Rapid high-precision scanning type laser fuze data processing method

Abstract

The invention relates to a rapid high-precision scanning type laser fuze data processing method, and belongs to the technical field of laser detection and fuze control. According to the method, an echo signal of a line scanning laser radar is received, after shaping, multi-echo screening, distance calculation, speed correction and elevation conversion are carried out, an improved one-dimensional clustering algorithm is adopted to carry out rapid analysis on elevation data, and a target elevation and a confidence coefficient thereof are output for detonation moment prediction. The method abandons a traditional three-dimensional point cloud reconstruction process, has the advantages of being high in processing speed, high in anti-interference capability, small in resource occupation and the like, and is suitable for real-time target detection and fuze control of a high-dynamic missile-borne platform in a complex environment.

Description

Technical Field

[0001] This invention belongs to the field of laser detection and fuze control, specifically relating to a fast and high-precision scanning laser fuze data processing method. Background Technology

[0002] Traditional laser fuses mostly use the Time-of-Flight (TOF) mechanism for ranging, supplemented by point cloud generation and target modeling technology, to achieve accurate detection and identification.

[0003] However, laser fuses based on single-point ranging have a small ranging area and are easily affected by terrain, making it difficult to accurately determine the detonation time in areas with complex ground conditions. Obtaining a complete 3D point cloud often requires continuous frame data stitching and complex algorithm processing, such as point cloud classification and detection networks based on PointNet and PV-RCNN, which places high demands on the computational resources, power consumption, and response speed of the missile platform. At the same time, under the influence of interference factors such as rain, fog, and smoke in complex battlefield environments, the laser reflection signal may fluctuate significantly, resulting in a lot of noise in the final point cloud and frequent misidentification.

[0004] On the other hand, in laser fuze applications, it is often not necessary to reconstruct the complete target shape; rather, the primary goal is to predict the optimal detonation timing. Therefore, efficient time-series analysis and trend prediction based solely on distance information, without the need for point cloud modeling, can achieve a functional closed loop, significantly improving system stability and real-time performance. Currently, there is a lack of lightweight processing algorithms and robustness enhancement mechanisms to meet this requirement, which has become one of the key bottlenecks restricting the practical deployment of laser fuzes. Summary of the Invention

[0005] This invention provides a fast and high-precision scanning laser fuze data processing method, which uses the processing results as the output of the laser fuze for detonation timing calculation.

[0006] To address the above technical problems, this invention provides a fast and high-precision scanning laser fuze data processing method, characterized by the following steps: S1. Shape the echo signal received by the line-scan lidar and extract multiple echo pulses; S2. Select valid echo data from the multi-echo pulses; S3. Calculate the target distance value based on the effective echo data; S4. Correct the distance value based on the projectile's velocity and flight angle, and convert it into elevation data; S5. Perform cluster analysis on all elevation data within a frame, and output the final elevation result and its confidence level.

[0007] Furthermore, the echo signal shaping includes filtering and threshold segmentation to obtain an echo signal with a near-Gaussian waveform.

[0008] Furthermore, the effective echo data includes at least one of the following: first echo, last echo, and strongest echo.

[0009] Furthermore, the distance value is calculated using a quadratic curve inflection point fitting formula, and the ranging accuracy is ±2 cm under the conditions of a laser pulse width of 6-10 ns and a sampling rate of 1 GHz.

[0010] Furthermore, the speed correction includes: performing time compensation on the distance value based on the projectile's flight speed and the scanning timestamp.

[0011] Furthermore, the clustering analysis employs an improved one-dimensional DBSCAN algorithm, which has low computational overhead, supports parallel processing, and has a single-frame processing time of no more than 10 milliseconds.

[0012] Furthermore, the clustering analysis output includes: the elevation of the center point of each cluster, the amount of data, the total number of clusters, and the confidence level.

[0013] Furthermore, the method is implemented on an FPGA or MCU embedded platform, supporting parallel or pipelined processing.

[0014] Furthermore, the elevation data is obtained by projecting the distance value along the vertical direction of the missile's flight direction; in the one-dimensional DBSCAN algorithm, the distance is calculated using absolute difference, and the cluster radius and minimum sample number are dynamically set according to the elevation data distribution.

[0015] Beneficial effects: This invention achieves target proximity detection, anti-interference processing, and ideal detonation point calculation based solely on distance information obtained from linear laser scanning, without the need to generate a three-dimensional point cloud, thereby improving the fuze's environmental adaptability and response speed.

[0016] The present invention has the following advantages: No point cloud data construction is required, resulting in low computational overhead. This invention processes data solely based on distance information obtained from linear laser scanning, eliminating the need for complex operations such as 3D point cloud reconstruction and target modeling, significantly reducing memory and processor resource consumption. Single-frame processing time can be controlled within milliseconds, demonstrating high real-time performance and rapid response capabilities.

[0017] It exhibits strong resistance to interference from rain, fog, and smoke. By processing multi-echo ranging data, it not only eliminates noise but also effectively removes abnormal ranging values ​​caused by environmental factors such as rain, fog, and smoke, enhancing the stability and reliability of the fuze system in harsh environments. Furthermore, by abandoning the point cloud construction process and focusing on the essential requirements of the fuze system, it demonstrates good tolerance to the influence of terrain undulations, ground vegetation, and buildings.

[0018] Support for detonation prediction on highly dynamic platforms: This invention utilizes prior conditions such as relative velocity to accurately calculate target distance and is highly synchronized with system time. Simultaneously, the algorithm structure is simple and suitable for parallel or pipelined implementation on embedded platforms such as FPGAs and MCUs. It can be adapted to various missile-borne systems with different sizes and computing power requirements. Even under conditions of limited projectile volume, power supply, and heat dissipation, it can still stably operate at the optimal detonation time. It can achieve high-precision measurement of target distance on rapidly moving projectiles, accurately control detonation time, and improve combat effectiveness. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the application scenarios involved in this invention.

[0020] Figure 2 This is a schematic diagram of a laser echo signal.

[0021] Figure 3 This is a schematic diagram of a typical terrain side profile involved in this invention and the distribution of the corresponding elevation measurement results.

[0022] Figure 4 This is a schematic diagram illustrating the principle of laser scanning generating multiple echoes in rain, fog, smoke, and dust environments, as described in this invention.

[0023] Figure 5 This is the overall algorithm flowchart involved in this invention.

[0024] Figure 6 This is a flowchart of the speed correction and elevation calculation involved in this invention.

[0025] In the figure: 1. Projectile with laser fuse, 2. Laser scanning range, 3. Ground interference object, 4. Target, 5. Typical terrain of raised ground, 6. Distribution of ranging results of raised terrain, 7. Typical terrain of depressed ground, 8. Distribution of ranging results of depressed ground. Detailed Implementation

[0026] To make the objectives, contents, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in further detail below.

[0027] The present invention proposes a fast and high-precision scanning laser fuze data processing method, the specific steps of which are as follows: S1. Laser echo signal shaping; See Figure 5 After the projectile enters the maximum ranging range of the lidar, the laser fuze begins to operate. First, for projectiles shaped like... Figure 2 The laser echo signal is shaped, and noise is removed through filtering, threshold segmentation and other measures to obtain a near-Gaussian waveform echo signal and separate multiple echo data.

[0028] S2. Multi-echo pulse identification and screening; Based on the usage scenario, effective distance data (such as the first, last, and strongest echoes) are filtered from multiple echoes, and information such as the start position, width, peak value, and peak position of each echo is recorded. The filtered echo data is stored in a buffer according to different filtering conditions. During the continuous judgment process, the buffer is continuously updated until all laser echo data for that session has been judged, thus obtaining multiple echo data that meet the set conditions.

[0029] S3. Calculate the distance value; Based on the quadratic curve inflection point fitting formula, the precise distance value corresponding to each echo is calculated. Under the conditions of 6~10ns laser narrow pulse and 1GHz sampling rate, the ranging accuracy using the full-wave sampling method is as high as ±2 cm.

[0030] S4. Speed ​​correction and elevation calculation; Subsequently, the projectile's velocity and flight angle data are acquired. If there is no communication with the projectile, the velocity and flight angle data are acquired based on preset mission information. Due to the projectile's high flight speed, the ranging error caused by the difference in laser emission time during the scanning process needs to be corrected based on the velocity information, and distance compensation is performed with reference to the timestamp of the end of the scan for that frame. Then, based on the projectile's flight angle, the ranging value is projected onto the height direction, and the elevation data corresponding to each ranging point is calculated.

[0031] S5. Height value clustering; Finally, based on the improved one-dimensional DBSCAN algorithm, all elevation values ​​within a frame are clustered to obtain their distribution. Since the elevation data is one-dimensional, calculating the distance between data points and cluster cores in the DBSCAN algorithm clustering process only requires a simple subtraction operation, resulting in low computational overhead. Furthermore, it facilitates parallel computation, and the overall algorithm process can be controlled within 10ms.

[0032] S6. Output the final calculation results.

[0033] Due to the characteristics of lidar, flat terrain will occupy a larger proportion of the data, while small ground obstacles will form a few concentrated clusters. Environmental interferences such as rain, fog, smoke, dust, and sparse vegetation will be considered. Figure 4 The diagram illustrates that the elevation data corresponding to the echoes will be distributed in a relatively discrete manner, and will not be able to be clustered into independent clusters in the final clustering result. The algorithm involved in this invention can, based on information such as the center point of the cluster, the amount of data in each cluster, and the total number of clusters, not only obtain the final elevation result, but also calculate the confidence level of the elevation result, and output it together to the projectile system for data fusion with other detection devices to improve the accuracy of the fuze.

[0034] This invention, based on data acquired by line-scan laser imaging radar, abandons the computationally expensive and time-consuming point cloud modeling step. Based on the requirements of missile-borne fuses, it realizes a high-precision, high-speed, and interference-resistant laser fuse data processing algorithm, which efficiently meets the requirement of precise control of detonation time, while retaining the advantages of line-scan laser imaging radar data itself, such as a large detection field of view and high ranging accuracy.

[0035] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for rapid and high-precision scanning laser fuze data processing, characterized in that, Includes the following steps: S1. Shape the echo signal received by the line-scan lidar and extract multiple echo pulses; S2. Select valid echo data from the multi-echo pulses; S3. Calculate the target distance value based on the effective echo data; S4. Correct the distance value based on the projectile's velocity and flight angle, and convert it into elevation data; S5. Perform cluster analysis on all elevation data within a frame, and output the final elevation result and its confidence level.

2. The method according to claim 1, characterized in that, The echo signal shaping includes filtering and threshold segmentation to obtain an echo signal with a near-Gaussian waveform.

3. The method according to claim 1, characterized in that, The valid echo data includes at least one of the following: first echo, last echo, and strongest echo.

4. The method according to claim 1, characterized in that, The distance value is calculated using a quadratic curve inflection point fitting formula. Under the conditions of a laser pulse width of 6-10 ns and a sampling rate of 1 GHz, the ranging accuracy is ±2 cm.

5. The method according to claim 1, characterized in that, The speed correction includes: time compensation of the distance value based on the projectile's flight speed and the scan timestamp.

6. The method according to claim 1, characterized in that, The clustering analysis uses an improved one-dimensional DBSCAN algorithm, which has low computational overhead, supports parallel processing, and the processing time for a single frame does not exceed 10 milliseconds.

7. The method according to claim 1, characterized in that, The clustering analysis output includes: the elevation of the centroid of each cluster, the amount of data, the total number of clusters, and the confidence level.

8. The method according to any one of claims 1-7, characterized in that, The method is implemented on an FPGA or MCU embedded platform and supports parallel or pipelined processing.

9. The method according to claim 8, characterized in that, The elevation data is obtained by projecting the distance value along the direction perpendicular to the projectile's flight direction.

10. The method according to claim 8, characterized in that, In the one-dimensional DBSCAN algorithm, the distance is calculated using absolute difference, and the cluster radius and minimum number of samples are dynamically set according to the distribution of elevation data.

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