0040] S42. Based on the least squares method, determine the three unknown parameters in the triangular model as follows:

[0041]

[0042] Where A is a matrix, specifically represented as:

[0043]

[0044] Where, n r n is the number of data points on the rising edge. f It represents the number of data points on the falling edge; j is the sampling point number; It is the sampling time of the j-th sampling point in the rising edge; It is the sampling time of the j-th sampling point in the falling edge;

[0045] b is a vector, specifically represented as:

[0046]

[0047] in, It is the cross-correlation amplitude of the j-th sampling point during the rising edge; It is the cross-correlation amplitude of the j-th sampling point in the falling edge;

[0048] S43. Based on the above triangular model parameters, calculate the cross-correlation peak position and solve for the target distance, as shown below:

[0049]

[0050] Where R is the target distance; c is the speed of light; t p It is the position of the cross-correlation peak, specifically represented as:

[0051]

[0052] Preferably, a pseudo-random modulation ranging system based on K-means clustering and triangulation fitting is provided. The system includes a semiconductor laser, a signal generator, an electro-optic modulator, a fiber amplifier, an optical unit, a photodetector, a high-speed data acquisition card, and a computer. The optical unit is equipped with an emission collimating lens and an echo converging lens.

[0053] Preferably, the semiconductor laser is connected to the electro-optic modulator via a single-mode optical fiber;

[0054] The signal generator is connected to the modulation pin of the electro-optic modulator and the input channel of the high-speed data acquisition card via radio frequency cables.

[0055] The electro-optic modulator is connected to the fiber amplifier via a single-mode fiber;

[0056] The fiber amplifier is connected to the optical unit via a single-mode fiber, and the end face of the fiber is placed at the focal point of the transmitting collimating lens of the optical unit.

[0057] The optical unit is connected to the photodetector via a multimode fiber, and the end face of the multimode fiber is placed at the focal point of the echo converging lens.

[0058] The photodetector is connected to channel two of the high-speed data acquisition card via an RF cable;

[0059] The high-speed data acquisition card connects to the computer via the PCIe bus.

[0060] Preferably, the semiconductor laser is used to output 2-micron wavelength laser;

[0061] The signal generator is used to generate an M-sequence pseudo-random modulation signal to intensity modulate a 2-micron band laser.

[0062] Electro-optic modulators are used to load M-sequence pseudo-random modulation signals onto 2-micron band lasers to generate pseudo-random modulated continuous lasers.

[0063] Fiber optic amplifiers are used to amplify the power of pseudo-randomly modulated continuous laser beams.

[0064] The echo-converging lens within the optical unit is used to receive the echo laser reflected from the target;

[0065] The photodetector converts the echo laser into an echo signal;

[0066] The high-speed data acquisition card simultaneously acquires pseudo-random modulation signals and echo signals;

[0067] The computer is used to process the collected data and calculate the target distance.

[0068] Therefore, the pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting described above in this invention have the following beneficial effects:

[0069] (1) The present invention uses 2-micron band laser to irradiate the target, which has high atmospheric transmittance and is less affected by turbulence and other factors, which is conducive to improving the signal-to-noise ratio of long-distance echo.

[0070] (2) This invention improves the accuracy of system distance calculation by fitting the cross-correlation signal peaks into a triangular model to determine the laser flight time.

[0071] (3) This invention has a wide range of applications in fields such as laser ranging and lidar detection.

[0072] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0073] Figure 1 This is a structural diagram of the pseudo-random modulation ranging system based on K-means clustering and triangular fitting of the present invention;

[0074] Figure 2 This is a flowchart of the pseudo-random modulation ranging method based on K-means clustering and triangular fitting of the present invention;

[0075] Figure 3 This is a schematic diagram of a pseudo-random modulation signal according to an embodiment of the present invention;

[0076] Figure 4 This is a schematic diagram of the echo signal according to an embodiment of the present invention;

[0077] Figure 5 This is a schematic diagram of the cross-correlation signal and ranging result in an embodiment of the present invention.

[0078] Figure Labels

[0079] 1. Semiconductor laser; 2. Signal generator; 3. Electro-optic modulator; 4. Fiber optic amplifier; 5. Optical unit; 6. Transmitting collimating lens; 7. Echo converging lens; 8. Photodetector; 9. High-speed data acquisition card; 10. Computer. Detailed Implementation

[0080] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0081] The pseudo-random modulation ranging method based on K-means clustering and triangular fitting of the present invention includes the following steps:

[0082] S1. Acquire pseudo-random modulation signals and echo signals through a hardware system.

[0083] S2. Preprocess the acquired pseudo-random modulation signal and echo signal through cross-correlation calculation and waveform truncation.

[0084] S3. Extract the rising and falling edge data points of the preprocessed cross-correlation signal through K-means clustering iteration.

[0085] S4. Using the least squares method, fit the rising and falling edge data points obtained in S3 into a triangular model to calculate the target distance.

[0086] A pseudo-random modulation ranging system based on K-means clustering and triangulation fitting is proposed. The system includes a semiconductor laser, a signal generator, an electro-optic modulator, a fiber amplifier, an optical unit, a photodetector, a high-speed data acquisition card, and a computer. The optical unit is equipped with an emission collimating lens and an echo converging lens.

[0087] Specifically, the semiconductor laser is connected to the electro-optic modulator via a single-mode fiber; the signal generator is connected to the modulation pin of the electro-optic modulator and channel one of the high-speed data acquisition card via RF cables; the electro-optic modulator is connected to the fiber amplifier via a single-mode fiber; the fiber amplifier is connected to the optical unit via a single-mode fiber, with the fiber end face positioned at the focal point of the transmitting collimating lens of the optical unit; the echo converging lens of the optical unit is connected to the photodetector via a multimode fiber, with the multimode fiber end face positioned at the focal point of the echo converging lens; the photodetector is connected to channel two of the high-speed data acquisition card via an RF cable; the high-speed data acquisition card simultaneously acquires the pseudo-random modulation signal and the echo signal, and transmits them to the computer via the PCIe bus; the computer processes the acquired data and calculates the target distance.

[0088] Example

[0089] like Figure 1 As shown, a pseudo-random modulation ranging system based on K-means clustering and triangular fitting is used. The system includes a semiconductor laser 1, a signal generator 2, an electro-optic modulator 3, an optical amplifier 4, an optical unit 5, a transmitting collimating lens 6, an echo converging lens 7, a photodetector 8, a high-speed data acquisition card 9, and a computer 10.

[0090] Semiconductor laser 1 generates a 2-micron wavelength laser, which is input to the input terminal of electro-optic modulator 3. Signal generator 2 generates an M-sequence pseudo-random signal, which is input to electro-optic modulator 3 to pseudo-randomly modulate the 2-micron wavelength laser generated by semiconductor laser 1. Channel 1 of high-speed data acquisition card 9 acquires the pseudo-random modulation signal generated by signal generator 2. The pseudo-random modulation signal is as follows: Figure 3As shown; the pseudo-random modulated continuous laser output from electro-optic modulator 3 is input to fiber amplifier 4 for power amplification; the amplified laser output from fiber amplifier 4 is input to optical unit 5, and collimated by transmitting collimating lens 6 into parallel spatial light illuminating the target; the echo converging lens 7 in optical unit 5 converges the echo into photodetector 8, converting it into an echo signal; channel two of high-speed data acquisition card 9 acquires the echo signal output from photodetector 8, and the echo signal is as follows. Figure 4 As shown, the high-speed data acquisition card 9 uploads the pseudo-random modulation signal and echo signal to the computer 10 for processing.

[0091] Based on the above system, such as Figure 2 The pseudo-random modulation ranging method based on K-means clustering and triangular fitting of the present invention includes the following steps:

[0092] S1. Acquire pseudo-random modulation signals and echo signals through a hardware system;

[0093] S11. The M-sequence pseudo-random modulation signal generated by the signal generator 2 is loaded onto the 2-micron band laser output by the semiconductor laser 1 through the electro-optic modulator 3 to obtain pseudo-random modulated continuous laser.

[0094] S12. The pseudo-random modulated continuous laser is amplified by the fiber amplifier 4, and then emitted and received by the optical unit 5. The emitted laser is then converted into an echo signal by the photodetector 8.

[0095] S13. Use the high-speed data acquisition card 9 to synchronously acquire pseudo-random modulation signals and echo signals.

[0096] S2. The acquired pseudo-random modulation signal and echo signal are preprocessed by cross-correlation calculation and waveform truncation.

[0097] S21. The collected pseudo-random modulation signal and echo signal are cross-correlated to obtain the cross-correlation signal, as shown below:

[0098] y j =∑v j m i+ j;

[0099] Among them, y j It is a cross-correlation signal; v j It is the echo signal at the j-th sampling point; m i+j It is the pseudo-random modulated signal at the (i+j)th sampling point;

[0100] S22. The cross-correlation signal obtained in S21 is truncated to obtain a truncated cross-correlation signal. This removes invalid noise waveforms from the cross-correlation signal, reduces the computational load in subsequent steps, and improves operating efficiency. The specific details of the waveform truncation are as follows:

[0101] The maximum value of the cross-correlation signal obtained in S21 is truncated to the left. Data points, cropped from the right. The system outputs a waveform with a total length of N data points for subsequent processing. The waveform truncation length N is determined by the modulation frequency of the signal generator 2 and the electro-optic modulator 3. The truncation range is as follows: Figure 5 As shown.

[0102] S3. Extract the rising and falling edge data points of the preprocessed cross-correlation signal through K-means clustering iteration.

[0103] S31. Divide the cross-correlation signal data points obtained in S22 into four categories, and set the initial value of the center point of each category.

[0104] S311, with sampling time t j With local slope s j Based on the characteristic, the data points of the truncated cross-correlation signal are divided into four categories: left basis, right basis, rising edge, and falling edge, with their center points being (t) and (t', respectively). lb s lb ), (t rb s rb ), (t r s r ), (t f s f ).

[0105] S312. Determine the initial values ​​of the center points of the four categories, according to the following rules:

[0106] (1) Initial value at sampling time:

[0107] Left base t lb : The minimum sampling time of the cross-correlation signal after truncation.

[0108] Right base t rb : The maximum sampling time of the cross-correlation signal after truncation.

[0109] rising edge t r Falling edge t f : The average value of the cross-correlation signal at each sampling time after truncation.

[0110] (2) Initial slope value:

[0111] Left base s lb , right base s rb Set to 0.

[0112] rising edge s r Falling edge s f Set as experience value.

[0113] S32. Calculate the sampling time and local slope (t) of each data point in the truncated cross-correlation signal. j s j ), the distances to the center points of the four categories respectively; classify the data points into the category with the smallest distance.

[0114] S33. Based on each category formed in S32, calculate the sampling time t of the data points within each category. j Mean and local slope s j The mean is used as the new center point for the corresponding category.

[0115] S34. Determine if the four category centers obtained in S33 have changed. If they have changed, repeat steps S32 to S33; otherwise, the clustering ends, the classification results are stored, and data points in the rising edge and falling edge categories are filtered out, such as... Figure 5 As shown.

[0116] S4. Using the least squares method, fit the rising and falling edge data points obtained in S3 into a triangular model to calculate the target distance.

[0117] S41. Fit the rising and falling edge data points obtained in S3 into a triangular model, as shown below:

[0118]

[0119] Where y(t) is the triangular model of cross-correlation signals; k r It is the rising slope; -k r It is the slope of the falling edge; b r It is the rising edge intercept; b f It is the falling edge intercept; t is time; t p It is the position of the cross-correlation peak.

[0120] S42. Based on the least squares method, determine the three unknown parameters in the triangular model as follows:

[0121]

[0122] Where A is a matrix, specifically represented as:

[0123]

[0124] Where, n r n is the number of data points on the rising edge. f It represents the number of data points on the falling edge; j is the sampling point number; It is the sampling time of the j-th sampling point in the rising edge; It is the sampling time of the j-th sampling point during the falling edge.

[0125] b is a vector, specifically represented as:

[0126]

[0127] in, It is the cross-correlation amplitude of the j-th sampling point during the rising edge; It is the cross-correlation amplitude of the j-th sampling point during the falling edge.

[0128] S43. Based on the above triangular model parameters, calculate the cross-correlation peak position and solve for the target distance, as shown below:

[0129]

[0130] Where R is the target distance; c is the speed of light; t p It is the position of the cross-correlation peak, specifically represented as:

[0131]

[0132] Therefore, the pseudo-random modulation ranging system and method based on K-means clustering and triangular fitting described above in this invention have the following beneficial effects:

[0133] (1) The present invention uses 2-micron band laser to irradiate the target, which has high atmospheric transmittance and is less affected by turbulence and other factors, which is conducive to improving the signal-to-noise ratio of long-distance echo.

[0134] (2) This invention improves the accuracy of system distance calculation by fitting the cross-correlation signal peaks into a triangular model to determine the laser flight time.

[0135] (3) This invention has a wide range of applications in fields such as laser ranging and lidar detection.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A pseudo-random modulation ranging method based on K-means clustering and triangular fitting, characterized in that, Includes the following steps: S1. Acquire pseudo-random modulation signals and echo signals through a hardware system; S2. Preprocess the acquired pseudo-random modulation signal and echo signal through cross-correlation calculation and waveform truncation; S3. Extract the rising and falling edge data points of the preprocessed cross-correlation signal through K-means clustering iteration; S4. Using the least squares method, fit the rising and falling edge data points obtained in S3 into a triangular model to calculate the target distance.

2. The pseudo-random modulation ranging method based on K-means clustering and triangular fitting according to claim 1, characterized in that, S1 specifically refers to: S11. The M-sequence pseudo-random modulation signal generated by the signal generator is loaded onto the 2-micron band laser output by the semiconductor laser through the electro-optic modulator to obtain pseudo-random modulated continuous laser. S12. The pseudo-random modulated continuous laser is amplified by an optical fiber amplifier, then emitted and received by an optical unit, and converted into an echo signal by a photodetector. S13. Use a high-speed data acquisition card to synchronously acquire pseudo-random modulation signals and echo signals.

3. The pseudo-random modulation ranging method based on K-means clustering and triangular fitting according to claim 1, characterized in that, S2 specifically refers to: S21. The collected pseudo-random modulation signal and echo signal are cross-correlated to obtain the cross-correlation signal, as shown below: y j <∑v j m i+ j; Among them, y j It is a cross-correlation signal; v j It is the echo signal at the j-th sampling point; m i+j It is the pseudo-random modulated signal at the (i+j)th sampling point; S22. Truncate the cross-correlation signal obtained in S21 to remove invalid noise waveforms. The truncation length N is determined by the modulation frequency of the signal generator and the electro-optic modulator, and the truncation is performed on the left side. Data points, cropped from the right. From the data points, the truncated cross-correlation signal is obtained.

4. The pseudo-random modulation ranging method based on K-means clustering and triangular fitting according to claim 1, characterized in that, S3 specifically refers to: S31. Divide the cross-correlation signal data points obtained in S22 into four categories, and set the initial value of the center point of each category; S32. Calculate the sampling time and local slope of each data point in the truncated cross-correlation signal, and the distance between each data point and the center point of the four categories; classify the data points into the category with the smallest distance. S33. Based on each category formed in S32, calculate the average sampling time and the average local slope of the data points within each category, and use them as the new center point of the corresponding category. S34. Determine whether the four category centers obtained in S33 have changed. If they have changed, repeat steps S32 to S33; otherwise, the clustering ends, the classification results are stored, and the data points in the rising edge and falling edge categories are filtered out.

5. The pseudo-random modulation ranging method based on K-means clustering and triangular fitting according to claim 4, characterized in that, S31 specifically refers to: S311. Based on the sampling time and local slope, the data points of the truncated cross-correlation signal are divided into four categories: left base, right base, rising edge, and falling edge, with the center points being (t...). lb s lb ), (t rb s rb ), (t r s r ), (t f s f ); S312. Determine the initial values ​​of the center points of the four categories, according to the following rules: (1) Initial value at sampling time: Left base t lb The minimum sampling time of the truncated cross-correlation signal; Right base t rb : The maximum sampling time of the truncated cross-correlation signal; rising edge t r Falling edge t f : The average value of the cross-correlation signal at each sampling time after truncation; (2) Initial slope value: Left base s lb , right base s rb Set to 0; rising edge s r Falling edge s f Set as experience value.

6. The pseudo-random modulation ranging method based on K-means clustering and triangular fitting according to claim 1, characterized in that, S4 specifically refers to: S41. Fit the rising and falling edge data points obtained in S3 into a triangular model, as shown below: Where y(t) is the triangular model of cross-correlation signals; k r It is the rising slope; -k r It is the slope of the falling edge; b r It is the rising edge intercept; b f It is the falling edge intercept; t is time; t p It is the peak position of the cross-correlation; S42. Based on the least squares method, determine the three unknown parameters in the triangular model as follows: Where A is a matrix, specifically represented as: Where, n r n is the number of data points on the rising edge. f It represents the number of data points on the falling edge; j is the sampling point number; It is the sampling time of the j-th sampling point in the rising edge; It is the sampling time of the j-th sampling point in the falling edge; b is a vector, specifically represented as: in, It is the cross-correlation amplitude of the j-th sampling point during the rising edge; It is the cross-correlation amplitude of the j-th sampling point in the falling edge; S43. Based on the above triangular model parameters, calculate the cross-correlation peak position and solve for the target distance, as shown below: Where R is the target distance; c is the speed of light; t p It is the position of the cross-correlation peak, specifically represented as:

7. The pseudo-random modulation ranging method based on K-means clustering and triangulation fitting according to any one of claims 1-6, applied to a pseudo-random modulation ranging system based on K-means clustering and triangulation fitting, characterized in that... The system includes a semiconductor laser, a signal generator, an electro-optic modulator, a fiber amplifier, an optical unit, a photodetector, a high-speed data acquisition card, and a computer. The optical unit is equipped with a transmitting collimating lens and an echo converging lens.

8. The pseudo-random modulation ranging system based on K-means clustering and triangular fitting according to claim 7, characterized in that, The semiconductor laser is connected to the electro-optic modulator via a single-mode optical fiber; The signal generator is connected to the modulation pin of the electro-optic modulator and channel one of the high-speed data acquisition card via radio frequency cables. The electro-optic modulator is connected to the fiber amplifier via a single-mode fiber; The fiber amplifier is connected to the optical unit via a single-mode fiber, and the end face of the fiber is placed at the focal point of the transmitting collimating lens of the optical unit. The optical unit is connected to the photodetector via a multimode fiber, and the end face of the multimode fiber is placed at the focal point of the echo converging lens. The photodetector is connected to channel two of the high-speed data acquisition card via an RF cable; The high-speed data acquisition card connects to the computer via the PCIe bus.

9. The pseudo-random modulation ranging system based on K-means clustering and triangular fitting according to claim 8, characterized in that, Semiconductor lasers are used to output 2-micron wavelength lasers; The signal generator is used to generate an M-sequence pseudo-random modulation signal to intensity modulate a 2-micron band laser. Electro-optic modulators are used to load M-sequence pseudo-random modulation signals onto 2-micron band lasers to generate pseudo-random modulated continuous lasers. Fiber optic amplifiers are used to amplify the power of pseudo-randomly modulated continuous laser beams. The echo-converging lens within the optical unit is used to receive the echo laser reflected from the target; The photodetector converts the echo laser into an echo signal; The high-speed data acquisition card simultaneously acquires pseudo-random modulation signals and echo signals; The computer is used to process the collected data and calculate the target distance.

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