Laser ranging method, device and equipment and storage medium

Abstract

The invention provides a laser ranging method and device, equipment and a storage medium. The method comprises the following steps: acquiring an echo signal from a laser ranging module; performing multi-echo separation processing on the echo signal to generate a separated secondary echo amplitude; performing penetration compensation calculation on the secondary echo amplitude according to the glass thickness and the glass refractive index to generate a compensated secondary echo amplitude; dynamically adjusting the laser driving frequency according to the real-time detection distance; and generating a laser ranging result with an error smaller than a set threshold value based on the compensated secondary echo amplitude and the dynamically adjusted laser driving frequency. Through multi-echo separation, penetration compensation and dynamic adjustment of the laser frequency, the range finding precision is significantly improved, errors are reduced, and powerful technical support is provided for precise off-axis shooting in a complex bank environment.

Description

Technical Field

[0001] This application belongs to the field of ranging, and particularly relates to a laser ranging method, apparatus, device and storage medium. Background Technology

[0002] In bank security scenarios, traditional firearms sights such as iron sights, optical sights, and red dot sights are widely used, but they reveal numerous limitations when facing high-precision, high-dynamic, and high-risk security demands. Specifically, these traditional devices heavily rely on the shooter's personal experience, leading to low aiming efficiency and significantly reduced performance in complex and changing lighting conditions. Crucially, in complex environments like banks with numerous obstacles (such as metal frames and glass curtain walls), traditional firearms sights cannot adapt to off-axis shooting, where the firearm's axis of fire does not coincide with the line of sight. This severely limits the combat effectiveness and survivability of security personnel under concealed conditions.

[0003] Furthermore, traditional gun aiming systems lack intelligent target recognition and tactical assistance functions, failing to provide real-time decision support for security personnel. Simultaneously, they struggle to effectively integrate with intelligent security systems to form a comprehensive security protection system. In practical applications, these shortcomings often put security personnel at a disadvantage when facing armed assailants or those exploiting terrain advantages, making it difficult to ensure their own safety and effectively stop criminal activities. Summary of the Invention

[0004] The purpose of this application is to overcome the deficiencies in the prior art and provide a laser ranging method, apparatus, device and storage medium.

[0005] This application provides a laser ranging method, including:

[0006] Obtain echo signals from the laser ranging module;

[0007] Perform multi-echo separation processing on the echo signal to generate the amplitude of the separated sub-echoes;

[0008] Based on the glass thickness and glass refractive index, a penetration compensation calculation is performed on the secondary echo amplitude to generate a compensated secondary echo amplitude.

[0009] The laser drive frequency is dynamically adjusted based on the real-time detection distance.

[0010] Based on the compensated secondary echo amplitude and the dynamically adjusted laser drive frequency, a laser ranging result with an error less than a set threshold is generated.

[0011] Optionally, performing multi-echo separation processing on the echo signal includes:

[0012] Acquire the signal after sampling by the high-speed ADC;

[0013] Perform polyphase filtering downsampling on the sampled signal;

[0014] Perform spectral feature extraction using parallelized FFT;

[0015] Prediction error is generated based on sliding window AR modeling;

[0016] The dynamic threshold CFAR detection threshold is adjusted based on the prediction error.

[0017] Optionally, the step of performing penetration compensation calculation on the secondary echo amplitude based on glass thickness and glass refractive index includes:

[0018] Obtain the glass type identifier;

[0019] When the glass type identifier indicates bulletproof glass, penetration compensation is performed using a first compensation coefficient;

[0020] When the glass type identifier indicates ordinary glass, penetration compensation is performed using a second compensation coefficient.

[0021] Optionally, after acquiring the echo signal from the laser ranging module, the method further includes:

[0022] An adaptive notch filter is applied to the echo signal to suppress fixed-frequency reflection interference.

[0023] Optionally, the step of dynamically adjusting the laser driving frequency based on the real-time detection distance includes:

[0024] Acquire the echo signal received by the multi-antenna array;

[0025] Use space-time adaptive processing to suppress strong reflection clutter;

[0026] Generate guidance vectors based on AR model parameters;

[0027] The beamforming weights are adjusted based on the steering vector.

[0028] Optionally, the step of generating prediction error using sliding window AR modeling includes:

[0029] The window size is determined based on the reflective properties of the bank's glass curtain wall.

[0030] The Burg algorithm is used to update the coefficients of the AR model.

[0031] Optionally, adjusting the beamforming weights according to the steering vector includes:

[0032] Correcting the steering vector phase based on the multipath interference characteristics of the metal frame;

[0033] Adjust the zero-depression depth using the reflectivity of bulletproof glass.

[0034] This application also provides a laser ranging device, comprising:

[0035] The acquisition module acquires echo signals from the laser ranging module;

[0036] The separation module performs multi-echo separation processing on the echo signal to generate the amplitude of the separated sub-echoes.

[0037] The compensation module performs penetration compensation calculations on the secondary echo amplitude based on the glass thickness and glass refractive index to generate the compensated secondary echo amplitude.

[0038] The adjustment module dynamically adjusts the laser drive frequency based on the real-time detection distance.

[0039] The generation module generates a laser ranging result with an error less than a set threshold based on the compensated secondary echo amplitude and the dynamically adjusted laser drive frequency.

[0040] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0041] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the above-described method.

[0042] The beneficial effects of this application are:

[0043] This application provides a laser ranging method, comprising: acquiring an echo signal from a laser ranging module; performing multi-echo separation processing on the echo signal to generate a separated secondary echo amplitude; performing penetration compensation calculation on the secondary echo amplitude based on the glass thickness and glass refractive index to generate a compensated secondary echo amplitude; dynamically adjusting the laser driving frequency according to the real-time detection distance; and generating a laser ranging result with an error less than a set threshold based on the compensated secondary echo amplitude and the dynamically adjusted laser driving frequency. This application significantly improves ranging accuracy and reduces errors through multi-echo separation, penetration compensation, and dynamic adjustment of the laser frequency, providing strong technical support for precise off-axis shooting in complex banking environments. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a laser ranging process in this application;

[0045] Figure 2 This is a schematic diagram of the overall system architecture in this application;

[0046] Figure 3This is a schematic diagram of the video processing flow in this application. Detailed Implementation

[0047] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is to be understood that various forms of implementation of the present disclosure are intended and should not be limited to the embodiments set forth herein. Rather, the embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0048] Please refer to Figures 1-3 As shown, this application provides a laser ranging method, including:

[0049] S101. Obtain the echo signal from the laser ranging module.

[0050] A laser ranging module refers to a laser ranging device that uses a 2×2 antenna array with an antenna spacing of less than λ / 2 (λ is the laser wavelength) to receive echo signals.

[0051] Acquiring echo signals refers to the laser ranging module emitting a laser and receiving the signal reflected back from the target. This signal includes direct echoes and multiple echoes generated by environmental factors such as glass curtain walls and metal frames.

[0052] Specifically, the laser ranging module receives echo signals through an antenna array and outputs analog signals to subsequent processing units. For example, in bank security scenarios, the laser ranging module is installed on a gun sight and aimed at the bank's glass curtain wall or interior environment to obtain distance information.

[0053] The data preprocessing steps of the attitude calculation module involve performing first-order Butterworth low-pass filtering on the six-axis sensor data. This means that based on the acceleration and angular velocity data in the X, Y, and Z directions of the six-axis sensor, a first-order Butterworth filter (cutoff frequency 2Hz) is applied to suppress high-frequency noise interference. This is implemented on the FPGA using fixed-point arithmetic, and the filtering parameters are dynamically configured based on the sensor output.

[0054] In bank security scenarios, high-frequency noise originates from environmental vibrations. Filters optimize data stability by lowering the cutoff frequency, while retaining low-frequency stable signals to improve the accuracy of subsequent attitude calculations. For example, when security personnel move in the bank lobby, sensor data undergoes this filtering process to reduce vibration-induced errors and ensure reliable data for AR calibration. The attitude calculation engine employs FPGA optimization based on extended Kalman filtering, performing complementary filtering to quickly fuse accelerometer (low-frequency stable) and gyroscope (high-frequency response) data, outputting a Q1.29 format quaternion value (1 sign bit + 29 decimal bits, range [-1, 1)), and adding a normalization verification module to check the conversion correctness.

[0055] Fixed-point computation is implemented using FPGA to ensure real-time performance, while a normalization module prevents data overflow. During rapid movements, quaternion values ​​are dynamically adjusted to maintain attitude accuracy. For example, in a bank standoff, rapid turning movements are processed by this engine, outputting stable attitude data for ballistic compensation.

[0056] S102. Perform multi-echo separation processing on the echo signal to generate the amplitude of the separated sub-echoes.

[0057] Multi-echo separation processing refers to the use of digital signal processing technology to separate the main echo and secondary echo in the echo signal in order to extract the amplitude of the secondary echo.

[0058] Generating the amplitude of the separated secondary echo refers to obtaining the amplitude value of the secondary echo through processing, which is used for subsequent compensation. Specifically, the multi-echo separation process includes the following sub-steps:

[0059] First, the signal sampled by the high-speed ADC is acquired, where the ADC has a 12-bit resolution and a 4GSPS sampling rate, and the echo signal is digitized.

[0060] Then, polyphase filtering downsampling is performed on the sampled signal to reduce the data rate and FPGA logic resource consumption;

[0061] Next, a parallelized FFTIP kernel (based on the Radix-2^2 algorithm) is used to perform spectral feature extraction, achieving low-latency (less than 10μs) spectral analysis;

[0062] Finally, prediction errors are generated based on sliding window AR modeling (using the Burg algorithm), and the dynamic threshold CFAR detection threshold is adjusted according to the prediction errors to reduce false alarms.

[0063] For example, in a bank glass curtain wall environment, the size of the sliding window is determined based on the reflective characteristics of the glass curtain wall, and the AR model coefficients are updated in real time using the Burg algorithm to adapt to the dynamic environment.

[0064] Before performing multi-echo separation processing on the echo signal, adaptive notch filtering is also performed to suppress fixed-frequency reflection interference. An adaptive notch filter (ANF) is used to process the echo signal to suppress fixed-frequency interference such as 50Hz power line noise. This is achieved by cascading the ANF filter after high-speed ADC sampling, and dynamically adjusting the filter parameters by analyzing the signal spectrum.

[0065] In bank glass curtain wall environments, the glass curtain walls can cause reflection interference at specific frequencies. ANF (Anti-Fluid Filtering) optimizes the filtering depth in real time based on echo characteristics to ensure efficient noise suppression on the FPGA. For example, when facing a bank's metal frame, ANF automatically adjusts to suppress 50Hz noise, reducing the false alarm rate. When generating prediction errors using sliding window AR modeling, the window size is determined based on the reflection characteristics of the bank's glass curtain wall, and the Burg algorithm is used to update the AR model coefficients.

[0066] Dynamically adjust model parameters to adapt to environmental changes: In high-reflectivity scenarios, the window size is reduced to improve response speed, and the Burg algorithm updates coefficients in real time to ensure accurate prediction.

[0067] After multi-echo separation processing, practical environmental adaptability tests were conducted to supplement comparative data on the impact of glass curtain wall type (e.g., single / double-layer, bulletproof glass) on distance measurement. This involved collecting distance measurement errors for different glass types experimentally to optimize the compensation algorithm. In a banking environment, the compensation effects of bulletproof glass (refractive index 1.8) and ordinary glass (refractive index 1.5) were tested to ensure the error was less than 2cm, and model parameters were adjusted through back-end analysis.

[0068] S103. Perform penetration compensation calculation on the secondary echo amplitude based on the glass thickness and glass refractive index to generate the compensated secondary echo amplitude.

[0069] Penetration compensation calculation refers to compensating for the attenuation effect of glass curtain walls on laser signals by adjusting the amplitude of the secondary echo to offset the signal loss caused by the glass. The generated compensated secondary echo amplitude is a corrected amplitude obtained through mathematical calculation, improving ranging accuracy. Specifically, the penetration compensation calculation uses the following formula:

[0070] = ×

[0071] Among them, the To compensate for the amplitude of the subsequent echo, the The amplitude of the second echo is d, where d is the glass thickness. is the refractive index of the glass.

[0072] Select the refractive index value according to the glass type label: when the glass type label indicates bulletproof glass, the refractive index is 1.8; when the glass type label indicates ordinary glass, the refractive index is 1.5.

[0073] For example, in a banking environment, glass thickness can be obtained through pre-measurement or sensor estimation, and after compensation, the secondary echo signal-to-noise ratio is improved by 40% and the ranging accuracy is improved by 15%.

[0074] When performing penetration compensation calculations, if the glass type indicator indicates bulletproof glass, the first compensation coefficient is used to perform penetration compensation, and the compensation intensity is adjusted according to the high density characteristics of the bulletproof glass.

[0075] Specifically, this involves substituting a refractive index of 1.8 into the formula. Bulletproof glass is commonly used in bank security, and its high refractive index leads to greater attenuation. The compensation coefficient is dynamically increased to offset the loss. For example, in a bank vault environment, when bulletproof glass is detected, a higher compensation value is automatically applied to ensure ranging accuracy.

[0076] When the glass type is indicated as ordinary glass, a second compensation factor is used to perform transmission compensation, calculated based on the standard refractive index of ordinary glass of 1.5. Ordinary glass is thinner, resulting in a smaller compensation factor and reduced error after optimization.

[0077] After penetration compensation calculation, the ballistic compensation calculation module uses the rotation matrix output by the attitude calculation to generate compensation values, using the following formula:

[0078]

[0079] Among them, the The rotation matrix is ​​given by: g = gravitational acceleration (9.8 m / s²), t = flight time, d = target distance. This is the initial velocity of the bullet.

[0080] After converting the quaternion into a rotation matrix, the three-directional compensation value is calculated, and the ballistic impact point is simulated based on the influence of gravity. For example, in off-axis shooting, the distance d comes from the laser rangefinder module, and the compensation value is used to dynamically adjust the crosshair position, ensuring efficient execution of matrix multiplication in the FPGA.

[0081] In later testing steps, the Q30 quaternion scaling factor was optimized by fixing the measured target landing point error and then adjusting the scaling factor. This included collecting error data and adjusting the scaling factor to improve accuracy. In the bank's testing range, the measured landing points were compared with the calculated values, and parameters were optimized to ensure system reliability.

[0082] S104. Dynamically adjust the laser drive frequency according to the real-time detection distance.

[0083] Real-time detection distance refers to the distance value currently measured by the laser ranging module. Dynamically adjusting the laser drive frequency refers to changing the laser emission frequency according to distance changes to optimize system power consumption and performance. Specifically, the adjustment strategy uses the following formula:

[0084] fupdata =

[0085] Wherein, fupdata is the laser driving frequency, and d is the real-time detection distance.

[0086] In addition, the process includes the following sub-steps: acquiring the echo signal received by the multi-antenna array; using space-time adaptive processing (STAP) to suppress strong reflection clutter and generating a steering vector in combination with AR model parameters; and adjusting the beamforming weights based on the steering vector.

[0087] For example, in a bank glass curtain wall environment, STAP uses a multi-antenna array to suppress fixed clutter, the steering vector is dynamically generated based on AR model parameters, and the beamforming weights are adjusted in real time according to the environment.

[0088] When using space-time adaptive processing to suppress strong reflection clutter, a steering vector is generated based on AR model parameters, involving the analysis of echo signal characteristics to generate the steering vector. The STAP algorithm, deployed within an FPGA, utilizes a multi-antenna array to suppress clutter. In a bank's metal frame environment, strong reflection clutter is effectively suppressed through this processing, and the steering vector is dynamically adjusted based on AR model parameters to ensure ranging stability.

[0089] When adjusting beamforming weights based on the steering vector, the steering vector phase is corrected based on the multipath interference characteristics of the metal frame, and the null depth is adjusted using the reflection intensity of bulletproof glass. In a banking environment, metal frames (such as door frames and railings) generate multipath interference. By analyzing multipath characteristics (such as delay and amplitude), the steering vector phase is corrected to make beamforming more accurate; simultaneously, based on the high reflectivity of bulletproof glass, the null depth is increased to further suppress clutter. For example, experimental tests show that the ranging error is reduced by 10% after adjustment. The logic is that phase correction cancels out multipath effects, and null depth optimization enhances signal clarity. After dynamically adjusting the laser drive frequency, the system implementation steps involve initialization of the gun sight device, including initializing the camera module and sensor module via the I2C protocol. This includes configuring the infrared camera (640*480 pixels) and the low-light RGB camera on the FPGA control board to meet power-on timing requirements. The power control module ensures that each component starts up in sequence to avoid conflicts; for example, after camera initialization, it waits for the self-organizing network signal to ensure synchronous processing.

[0090] S105. Based on the compensated secondary echo amplitude and the dynamically adjusted laser driving frequency, generate a laser ranging result with an error less than a set threshold.

[0091] Generating laser rangefinding results refers to calculating the final distance value with an error of less than 2cm (a set threshold). Specifically, the compensated second-echo amplitude is combined with the adjusted drive frequency, and the output distance value is calculated using methods such as time-of-flight. For example, in bank security scenarios, this result is used for AR calibration ballistic compensation to ensure shooting accuracy.

[0092] Image fusion algorithms are applied to the system output, including adaptive weight calculation, guided filtering improvement, and pixel-level fusion. In adaptive weight calculation, multi-feature fusion weights are used, combining the gradient magnitude of the infrared image (calculated via the Sobel operator) and local contrast (calculated via the Laplacian operator) to generate a weight map, highlighting edges while enhancing sensitivity in weak texture areas. This is incorporated into parallel processing of dual image inputs (RGB and infrared) within the FPGA. In bank surveillance scenarios, weak texture areas such as human contours are enhanced through the weight map to ensure accurate target recognition; for example, the Sobel operator detects edge gradients, the Laplacian enhances local changes, and the weight map is dynamically optimized to balance detail and noise. In guided filtering improvement, grayscale guidance reduces color information infiltration into the weight map. Motion features involve converting the RGB image to the Y channel (luminance) as a guide image to avoid color interference. Dynamic filter radius adjustment is included, adaptively setting the window size based on the infrared image gradient magnitude (e.g., using a small radius to preserve detail in high-gradient areas). In a bank environment, red warning light areas are prone to over-enhancement; grayscale guidance and edge smoothing eliminate weight fluctuations caused by noise. Pixel-level fusion uses the following formula:

[0093]

[0094] Wherein, W is the weight map after guided filtering optimization, α is the global fusion coefficient (dynamically adjusted according to actual results), and β is the infrared enhancement coefficient. The formula for calculating β is:

[0095]

[0096] Among them, the and For the range of infrared enhancement coefficients, the The value is the average brightness of the RGB image, where k is the attenuation coefficient (0.1-0.3, which needs to be calibrated).

[0097] The optimized weight map is fused with the processed image, based on ambient light intensity ( Dynamic adjustment of β, for example, in low-light banking environments, increases the β value to enhance the contribution of the infrared image, ensuring a clear fused image. On the head-mounted display (HMD) side, during the mode switching step, a rotary encoder controls the image output mode, involving cyclically switching the output mode (RGB, infrared, or fused image) via encoder switches. This includes real-time synchronous adjustment using internal FPGA algorithms.

[0098] When the user rotates the encoder, the FPGA dynamically updates the display mode without interrupting processing, such as quickly switching to infrared mode to penetrate obstacles during a bank standoff.

[0099] In the data transmission step, data is parsed via the USB 2.0 bus, including the self-organizing network receiving module transmitting data to the FPGA's USB 2.0 IP core for decompression. This involves the H.264 decoder processing image data and crosshair compensation values. After the data is written to the DDR cache, it is read according to the mode selection to ensure low-latency display; for example, merged images require additional processing, and the compensation values ​​are written to the display screen in real time.

[0100] This application also provides a laser ranging device, comprising:

[0101] The acquisition module acquires echo signals from the laser ranging module;

[0102] The separation module performs multi-echo separation processing on the echo signal to generate the amplitude of the separated sub-echoes.

[0103] The compensation module performs penetration compensation calculations on the secondary echo amplitude based on the glass thickness and glass refractive index to generate the compensated secondary echo amplitude.

[0104] The adjustment module dynamically adjusts the laser drive frequency based on the real-time detection distance.

[0105] The generation module generates a laser ranging result with an error less than a set threshold based on the compensated secondary echo amplitude and the dynamically adjusted laser drive frequency.

[0106] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the above-described method.

[0107] This application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0108] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the above-described method.

[0109] The above description of the embodiments is provided to enable those skilled in the art to understand and apply this application. Those skilled in the art will readily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without inventive effort. Therefore, this application is not limited to the above embodiments, and any improvements and modifications made to this application based on the disclosure thereof should be within the scope of protection of this application.

Claims

1. A laser ranging method, characterized by, The method comprises: obtaining echo signals from a laser ranging module; performing multi-echo separation processing on the echo signals to generate separated secondary echo amplitudes; performing penetration compensation calculation on the secondary echo amplitudes according to the glass thickness and the glass refractive index to generate compensated secondary echo amplitudes; dynamically adjusting the laser driving frequency according to the real-time detection distance; generating a laser ranging result with an error less than a set threshold based on the compensated secondary echo amplitudes and the dynamically adjusted laser driving frequency.

2. The method of claim 1, wherein, The method of performing multi-echo separation processing on the echo signals comprises: obtaining signals sampled by a high-speed ADC; performing multi-phase filtering and downsampling on the sampled signals; performing spectral feature extraction using parallelized FFT; generating prediction errors based on sliding window AR modeling; adjusting the dynamic threshold CFAR detection threshold according to the prediction errors.

3. The method of claim 1, wherein, The method of performing penetration compensation calculation on the secondary echo amplitudes according to the glass thickness and the glass refractive index comprises: obtaining a glass type identifier; when the glass type identifier indicates a bulletproof glass, performing penetration compensation using a first compensation coefficient; when the glass type identifier indicates a common glass, performing penetration compensation using a second compensation coefficient.

4. The method of claim 1, wherein, After obtaining the echo signals from the laser ranging module, the method further comprises: performing adaptive notch filtering on the echo signals to suppress fixed frequency reflection interference.

5. The method of claim 1, wherein, The method of dynamically adjusting the laser driving frequency according to the real-time detection distance comprises: obtaining echo signals received by a multi-antenna array; suppressing strong reflection clutter using space-time adaptive processing; generating a steering vector based on AR model parameters; adjusting beamforming weights according to the steering vector.

6. The method of claim 2, wherein, The method of generating prediction errors using sliding window AR modeling comprises: determining the window size according to the reflection characteristics of bank glass curtain walls; performing AR model coefficient update using the Burg algorithm.

7. The method of claim 5, wherein, The method of adjusting beamforming weights according to the steering vector comprises: correcting the phase of the steering vector based on the multipath interference characteristics of the metal frame; adjusting the null depth using the reflection intensity of the bulletproof glass.

8. A laser range finder device, characterized by, The method comprises: a obtaining module that obtains echo signals from a laser ranging module; a separation module that performs multi-echo separation processing on the echo signals to generate separated secondary echo amplitudes; a compensation module that performs penetration compensation calculation on the secondary echo amplitudes according to the glass thickness and the glass refractive index to generate compensated secondary echo amplitudes; an adjustment module that dynamically adjusts the laser driving frequency according to the real-time detection distance; a generation module that generates a laser ranging result with an error less than a set threshold based on the compensated secondary echo amplitudes and the dynamically adjusted laser driving frequency.

9. An electronic device, comprising: A computer program is stored in the memory and executed by the processor to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer and executed by the computer to implement the method of any one of claims 1-7.

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