Unmanned aerial vehicle detection and identification system and method based on multispectral laser radar

Abstract

The invention provides an unmanned aerial vehicle detection and identification system and method based on a multispectral laser radar, and the system comprises a laser, a rotary table control module, a transmitting optical system, a receiving optical system, a photon intensity collector, a spectrometer collector, and an upper computer. The rotating platform is used for driving the transmitting optical system to move in space, the laser transmits laser to the miniature unmanned aerial vehicle through the transmitting optical system, echoes are transmitted to the photon intensity collector and the spectrometer collector through the receiving optical system, and the photon intensity collector and the spectrometer collector are connected with the upper computer through the high-speed data acquisition card. The turntable control module is used for controlling the rotating platform. According to the method, more intensity information and spectral information can be obtained through echoes, and the characteristics of the target are refined, so that the unmanned aerial vehicle target can be detected in a large amount of spectral data and point cloud data, and the method can be used for target detection of an anti-unmanned aerial vehicle system in the ground / sky.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle detection, and in particular to a system and method for detecting and identifying unmanned aerial vehicles based on a multispectral laser radar. Background Art

[0002] In recent years, some domestic units have carried out research on low-altitude defense systems, especially in the field of drone countermeasure systems. However, domestic defense equipment still has shortcomings such as single detection means, low comprehensiveness of system functions, and weak networking and coordination capabilities of equipment.

[0003] At present, with the continuous development and expansion of the "low, slow and small" flying object market, the "illegal flying" phenomenon of "low, slow and small" flying objects may have a serious adverse impact on public facilities. In recent years, people's concerns about the potential safety threats of "low, slow and small" flying objects have become increasingly serious. In response to this problem, in order to protect the personal safety of the public, the privacy safety of citizens, and the property safety, etc., relevant research on anti-"low, slow and small" flying object technology is being actively carried out, and different anti-"low, slow and small" flying object technology development strategies have been launched one after another. The new market for anti-"low, slow and small" flying object technology is also rapidly emerging.

[0004] At present, the main technical means to counter "low, slow and small" flying objects include detection, tracking and early warning technology, jamming technology, camouflage and deception technology, and destruction and capture technology. By collecting flight data and other information of "low, slow and small" flying objects, combined with jamming technology such as optoelectronic countermeasure technology, control information jamming technology and data link jamming technology, the "low, slow and small" flying objects can be reduced or even lose their flying ability. However, the spatial identification and positioning means of intercepting "low, slow and small" flying objects are relatively single, which is easily affected by various environmental uncertainties, resulting in poor accuracy and weak anti-interference. Therefore, it is very necessary to design a UAV detection and identification system and method based on multispectral lidar. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, the object of the present invention is to provide a UAV detection and identification system and method based on multispectral laser radar.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] The present invention provides a UAV detection and identification system based on a multispectral laser radar, comprising: a laser, a turntable control module, a rotating platform, a transmitting optical system, a receiving optical system, a photon intensity collector, a spectrometer collector and a host computer, wherein the transmitting optical system is arranged on the rotating platform, and the rotating platform is used to drive the transmitting optical system to move in space, the laser emits laser light to a micro UAV through the transmitting optical system, and the echo is transmitted to the photon intensity collector and the spectrometer collector through the receiving optical system, the photon intensity collector and the spectrometer collector are connected to the host computer through a high-speed data acquisition card, and the turntable control module is used to control the rotating platform.

[0008] The present invention also provides a method for detecting and identifying unmanned aerial vehicles based on a multispectral laser radar, comprising:

[0009] Assemble and connect the drone detection and identification system;

[0010] Obtain drones on the market and collect spectral data from them. The host computer performs SVM classification modeling based on the spectral data to obtain an SVM classification model.

[0011] Connect the laser and perform status check and parameter initialization;

[0012] Connect the spectrometer collector and perform status check and integration time setting on it;

[0013] Connect the high-speed data acquisition card, check its status, and then set the receiving channel and sending channel;

[0014] Connect the turntable control module, perform status check and parameter initialization on it, and set the rotation rate and turntable scanning range;

[0015] Start scanning to obtain the light intensity data, spectrum data and point cloud data of a certain point of the target in space;

[0016] Fusion of the point cloud data of the point with the spectral data of the point;

[0017] Based on the SVM classification model, the spectral data that meets the requirements of the UAV is screened, and the point cloud data in the space is screened in turn, and multi-source heterogeneous data is fused to obtain multi-source data;

[0018] Based on the obtained multi-source data, pointnet-based object recognition is performed to obtain the location information and classification results of the targets in space.

[0019] Preferably, the integration time is set to 200 ms.

[0020] Preferably, the point cloud data of a certain point is obtained, specifically:

[0021] Get the angle information (V_angle, H_angle) of the rotating platform at a certain point, where V_angle is the horizontal angle and H_angle is the pitch angle. According to the high-speed data acquisition card, the distance between the detection and recognition system and the target is DisValue. According to the distance formula (DisValue×cos(V_angle)×sin(H_angle), DisValue(i)×cos(V_angle)×cos(H_angle), DisValue(i)×sin(V_angle)), get the point cloud data of the target in space, that is, (x, y, z).

[0022] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0023] The present invention provides a UAV detection and identification system and method based on multi-spectral laser radar, the system includes a laser, a turntable control module, a rotating platform, a transmitting optical system, a receiving optical system, a photon intensity collector, a spectrometer collector and a host computer, the method includes assembling and connecting the UAV detection and identification system, acquiring UAVs on the market, and collecting spectral data thereof, the host computer performs SVM classification modeling based on the spectral data to obtain the SVM classification model, connecting the laser, and performing state verification and parameter initialization on the laser, connecting the spectrometer collector, and performing state verification and integration time setting on the laser, connecting the high-speed data acquisition card, and performing SVM classification modeling on the SVM classification model, Perform a state check, then set the receiving channel and the sending channel, connect the turntable control module, perform a state check and parameter initialization, set the rotation rate and the turntable scanning range, start scanning, obtain the light intensity data, spectral data and point cloud data of the target in space, fuse the point cloud data of the point with the spectral data of the point, filter the spectral data that meets the UAV based on the SVM classification model, and filter out the point cloud data in space in turn, perform multi-source heterogeneous data fusion, obtain multi-source data, perform pointnet-based object recognition based on the obtained multi-source data, and obtain the location information and classification results of the target in space. The present invention can obtain more intensity information and spectral information through echoes, so that the characteristics of the target can be refined, so that the method of detecting UAV targets in a large amount of spectral data and point cloud data can be used for anti-UAV systems on the ground / in the sky for target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] Figure 1 A structural block diagram of a UAV detection and identification system based on a multispectral laser radar provided in an embodiment of the present invention;

[0026] Figure 2 A flowchart of a method for detecting and identifying unmanned aerial vehicles based on a multispectral laser radar provided in an embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of the data processing flow;

[0028] Figure 4 A schematic diagram of an embodiment provided by the present invention;

[0029] Figure 5 This is a schematic diagram showing the UAV 3D point cloud results. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The purpose of the present invention is to provide a UAV detection and identification system and method based on multispectral lidar, which can obtain more intensity information and spectral information through echoes, so as to refine the understanding of the characteristics of the target, thereby being able to detect UAV targets in a large amount of spectral data and point cloud data, and can be used for target detection in anti-UAV systems on the ground / in the sky.

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] Figure 1 The structure block diagram of the UAV detection and identification system based on multi-spectral laser radar provided by the embodiment of the present invention is as follows: Figure 1 As shown, the present invention provides a UAV detection and identification system based on multi-spectral laser radar, including: a laser, a turntable control module, a rotating platform, a transmitting optical system, a receiving optical system, a photon intensity collector, a spectrometer collector and a host computer;

[0034] The laser may be a white light laser.

[0035] The emission optical system is used to emit laser light generated by the laser;

[0036] The transmitting optical system is arranged on a rotating platform, and the rotating platform is used to drive the transmitting optical system to move in space. The rotating platform is connected to the turntable control module, and the turntable control module is connected to the host computer. The host computer controls the movement of the rotating platform through the turntable control module, wherein the rotation control module can adopt a motor control system. The transmitting optical system includes three reflectors and one optical rotating mirror;

[0037] The receiving optical system is used to receive echoes, and the receiving optical system includes an optical rotating mirror, APD210, and a half mirror;

[0038] The photon intensity collector is used to collect light intensity data of echoes;

[0039] The spectrometer collector is used to collect spectral data of the echo;

[0040] The photon intensity collector and the spectrometer collector are connected to the host computer via a high-speed data acquisition card;

[0041] The host computer controls the turntable and reads the position information, obtains the spectral data and photon intensity data of the target at the position reflected by the light source, fuses multi-source heterogeneous data, identifies and processes the point cloud data, and displays the results, and performs UAV spectral data model training and point cloud data recognition based on PointNet.

[0042] The laser emits laser light through the transmitting optical system to the micro-UAV, and the echo is sent to the photon intensity collector and the spectrometer collector through the receiving optical system.

[0043] Support Vector Machine (SVM) is a machine learning algorithm used for classification and regression analysis. It can effectively process linearly separable and linearly inseparable data and construct optimal decision boundaries in high-dimensional space.

[0044] The core idea of ​​SVM is to find a hyperplane that separates sample points of different categories as much as possible and maximizes the interval between two categories. This hyperplane is called the maximum margin hyperplane, which can perform classification prediction well.

[0045] Specifically, SVM maps samples to a high-dimensional feature space so that the data can be linearly separated in the space. If a linear hyperplane cannot be found in the original input space to separate the data, SVM introduces a kernel function to transfer the computational complexity from the high-dimensional feature space to the original input space. Common kernel functions include linear kernels, polynomial kernels, and Gaussian kernels.

[0046] The training process of SVM is a convex optimization problem, the goal is to minimize the structural risk of the model. In the solution process, SVM only focuses on those samples near the decision boundary, which are called support vectors. This feature makes SVM have good robustness and generalization ability.

[0047] In general, SVM is a powerful machine learning algorithm that can process data in high-dimensional space and achieve classification and regression analysis by finding the optimal hyperplane. It has been widely used in many fields such as image recognition, text classification and bioinformatics.

[0048] like Figure 2 As shown, the present invention also provides a method for detecting and identifying unmanned aerial vehicles based on a multispectral laser radar, comprising:

[0049] Assemble and connect the drone detection and identification system;

[0050] Obtain drones from the market and collect spectral data from them. The host computer performs SVM classification modeling based on the spectral data to obtain the SVM classification model, which is specifically:

[0051] Build an SVM classification model, train it based on spectral data, and obtain a trained SVM classification model;

[0052] Connect the laser and perform status check and parameter initialization;

[0053] Connect the spectrometer collector and perform status check and integration time setting on it;

[0054] Connect the high-speed data acquisition card, check its status, and then set the receiving channel and sending channel;

[0055] Connect the turntable control module, perform status check and parameter initialization on it, and set the rotation rate and turntable scanning range;

[0056] Data processing flow Figure 3 As shown, scanning starts, and the turntable will continuously scan the set range to obtain the light intensity data, spectrum data and point cloud data of the target in space;

[0057] Fusion of the point cloud data of the point with the spectral data of the point;

[0058] Based on the SVM classification model, the spectral data that meets the requirements of the UAV is screened, and the point cloud data in the space is screened in turn, and multi-source heterogeneous data is fused to obtain multi-source data;

[0059] Based on the obtained multi-source data, pointnet-based object recognition is performed to obtain the location information and classification results of the targets in space.

[0060] The integration time is set to 200 ms.

[0061] Get the point cloud data of a certain point, specifically:

[0062] Get the angle information (V_angle, H_angle) of the rotating platform at a certain point, where V_angle is the horizontal angle and H_angle is the pitch angle. According to the high-speed data acquisition card, the distance between the detection and recognition system and the target is DisValue. According to the distance formula (DisValue×cos(V_angle)×sin(H_angle), DisValue(i)×cos(V_angle)×cos(H_angle), DisValue(i)×sin(V_angle)), get the point cloud data of the target in space, that is, (x, y, z).

[0063] The present invention provides a schematic diagram of an embodiment, such as Figure 4 As shown, the xiaomo DS motor is used, the high-speed acquisition card is adq8, there are 8 channels in total, each channel has a sampling rate of 1G, the selected target is the DJI Inspiration series of drones, and the spectrometer uses the ATP3330 series of Optiancheng.

[0064] The three-dimensional point cloud result display diagram of the UAV obtained during the experiment is shown in the figure Figure 5 shown.

[0065] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0066] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A drone detection and identification system based on multispectral laser radar, characterized in that: include: A laser, a turntable control module, a rotating platform, a transmitting optical system, a receiving optical system, a photon intensity collector, a spectrometer collector and a host computer. The transmitting optical system is arranged on the rotating platform, and the rotating platform is used to drive the transmitting optical system to move in space. The laser emits laser light through the transmitting optical system to the micro-UAV, and the echo is transmitted through the receiving optical system to the photon intensity collector and the spectrometer collector. The photon intensity collector and the spectrometer collector are connected to the host computer through a high-speed data acquisition card. The turntable control module is used to control the rotating platform.

2. A method for detecting and identifying unmanned aerial vehicles based on multispectral laser radar, characterized in that: include: Assemble and connect the drone detection and identification system; Obtain drones on the market and collect spectral data from them. The host computer performs SVM classification modeling based on the spectral data to obtain an SVM classification model. Connect the laser and perform status check and parameter initialization; Connect the spectrometer collector and perform status check and integration time setting on it; Connect the high-speed data acquisition card, check its status, and then set the receiving channel and sending channel; Connect the turntable control module, perform status check and parameter initialization on it, and set the rotation rate and turntable scanning range; Start scanning to obtain the light intensity data, spectrum data and point cloud data of a certain point of the target in space; Fusion of the point cloud data of the point with the spectral data of the point; Based on the SVM classification model, the spectral data that meets the requirements of the UAV is screened, and the point cloud data in the space is screened in turn, and multi-source heterogeneous data is fused to obtain multi-source data; Based on the obtained multi-source data, pointnet-based object recognition is performed to obtain the location information and classification results of the targets in space.

3. The method according to claim 2, characterized in that The integration time is set to 200 ms.

4. The method according to claim 2, characterized in that: Get the point cloud data of a certain point, specifically: Get the angle information (V_angle, H_angle) of the rotating platform at a certain point, where V_angle is the horizontal angle and H_angle is the pitch angle. According to the high-speed data acquisition card, the distance between the detection and recognition system and the target is DisValue. According to the distance formula (DisValue×cos(V_angle)×sin(H_angle), DisValue(i)×cos(V_angle)×cos(H_angle), DisValue(i)×sin(V_angle)), get the point cloud data of the target in space, that is, (x, y, z).

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