CN201918032U - An anti-collision device for aircraft flying at low altitude - Google Patents
An anti-collision device for aircraft flying at low altitude Download PDFInfo
- Publication number
- CN201918032U CN201918032U CN2010206968009U CN201020696800U CN201918032U CN 201918032 U CN201918032 U CN 201918032U CN 2010206968009 U CN2010206968009 U CN 2010206968009U CN 201020696800 U CN201020696800 U CN 201020696800U CN 201918032 U CN201918032 U CN 201918032U
- Authority
- CN
- China
- Prior art keywords
- module
- aircraft
- radar
- computer system
- decision
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims description 4
- 230000000295 complement effect Effects 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 7
- 238000001514 detection method Methods 0.000 description 10
- 238000000034 method Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 239000013598 vector Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000012706 support-vector machine Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000000875 corresponding effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Images
Landscapes
- Position Fixing By Use Of Radio Waves (AREA)
- Traffic Control Systems (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
Description
技术领域technical field
本实用新型涉及飞行器飞行设备技术领域,具体涉及一种飞行器低空飞行防撞的装置。The utility model relates to the technical field of aircraft flight equipment, in particular to an anti-collision device for aircraft flying at low altitude.
背景技术Background technique
山脉,建筑物等对低空高速飞行的飞机而言都可能构成一种潜在的威胁。日本航空学会的统计分析显示,直升机飞行事故的50%是由于直接碰撞低可观测性障碍物所至。近地防撞系统旨在为飞机低空飞行提供安全保障,躲过与地面障碍物相撞的危险。解决近地防撞问题已经成为世界上许多国家关注并力求解决的一大难题,目前国内外都在积极进行有关飞机超低空防撞系统的研制。Mountains, buildings, etc. may pose a potential threat to aircraft flying at low altitude and high speed. Statistical analysis by the Aeronautical Society of Japan shows that 50% of helicopter flight accidents are due to direct collision with low-observable obstacles. The Ground Near Collision Avoidance System is designed to provide safety protection for aircraft flying at low altitudes and avoid the danger of colliding with ground obstacles. Solving the problem of near-ground collision avoidance has become a major problem that many countries in the world pay attention to and strive to solve. At present, both at home and abroad are actively developing the ultra-low altitude collision avoidance system for aircraft.
在抢险、救援等过程中,飞机时常要在极端条件如低能见度、超低空60m-200m下执行任务,其飞行安全极容易受到低空障碍物的威胁。如果飞机上的探测能力仅限于常规的观察如人眼或CCD相机等,则飞机与自然障碍物和人工障碍物发生碰撞的可能性就非常大。因此,抢险、救援等用飞机对近地防撞系统的探测能力提出了更高的要求。In the process of emergency rescue and rescue, aircraft often have to perform missions under extreme conditions such as low visibility and ultra-low altitude of 60m-200m, and its flight safety is extremely vulnerable to threats from low-altitude obstacles. If the detection capabilities on the aircraft are limited to conventional observations such as human eyes or CCD cameras, the possibility of the aircraft colliding with natural and artificial obstacles is very high. Therefore, aircraft used for rescue and rescue put forward higher requirements for the detection capability of the near ground collision avoidance system.
另外,目前国内对近地防撞系统的研究仅局限于近地告警系统,忽略了飞行器在危急状态下自动采取自救措施的研究。以森林灭火和水上救援为主要任务的蛟龙600为例而言,对这方面的研究提出了更高的要求。首先,在救火的同时首先必须保证飞行员以及飞机自身的飞行安全,另一方面,我们不能只考虑到潜在的威胁,救援行动必须分秒必争,因此对存在的威胁进行评估并决定是否采取躲避行为是非常必要的。其次,一般用于森林救火的水源来自就近的湖泊或海面,因此灭火飞机必须具备在较短时间内识别水面或陆地的能力,以便采取不同的运行模式。In addition, the current domestic research on the ground proximity avoidance system is limited to the ground proximity warning system, ignoring the research on the aircraft automatically taking self-rescue measures in critical conditions. Taking the Jiaolong 600, whose main tasks are forest fire fighting and water rescue, as an example, higher requirements are put forward for research in this area. First of all, while fighting the fire, the flight safety of the pilot and the aircraft itself must be ensured. On the other hand, we cannot only consider potential threats. Rescue operations must count against every second. Therefore, it is very important to assess the existing threats and decide whether to take evasive actions. necessary. Secondly, the water source generally used for forest firefighting comes from the nearest lake or sea, so the firefighting aircraft must have the ability to identify water or land in a short period of time in order to adopt different operating modes.
综上所述,研制一套具有探测能力强、能够对探测到的障碍进行尽快识别以及对威胁进行评估的近地防撞装置迫在眉睫。To sum up, it is imminent to develop a set of near-earth collision avoidance devices with strong detection capabilities, which can identify detected obstacles as soon as possible and assess threats.
实用新型内容Utility model content
本实用新型所要解决的问题是提供一种能够尽快进行障碍识别以及威胁评估的防撞装置。本实用新型为解决其技术问题所采取的技术方案是提供一种飞行器低空飞行防撞的装置,该装置由多探测器与之相连的计算机系统、人机接口系统、飞行自动控制系统组成:The problem to be solved by the utility model is to provide an anti-collision device capable of identifying obstacles and evaluating threats as soon as possible. The technical scheme that the utility model takes for solving its technical problem is to provide a kind of device of aircraft low-altitude flight anti-collision, and this device is made up of the computer system that multi-detector is connected with it, man-machine interface system, flight automatic control system:
所述的GPS与RS-422相连,用来确定飞行器的三维坐标;所述的高度计与RS-422相连,用来确定飞行器与地面高度;所述的雷达为微波雷达或激光雷达安装在飞行器头部位置与MAX485相连,产生波及波的传输途径,将探测器探测到的信息通过MIL-STD-1553总线送至计算机系统。Described GPS is connected with RS-422, is used for determining the three-dimensional coordinate of aircraft; Described altimeter is connected with RS-422, is used for determining the altitude of aircraft and ground; Described radar is that microwave radar or lidar are installed on aircraft head The external position is connected with MAX485 to generate a wave-to-wave transmission path, and the information detected by the detector is sent to the computer system through the MIL-STD-1553 bus.
所述的计算机系统包括依次相匹配的地形匹配计算模块、目标识别模块、态势威胁评估模块、决策模块;地形匹配计算模块分别与GPS和高度计连接,目标识别模块分别与雷达和摄像装置连接,决策模块连接至人机接口和飞行自动控制系统。Described computer system comprises the terrain matching computation module that matches successively, target recognition module, situation threat assessment module, decision-making module; Terrain matching computation module is connected with GPS and altimeter respectively, target recognition module is connected with radar and camera respectively, decision-making The module is connected to the human-machine interface and the flight automation control system.
本实用新型可以运行在水陆及森林灭火的复杂场合,其首先通过多探测器装置对障碍物进行探测,然后通过D-S证据理论算法对其障碍物行目标分类,再根据目标分类结果进行威胁评估,并针对威胁程度做出综合的应对决策,最后将结果显示或者通过语音告知飞行员。The utility model can be operated in the complex occasions of water, land and forest fire fighting. It firstly detects obstacles through a multi-detector device, and then classifies the obstacles according to the D-S evidence theory algorithm, and then conducts threat assessment according to the target classification results. And make a comprehensive response decision based on the threat level, and finally display the result or inform the pilot through voice.
与现有技术相比,本实用新型一种飞行器低空飞行防撞的装置由于采用了多探测器配置,因此探测能力强,有效的提高了障碍识别以及威胁评估能力。本实用新型在一定程度上保证飞行员人身安全以及减小飞机失事率,其能够运行在抢险、救援等特殊场合,填补了国内拓宽了防撞技术在航空领域的应用,也为我国进一步发展和完善近地告警防撞系统提供了有力的支持。Compared with the prior art, the low-altitude flight collision avoidance device of the utility model adopts multi-detector configuration, so the detection capability is strong, and the obstacle identification and threat assessment capabilities are effectively improved. The utility model guarantees the personal safety of the pilots and reduces the accident rate of the plane to a certain extent. It can be operated in special occasions such as emergency rescue and rescue, which fills up the application of anti-collision technology in the aviation field in my country, and also contributes to the further development and improvement of our country. The ground proximity warning and collision avoidance system provides strong support.
附图说明Description of drawings
图1为本实用新型一种飞行器低空飞行防撞的装置结构示意图Fig. 1 is a kind of structural schematic diagram of the device structure of aircraft low-altitude flight anti-collision of the utility model
图2为本实用新型一种飞行器低空飞行防撞的装置态势威胁评估模块采用的Bayes网络模型结构示意图Fig. 2 is a schematic diagram of the structure of the Bayesian network model adopted by the device situational threat assessment module of a kind of aircraft low-altitude flight collision avoidance device of the utility model
图3为飞行自动控制系统的结构示意图Figure 3 is a schematic diagram of the structure of the flight automatic control system
具体实施方式Detailed ways
以下结合附图,具体说明本实用新型。Below in conjunction with accompanying drawing, specifically illustrate the utility model.
请参阅图1是本实用新型一种飞行器低空飞行防撞的装置结构示意图;Please refer to Fig. 1, which is a schematic structural diagram of a device for avoiding collisions in low-altitude flight of the utility model;
由多探测器与之相连的计算机系统、人机接口系统、飞行自动控制系统组成:It consists of a computer system connected with multiple detectors, a human-machine interface system, and an automatic flight control system:
所述的GPS与RS-422相连,用来确定飞行器的三维坐标;所述的高度计与RS-422相连,用来确定飞行器与地面高度;所述的雷达为微波雷达或激光雷达安装在飞行器头部位置,与MAX485相连,产生波及波的传输途径,将探测器探测到的信息通过MIL-STD-1553总线送至计算机系统。Described GPS is connected with RS-422, is used for determining the three-dimensional coordinate of aircraft; Described altimeter is connected with RS-422, is used for determining the altitude of aircraft and ground; Described radar is that microwave radar or lidar are installed on aircraft head The internal position is connected with MAX485 to generate a wave-to-wave transmission path, and the information detected by the detector is sent to the computer system through the MIL-STD-1553 bus.
计算机系统2包括依次相匹配的地形库21、地形匹配计算模块22、目标识别模块23、态势威胁评估模块24和决策模块25,地形库21、地形匹配计算模块22、目标识别模块23、态势威胁评估模块24和决策模块25依次连接;地形匹配计算模块22还分别与GPS11和高度计12连接;目标识别模块23分别与雷达13和摄像装置14连接;决策模块25分别连接至人机接口3和飞行自动控制系统4。监控器其分别与上述的决策模块25和人机接口3连接。
人机接口3与飞机的显示装置、耳机以及警告灯等连接,将决策模块25提供的决策支持输出给飞行员。本实用新型在此部分均采用现有技术,因此,在此不再做详细介绍。以下分别对多探测器1、计算机系统2和飞行自动控制系统4进行说明。The man-
多探测器1作为整套系统的检测装置,主要负责获取被测量的信息,其最终目的是用以满足信息的传输、处理、存储、显示、记录和控制等要求。对整套近地防撞系统而言,探测是实现所有功能的首要环节也是最重要的环节。As the detection device of the whole system, the multi-detector 1 is mainly responsible for obtaining the measured information, and its ultimate purpose is to meet the requirements of information transmission, processing, storage, display, recording and control. For the entire ground near-collision avoidance system, detection is the first and most important link to realize all functions.
在探测技术中,常用的主动传感器主要有超声波、红外、微波雷达和激光雷达四种。下表针对这四种传感器做了横向比较:In detection technology, commonly used active sensors mainly include ultrasonic, infrared, microwave radar and laser radar. The following table makes a horizontal comparison of these four sensors:
表一传感器横向分析比较表Table 1 Transverse analysis and comparison table of sensors
本实用新型针对大型灭火/救援水陆飞机运行的特殊工作环境,结合上述对各种传感器的分析比较,通过模拟人脑综合处理复杂问题的功能,充分利用多个传感器资源,通过对各种传感器及其观测信息的合理支配与使用,将各种传感器在空间和时间上的互补与冗余信息依据某种优化准则组合起来,产生对观测环境的一致性解释和描述。The utility model aims at the special working environment of large-scale fire extinguishing/rescue amphibious aircraft, combined with the above-mentioned analysis and comparison of various sensors, by simulating the function of the human brain to comprehensively deal with complex problems, making full use of multiple sensor resources, through the analysis of various sensors and The rational domination and use of its observation information combines the complementary and redundant information of various sensors in space and time according to a certain optimization criterion to produce a consistent interpretation and description of the observation environment.
本实用新型的多探测器1包括GPS11、高度计12、雷达13和摄像机14,GPS11和高度计12上都设置有超声波传感器和红外传感器,雷达13包括微波雷达和激光雷达。多探测器1合理有效地配置了整个多维探测器系统,多个多维探测器布置方案以及相互间的连接关系和提供信息的方式,突破了以往依靠单一传感器提供信息的传统模式,探测能力强。The utility model multi-detector 1 comprises GPS11, altimeter 12, radar 13 and video camera 14, and GPS11 and altimeter 12 are all provided with ultrasonic sensor and infrared sensor, and radar 13 comprises microwave radar and lidar. Multi-detector 1 rationally and effectively configures the entire multi-dimensional detector system, the layout scheme of multiple multi-dimensional detectors and the connection relationship among them and the way of providing information, breaking through the traditional mode of relying on a single sensor to provide information in the past, and has strong detection capabilities.
计算机系统2根据多探测器1提供的信息进行目标识别、态势威胁评估,并由确定的目标威胁程度,为飞行员提供决策支持。The
目标识别是态势威胁评估的前提,目标识别模块23分别与雷达13、摄像装置14以及地形匹配计算模块22连接,提取雷达回波信号的多普勒频率、极化特征、目标频率响应、谐波目标。地形匹配计算模块22又分别与GPS11、高度计12和地形库21连接,该目标识别模块分别进行基于地形数据库及GPS实时数据,由地形匹配计算模块22得出信息,如建筑、山脉、森林等的目标识别;雷达目标识别以及基于多源图像融合的目标识别。Target recognition is a prerequisite for situational threat assessment. The target recognition module 23 is connected to the radar 13, camera device 14, and terrain matching calculation module 22 to extract the Doppler frequency, polarization characteristics, target frequency response, and harmonics of the radar echo signal. Target. Terrain matching calculation module 22 is connected with GPS11, altimeter 12 and terrain storehouse 21 respectively again, and this target identification module carries out based on terrain database and GPS real-time data respectively, obtains information by terrain matching calculation module 22, as building, mountain range, forest etc. Target recognition; radar target recognition and target recognition based on multi-source image fusion.
探测器提供的多为多聚焦图像,即当摄像机拍摄与镜头距离不同的多个目标时,无法同时聚焦到这些目标使其清晰,分别聚焦到各个目标多次拍摄而得到多副图像。因此,本实用新型采用边界不变矩识别图像中的潜在目标,首先提取边缘特征,然后用边界矩特征描述各个目标特征。Most of the images provided by the detector are multi-focus images. That is, when the camera shoots multiple targets at different distances from the lens, it cannot focus on these targets at the same time to make them clear. Multiple images are obtained by focusing on each target separately. Therefore, the utility model adopts the boundary invariant moment to identify the potential target in the image, first extracts the edge feature, and then uses the boundary moment feature to describe each target feature.
另外,根据环境、障碍物的种类及影响,本实用新型将目标类型分为:飞机、建筑、山脉、电线电缆、飞鸟、树木、火、水、陆共九类。将对象的属性分为:位置,速度,时间,数量和强度共五个属性。In addition, according to the types and influences of the environment and obstacles, the utility model divides the target types into nine categories: airplanes, buildings, mountains, wires and cables, birds, trees, fire, water, and land. The properties of the object are divided into five properties: position, speed, time, quantity and strength.
目标识别是根据特征提取过程得到的目标物体的特征将目标从背景中分离出来,并确定目标的类型、位置以及其它有用的信息。目标识别通常有神经网络、支持向量机、D-S证据理论等方法,较之于神经网络,支持向量机能够根据有限的样本信息在模型的复杂性和学习能力之间寻求最佳折中,获得较高的泛化能力。D-S证据理论可以有效的处理不确定信息,但在冲突信息下,其归一化过程往往产生有悖常理的无效结果。因此,本实用新型采用支持向量机(SVM,Support Vector Machine)方法进行目标识别及分类。同时,为了提高SVM的运算速度,本实用新型在支持向量、决策函数方面对SVM算法进行改进。首先选择基向量,在高维特征空间内寻找一组基向量,基向量的个数小于支持向量并且所有样本都能够被这组样本子集通过线性混合重构,以便提高运算速度。然后确定决策函数,根据选定的基向量,确定相应的简化决策函数。Target recognition is to separate the target from the background according to the characteristics of the target object obtained in the feature extraction process, and to determine the type, location and other useful information of the target. Target recognition usually has methods such as neural network, support vector machine, and D-S evidence theory. Compared with neural network, support vector machine can seek the best compromise between the complexity of the model and the learning ability according to the limited sample information, and obtain more High generalization ability. D-S evidence theory can effectively deal with uncertain information, but under conflict information, its normalization process often produces invalid results that are counterintuitive. Therefore, the utility model adopts a support vector machine (SVM, Support Vector Machine) method to carry out target recognition and classification. At the same time, in order to increase the computing speed of the SVM, the utility model improves the SVM algorithm in terms of support vectors and decision functions. First, select the base vector, and find a set of base vectors in the high-dimensional feature space. The number of base vectors is smaller than the support vector and all samples can be reconstructed by linear mixing of this set of sample subsets, so as to improve the operation speed. Then determine the decision function, according to the selected basis vector, determine the corresponding simplified decision function.
态势威胁评估为决策支持提供基础,本实用新型的态势威胁评估模块24采用基于贝叶斯网络的态势评估方法,以贝叶斯网络为模型,结合专家知识构建的可计算的态势评估方法,最终将目标威胁程度定义为:高、中、低、无四个等级。如图2所示,其为本实用新型采用的Bayes网络模型结构示意图,首先,由多探测器装置1探测环境信息,得到GPS信息、高度信息、雷达信息以及图像信息后,再由目标识别模块23根据上述信息确定目标类型、目标位置、目标速度、目标时间、目标数量和目标强度。最后,态势威胁评估模块24根据目标的类型和属性确定目标威胁程度。Situational threat assessment provides the basis for decision support. The situational threat assessment module 24 of the present utility model adopts a situational assessment method based on Bayesian networks, using Bayesian networks as a model, combined with a computable situational assessment method constructed by expert knowledge, and finally The target threat level is defined as four levels: high, medium, low, and none. As shown in Figure 2, it is the Bayesian network model structure schematic diagram that the utility model adopts, at first, by
决策模块25分别与态势威胁评估模块24和人机接口3连接,其根据态势威胁评估模块24确定出的目标威胁程度,为飞行员提供决策支持。如图1所示,决策模块25还与飞行自动控制系统4连接,另外,其还连接一监控器,监控器又与人机接口3连接,当威胁程度超过境界线时,一旦飞行员未在指定时间内采取相应动作且威胁仍然存在,此时,决策模块25将障碍物的具体信息输出给飞行自动控制系统4,飞机将自动采取相应的避让措施。The decision-making module 25 is respectively connected with the situational threat assessment module 24 and the man-
以上分别对算机系统2的各个模块进行了说明,需要说明的是,上述的各个模块通常是交互和并行的,不存在截然划分的清晰界限。请参阅图3,为本实用新型一种飞行器低空飞行防撞的装置,其MIL-STD-1553总线与飞行自动控制系统连接,用于飞机的自动操纵和指令驾驶,本实用新型在现有技术的基础上位该系统增加了自动避撞系统41,自动避撞系统41分别与决策模块25和航机控制系统42连接。多探测器装置1探测到的障碍物信息依次通过目标识别模块23,态势威胁评估模块34和决策模块25,若最终需要进行避撞,但是飞行员在预先设定的时间内并没有执行相关避撞动作并且威胁仍然存在。这种情况下,决策模块25将该障碍物的具体信息(如类别,几何尺寸等)输入到自动避撞系统41,经过自动避撞系统41计算得出相应的防撞信号,并将防撞信号发送至航迹控制系统42,航迹控制系统42通过自动驾驶仪43控制自主舵机44,并最终通过对舵面45的控制操作实现飞机航向的改变以达到避撞效果。Each module of the
本实用新型一种飞行器低空飞行防撞的装置探测能力强、且能够进行障碍识别以及威胁评估,其能运行在水陆及森林灭火的复杂场合。另外,本实用新型还增加了自动避撞系统,当威胁超过警戒线时飞机能够自动采取避撞措施,提高了飞机的生存性,减轻了飞行员的负担。The utility model relates to an anti-collision device for low-altitude flight of an aircraft, which has strong detection ability, and can perform obstacle identification and threat assessment, and can operate in complex occasions of water, land and forest fire extinguishing. In addition, the utility model also adds an automatic collision avoidance system. When the threat exceeds the warning line, the aircraft can automatically take collision avoidance measures, which improves the survivability of the aircraft and reduces the burden on the pilot.
以上公开的仅为本实用新型的几个具体实施例,但本实用新型并非局限于此,任何本领域的技术人员能思之的变化,都应落在本实用新型的保护范围内。The above disclosures are only a few specific embodiments of the utility model, but the utility model is not limited thereto, and any changes conceivable by those skilled in the art should fall within the protection scope of the utility model.
Claims (4)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010206968009U CN201918032U (en) | 2010-12-31 | 2010-12-31 | An anti-collision device for aircraft flying at low altitude |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010206968009U CN201918032U (en) | 2010-12-31 | 2010-12-31 | An anti-collision device for aircraft flying at low altitude |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN201918032U true CN201918032U (en) | 2011-08-03 |
Family
ID=44417824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010206968009U Expired - Fee Related CN201918032U (en) | 2010-12-31 | 2010-12-31 | An anti-collision device for aircraft flying at low altitude |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN201918032U (en) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102568251A (en) * | 2011-12-13 | 2012-07-11 | 天利航空科技深圳有限公司 | Aircraft high-voltage wire anti-collision alarm device and aircraft |
| CN103344218A (en) * | 2013-06-18 | 2013-10-09 | 桂林理工大学 | System and method for measuring altitude of low-altitude unmanned plane |
| RU2496120C2 (en) * | 2011-12-30 | 2013-10-20 | Открытое акционерное общество "Корпорация "Фазотрон - научно-исследовательский институт радиостроения" | Multifunctional multirange scalable radar system for aircraft |
| US9030347B2 (en) * | 2012-05-03 | 2015-05-12 | Lockheed Martin Corporation | Preemptive signature control for vehicle survivability planning |
| CN104682267A (en) * | 2015-03-26 | 2015-06-03 | 国家电网公司 | Power transmission line fault removal device based on multi-axle air vehicle |
| CN104793630A (en) * | 2015-05-13 | 2015-07-22 | 沈阳飞羽航空科技有限公司 | Light airplane comprehensive obstacle avoiding system |
| US9240001B2 (en) | 2012-05-03 | 2016-01-19 | Lockheed Martin Corporation | Systems and methods for vehicle survivability planning |
| US9244459B2 (en) | 2012-03-07 | 2016-01-26 | Lockheed Martin Corporation | Reflexive response system for popup threat survival |
| CN106774405A (en) * | 2016-12-30 | 2017-05-31 | 华南农业大学 | Orchard plant protection unmanned plane obstacle avoidance apparatus and method based on three-level avoidance mechanism |
| CN106950564A (en) * | 2017-04-01 | 2017-07-14 | 荆州南湖机械股份有限公司 | A kind of extreme low-altitude blind area detection optimization device |
| CN107111958A (en) * | 2014-12-12 | 2017-08-29 | 亚马逊技术股份有限公司 | Commercial and General Aircraft Evasion Using Optical, Acoustic and/or Multispectral Mode Detection |
| CN107544332A (en) * | 2017-09-14 | 2018-01-05 | 深圳市盛路物联通讯技术有限公司 | Data processing method and related product |
| CN107636551A (en) * | 2016-09-22 | 2018-01-26 | 深圳市大疆创新科技有限公司 | A flight control method, device and intelligent terminal |
| CN107783544A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | A method for controlling the obstacle-avoiding flight of a single-rotor plant protection UAV |
| CN107783548A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | Data processing method based on multi-sensor information fusion technology |
| CN107783106A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | Data Fusion Method Between UAV and Obstacle |
| CN108924494A (en) * | 2018-07-13 | 2018-11-30 | 王新凤 | Aerial monitoring system based on ground |
| CN109445449A (en) * | 2018-11-29 | 2019-03-08 | 浙江大学 | A kind of high subsonic speed unmanned plane hedgehopping control system and method |
| CN109866676A (en) * | 2019-03-04 | 2019-06-11 | 福建德普柯发电设备有限公司 | Generator set for wall-mounted refrigerated container and adjusting method thereof |
| US10822110B2 (en) | 2015-09-08 | 2020-11-03 | Lockheed Martin Corporation | Threat countermeasure assistance system |
| WO2021082396A1 (en) * | 2019-11-01 | 2021-05-06 | 南京智慧航空研究院有限公司 | Unmanned aerial vehicle flight network modeling method based on low-altitude airspace restriction conditions |
| CN112804462A (en) * | 2021-02-20 | 2021-05-14 | 北京小米移动软件有限公司 | Multi-point focusing imaging method and device, mobile terminal and storage medium |
-
2010
- 2010-12-31 CN CN2010206968009U patent/CN201918032U/en not_active Expired - Fee Related
Cited By (31)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102568251A (en) * | 2011-12-13 | 2012-07-11 | 天利航空科技深圳有限公司 | Aircraft high-voltage wire anti-collision alarm device and aircraft |
| RU2496120C2 (en) * | 2011-12-30 | 2013-10-20 | Открытое акционерное общество "Корпорация "Фазотрон - научно-исследовательский институт радиостроения" | Multifunctional multirange scalable radar system for aircraft |
| US9244459B2 (en) | 2012-03-07 | 2016-01-26 | Lockheed Martin Corporation | Reflexive response system for popup threat survival |
| US9030347B2 (en) * | 2012-05-03 | 2015-05-12 | Lockheed Martin Corporation | Preemptive signature control for vehicle survivability planning |
| US9240001B2 (en) | 2012-05-03 | 2016-01-19 | Lockheed Martin Corporation | Systems and methods for vehicle survivability planning |
| CN103344218A (en) * | 2013-06-18 | 2013-10-09 | 桂林理工大学 | System and method for measuring altitude of low-altitude unmanned plane |
| CN107111958B (en) * | 2014-12-12 | 2021-06-18 | 亚马逊技术股份有限公司 | Commercial and general-purpose aircraft evasion methods using light, acoustic and/or multispectral mode detection and drones using the same |
| CN107111958A (en) * | 2014-12-12 | 2017-08-29 | 亚马逊技术股份有限公司 | Commercial and General Aircraft Evasion Using Optical, Acoustic and/or Multispectral Mode Detection |
| CN104682267A (en) * | 2015-03-26 | 2015-06-03 | 国家电网公司 | Power transmission line fault removal device based on multi-axle air vehicle |
| CN104793630A (en) * | 2015-05-13 | 2015-07-22 | 沈阳飞羽航空科技有限公司 | Light airplane comprehensive obstacle avoiding system |
| US10822110B2 (en) | 2015-09-08 | 2020-11-03 | Lockheed Martin Corporation | Threat countermeasure assistance system |
| CN107783548A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | Data processing method based on multi-sensor information fusion technology |
| CN107783544A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | A method for controlling the obstacle-avoiding flight of a single-rotor plant protection UAV |
| CN107783106A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | Data Fusion Method Between UAV and Obstacle |
| CN107783548B (en) * | 2016-08-25 | 2021-02-26 | 大连楼兰科技股份有限公司 | Data processing method based on multi-sensor information fusion technology |
| CN107783106B (en) * | 2016-08-25 | 2021-02-26 | 大连楼兰科技股份有限公司 | Data fusion method between unmanned aerial vehicle and barrier |
| CN107636551A (en) * | 2016-09-22 | 2018-01-26 | 深圳市大疆创新科技有限公司 | A flight control method, device and intelligent terminal |
| CN107636551B (en) * | 2016-09-22 | 2021-11-30 | 深圳市大疆创新科技有限公司 | Flight control method and device and intelligent terminal |
| CN106774405A (en) * | 2016-12-30 | 2017-05-31 | 华南农业大学 | Orchard plant protection unmanned plane obstacle avoidance apparatus and method based on three-level avoidance mechanism |
| CN106774405B (en) * | 2016-12-30 | 2019-09-10 | 华南农业大学 | Orchard plant protection drone obstacle avoidance apparatus and method based on three-level avoidance mechanism |
| CN106950564A (en) * | 2017-04-01 | 2017-07-14 | 荆州南湖机械股份有限公司 | A kind of extreme low-altitude blind area detection optimization device |
| CN107544332A (en) * | 2017-09-14 | 2018-01-05 | 深圳市盛路物联通讯技术有限公司 | Data processing method and related product |
| CN108924494A (en) * | 2018-07-13 | 2018-11-30 | 王新凤 | Aerial monitoring system based on ground |
| CN109445449B (en) * | 2018-11-29 | 2019-10-22 | 浙江大学 | A high subsonic UAV ultra-low altitude flight control system and method |
| CN109445449A (en) * | 2018-11-29 | 2019-03-08 | 浙江大学 | A kind of high subsonic speed unmanned plane hedgehopping control system and method |
| CN112026630A (en) * | 2019-03-04 | 2020-12-04 | 福州市长乐区三互信息科技有限公司 | Generator set for wall-mounted refrigerated container |
| CN109866676B (en) * | 2019-03-04 | 2020-10-16 | 福州市长乐区三互信息科技有限公司 | Generator set for wall-mounted refrigerated container and adjusting method thereof |
| CN109866676A (en) * | 2019-03-04 | 2019-06-11 | 福建德普柯发电设备有限公司 | Generator set for wall-mounted refrigerated container and adjusting method thereof |
| WO2021082396A1 (en) * | 2019-11-01 | 2021-05-06 | 南京智慧航空研究院有限公司 | Unmanned aerial vehicle flight network modeling method based on low-altitude airspace restriction conditions |
| CN112804462A (en) * | 2021-02-20 | 2021-05-14 | 北京小米移动软件有限公司 | Multi-point focusing imaging method and device, mobile terminal and storage medium |
| CN112804462B (en) * | 2021-02-20 | 2024-04-26 | 北京小米移动软件有限公司 | Multi-point focus imaging method and device, mobile terminal, and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN201918032U (en) | An anti-collision device for aircraft flying at low altitude | |
| Partheepan et al. | Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities | |
| US8509965B2 (en) | Integrated collision avoidance system for air vehicle | |
| JP5150615B2 (en) | Aircraft collision detection and avoidance system and method | |
| CN111196369B (en) | Collision avoidance device, avionics protection system, collision avoidance method, and computer program | |
| Liu et al. | A new approach of obstacle fusion detection for unmanned surface vehicle using Dempster-Shafer evidence theory | |
| CA3062578A1 (en) | Systems and methods for sensing and avoiding external objects for aircraft | |
| Skowron et al. | Sense and avoid for small unmanned aircraft systems: Research on methods and best practices | |
| CN114200471A (en) | Forest fire source detection system and method based on unmanned aerial vehicle, storage medium and equipment | |
| De Sousa et al. | Aerial forest fire detection and monitoring using a small UAV | |
| CN107577241A (en) | A kind of fire-fighting unmanned aerial vehicle flight path planing method based on obstacle avoidance system | |
| Tsintotas et al. | The MPU RX-4 project: Design, electronics, and software development of a geofence protection system for a fixed-wing vtol uav | |
| Zsedrovits et al. | Onboard visual sense and avoid system for small aircraft | |
| Shish et al. | Survey of capabilities and gaps in external perception sensors for autonomous urban air mobility applications | |
| Hutchings et al. | Architecting UAV sense & avoid systems | |
| US20200278703A1 (en) | Image Processing-Based Collision Avoidance System for Flight Vehicle and Flight Vehicle Including Same | |
| Vera-Yanez et al. | Optical flow-based obstacle detection for mid-air collision avoidance | |
| CN116560399B (en) | Unmanned aerial vehicle group fault isolation and formation reconstruction method and system based on bird group algorithm | |
| Sricharan et al. | Real-time drone detection using deep learning | |
| US11508244B2 (en) | Method, computer program product, system and craft for collision avoidance | |
| Minwalla et al. | Experimental evaluation of PICAS: An electro-optical array for non-cooperative collision sensing on unmanned aircraft systems | |
| Dolph et al. | Adversarial learning improves vision-based perception from drones with imbalanced datasets | |
| Rzucidło et al. | Simulation studies of a vision intruder detection system | |
| CN116884277A (en) | Configurable low-altitude environment perception and collision avoidance system design method | |
| Chakraborty et al. | A Machine Learning based approach to Detect Birds over and around the Runway before they Strike the Aircrafts |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20110803 Termination date: 20151231 |
|
| EXPY | Termination of patent right or utility model |