Mobile Networking through Sensing Human Behavior By

Wireless Sensor Network, 2012, 4, 162-166doi:10.4236/wsn.2012.46023 Published Online June 2012 (/journal/wsn)MNMU-RA: Most Nearest Most Used Routing Algorithm for Greening the Wireless Sensor NetworksHafiz Bilal Khalil, Syed Jawad Hussain ZaidiSchool of Electrical Engineering & Computer Sciences, National University of Sciences and Technology, Islamabad, PakistanEmail: {10mseetkhalil, 10mseejzaidi}@.pkReceived February 22, 2012; revised March 22, 2012; accepted April 10, 2012ABSTRACTWireless sensors are widely deployed in military and other organizations that significantly depend upon the sensed in-formation in any emergency situation. One of the main designs issues of the wireless sensor network (WSN) is the con-servation of energy which is directly proportional to the life of the networks. We propose most nearest most used rout-ing algorithm (MNMU-RA) for ad-hoc WSNs which vitally plays an important role in energy conservation. We find the best location of MNMU node for energy harvesting by apply our algorithm. Our method involves the least number of nodes in transmission of data and set large number of nodes to sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and enhance the life of the network.Keywords: Energy Efficiency; Wireless Sensor Networks; Routing1. IntroductionThe growth in wireless sensor networks and its applica- tions dramatically increased in last decade. Wireless sen- sor nodes are widely used in military surveillance, intel- ligence and targeting in war operations. Energy available at each sensor for sensing and communications is limited because of the cost constraints and smaller size, which affects the sensor application and network lifetime. The purpose of green networking is to overcome the carbon foot print, reduce the energy consumption and energy losses. Energy efficiency is an important issue to enhance the life time of the network. To achieve the green net- working every component of the network is integrated with energy efficient protocols, e.g., energy-aware rout- ing on network layer, energy-saving mode on MAC layer, etc. One of the most important components of the sensor node is the power source. In sensor networks generally there are three modes of power consumption: sensing, data processing, and communication. Compared to sensing and data processing, much more energy is required for data communication in a typical sensor node [1]. These are also categorized as sleep (idle) and wakeup (trans-mission) mode.In ad-hoc WSNs (Wireless Sensor Networks) always the nodes are cooperative, they sense and transmit their own data and also act as router to route the sensed infor- mation of other nodes towards the data center or gateway node which is connected to the internet. Most of the nodes consumed their power resource while transmitting the data of neighboring nodes. The scope of this paper is to minimize the power consumption in transmitting or routing process and set large number of nodes into sleep mode. The remaining sections of this paper organized as follows. Section 2 explains related work and current en-ergy efficient techniques for sensor networks. Section 3 introduces some problems and research issues in current work. Section 4 describes overview of network model, our proposed algorithm and proposed solution respec-tively. In Section 5 experiment, Results and comparisons are given.2. Related WorkEnergy efficiency is already achieved by many appro- aches. These approaches include energy aware protocol development and hardware optimizations, such as sleep- ing schedules to keep electronics inactive most of the time, dynamic optimization of voltage, and clock rate. In[2] Smart Dust motes are designed that are not more thana few cubic millimeters. They can float in the air, keep sensing and transmitting for hours or days. In [3] authors described the µAMPS wireless sensor node, it is hard- ware based solution in which they simultaneously con- sider the features of the microprocessors and transceivers to reduce the power consumption of the each wireless sensor node in network. Routing algorithms also play an important role to reduce the energy consumption during the routing of data. A lot of work is done in MAC layer and Mac protocols;MAC protocol for wireless sensorH. B. KHALIL, S. J. H. ZAIDI163networks is not like the traditional wireless MACs such as IEEE 802.11. One of the most important goals is en-ergy conservation, fairness and latency is less important [4].SMAC/AL (Sensor MAC with Adaptive Listening) is a famous MAC protocol for WSNs proposed by Ye et al. [5,6]. Main purpose of SMAC/AL is to reduce energy consumption. But in SMAC/AL without considering the distance among the nodes, all nodes unnecessarily con- sume the energy by transmitting information with con- stant power level. An energy efficient MAC protocol with adaptive transmit power scheme named ATPM (Adap- tive Transmit Power MAC) is proposed in [7]. By meas- uring the received power ATPM can calculate the dis- tance between the sender and the receiver, and then adap- tively choose the suitable transmit power level according to the propagation model and distance. The ATMP can not only conserve the energy source, but also decrease the collision probability. A Novel Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks (AN-CAEE) has been proposed [8]. It minimizes energy utili-zation during data transmission and energy consumptions are distributed uniformly among all nodes. Each cluster contains cluster head, each node send its data to cluster head with single hop transmission. And cluster transmits the combined data to the base station with multi hope transmission. This approach reduces energy consumption of nodes within the cluster.3. Problem StatementSensor nodes which are one hope away or closest to the gateway node always consume their power more quickly than others because they have to transmit the data of other nodes in addition to transmission of their own sensed information. In [9] a solution was proposed for such type of scenario by implementing the multiple base stations and periodically changing their positions. But the prob- lem is that if every time the most far away sensor trans- mits its data then major part of overall network energy will be consumed. Another solution for prolong the sen- sor network lifetime is to divide sensors nodes into dis- joint sets, such that all the targets completely covered by every set [3]. Authors consider that within an active sen- sor’s operational range a target is covered. These disjoint sets are activated in round robin fashion, such that at a time only one set is active. Sensors are into the active state in an active set and all other sensors are in a low- energy sleep state. According to this method almost half of the sensor remains active and remaining half goes to sleep mode which reduce energy down to 50%. To make it more efficient and conserve the larger amount of en- ergy we proposed an algorithm named as MNMU-RA (Most nearest most used routing algorithm). That algo- rithm finds the efficient placement of active sensor nodes and set other nodes into sleep mode. An issue is also re- solved by our algorithm, reducing the number of multiple base stations by finding out the best location for the base station without changing its location periodically.4. Synopsis of Our Network ModelIn this paper we deal with the issue of energy efficiency in wireless sensor networks for surveillance of a set of targets with known locality. Scenario of the network is chosen for armed forces purposes like surveillance of the boarder, battle fields and no go areas to acquire the in- formation about enemies and their locations without tak- ing the risk for human personal. We consider that a large number of sensors are distributed randomly in close prox- imity for monitoring and send the monitored information to a gateway node. All nodes are static and makes ad-hoc wireless sensor network. Every sensor nodes must moni-tor the area all the time in its operational range and each sensor has fixed transmission range. In network model we assume that each sensor has unique pre configured Id and Global/proactive routing algorithms are used. Main advantage of proactive algorithm is not route latency but drawback is the high maintenance overhead when many of the routes are never used.Proactive routing is appro-priate for networks with: Small size, low mobility and high communication rates. We proposed an algorithm called as most nearest most used routing algorithm for this purpose. By using MNMU-RA we can find the per-fect location of node for energy harvesting which also reduce the overall energy consumption and cost.4.1. Most Nearest Most Used Routing Algorithm Run shortest path routing algorithm or link state routing to find the shortest path for each node in the wireless sensor network. Calculate all the possible shortest paths for each node. Then find the MNMU node (Figure 1).∙ A node which is most nearest to the gateway node.∙Select a node which is used in maximum number of shortest paths.Figure 1. Location of selected MNMU node.H. B. KHALIL, S. J. H. ZAIDI 164In above network model we assumed that sensed in- formation is equally probable for all the nodes. Then we calculate the shortest path for the nodes A, B and C. Then we find out the nodes which are most nearest to the gate way node. In above network model there are only two nodes X and Y which are closer to the gateway node. Then for selection we give the preference to the node which is most used in shortest paths. In above model Y is node which is most used in all shortest paths. If nodes A, B and C transmit their data the entire time node Y will be included in their path. Then every node keeps its routes information towards the node Y for future communica- tions. Flow chart of our algorithm is given in Figure 2. 4.2. Proposed SolutionWe used our algorithm to find most nearest most used node in a network, that node should be active all the time while other sensors remain in sleep mode and keep sens- ing. As we use proactive routing so each sensor knows its path towards the MNMU node. If a node has to send its information before sending it will wake up the nodes along his route. When MNMU nodes receive the infor- mation it will forward the data to the gateway and sets all the nodes into sleep mode. The critical issue in this solu- tion is that if a node (MNMU node) remains active all the time then its energy source will be empty soon. We re- solve this issue by using the energy harvesting concept at MNMU node [1]. We can also use secondary batteryFigure 2. MNMU routing algorithm flow chart. which is rechargeable and coupled with photovoltaic cell[10]. If all the nodes can generate energy from light, vi-bration, heat etc [11,12] it will increase the system cost.We don’t need to replace all the nodes with secondary sources. By replacing only one node (MNMU node) re-solves the issue and slightly increases the cost of theoverall system. But effectively prolong the life time ofsensor network. A solution given by Gandham et al. [9]can be more energy efficient if we implement our pro-posed algorithm with every new location of mobile basestation. Split the network in equal parts and periodicallychange the position of base station in each part. Basestation can be easily implemented at the place of MNMUnode in each part of the network instead of replacing itoutside the network. MNMU node will reduce the multihop and number of transmission which directly reducethe energy consumption.5. ExperimentWe done the experiment by implementing our proposedalgorithm in a network and calculate the amount of en-ergy utilization using MATLAB. Then implement theconcept of disjoint set and analyze the values at same network. For simulation 20 nodes containing one gate-way node are distributed randomly in 30 meter squarearea. We consider the features of MICA2 mote platform.It is third generation mote specifically built for WSNs [4].MICA2 have selectable transmission power range whichoffers adjustable communication ranges, selected trans-mission range for each node is 10 meters. The packetlength is fixed at 200 bits. MICA2 usually operated with3 V battery and other features mentioned in Table 1.We divided our analysis in three parts; first we calcu-late the power consumption using disjoint sets methods[3], then we apply our algorithm and calculate & com-pare power consumption. Same network and topologytaken in which each node remains active all the time andno energy saving protocol and technique is implemented.Energy calculated during the 20 rounds, all nodes areactive in first five rounds in which they sense and trans-mit the data. After ten rounds there is no activity andnodes go to sleeping mode according to implemented Table 1. Features of MICA2 motes platform [12,13].Operation/Features UnitListening 8mA Receiving 10mA Transmission 17mA Sleep 19µA Radio Frequency 900 MHzCPU 8 bit Atmel at 8 MHzBandwidth 40KbpsH. B. KHALIL, S. J. H. ZAIDI165methodology. Calculated results are given in Figures3 and 4.Simulation ResultsFigure 3 shows the result comparison of energy con- sumption in different modes; sensing, Transmission and sleeping of network. In Figure 3(a) set of all the active nodes shown by blue line are transmitting the data with- out applying any energy saving protocol. During the transmission if all nodes are active they will keep trans- mitting the information to each other and maximum amount of energy is consumed. In disjoint system only active set take part in transmission and inactive nodesFigure 3. Power consumptions in different modes. (a) Trans- mission mode; (b) Power consume by sleeping nodes; (c)Power consume by active nodes in sleep mode. Figure 4. Result and comparison of energy consumption in different modes.remain inactive during the transmission of active set. Our proposed algorithm gives lowest amount of energy con- sumption because only the MNMU node and less number of nodes take part in transmission. Energy consumed by inactive nodes in sleeping modes is shown in Figure 3(b). Energy consumption of sleeping nodes is in µwatts. Ac- cording to our algorithm 19 nodes set to sleep mode and only one MNMU node is active. While Figure 3(c) shows the separately calculated energy consumption by active nodes when there is no activity and network is in idle mode. Similarly in sleeping mode only MNMU node remains active and rest of the network sets to sleep mode. Figure 4 shows the result of energy consumption of entire network in different rounds. In first 5 rounds we assume that there is no sensed information to send; all the nodes are active in listening mode and keep sensing. In 5 to 10 rounds nodes are transmitting their sensed in- formation to the gateway. After round 10 there is no ac- tivity and nodes set to sleep mode in sleep mode only energy consumed by active nodes are calculated and en- ergy consumed by sleeping nodes which is in µwatts is neglected. Our algorithm gives the minimum energy con- sumption during the transmission in which fewer num- bers of nodes take part in routing and also in sleep mode by keeping only MNMU node active.6. ConclusionWe presented the most nearest most used routing algo- rithm to reduce the energy utilization in wireless sensor networks. Using this algorithm we find the best location of energy harvested node in a network. Our algorithm involves least number of nodes during transmission and keeps one node active in sleep mode. That significantly reduces the energy consumption during the transmissionH. B. KHALIL, S. J. H. ZAIDI 166and sleep mode when there is no activity. An open re- search issue is the heterogeneity of energy resources of the nodes that must be resolved after practical imple- mentation in any network. In our solution there is uneven energy consumption due to the topology of the network and nature of data flow. But that uneven energy con- sumption is helpful to reduce the energy consumption of entire network7. Future DirectionDesired goal in wireless networks is energy efficiency to maximize the network life. Our algorithm can be used to find the location of cluster header quickly in novel clus- tering algorithm for energy efficiency in wireless sensor networks [8]. Further we can implement coding tech- niques to reduce the number of transmissions at MNMU node. Energy consumes per bit or per packet transmis- sion can be reduce. Number of packets can be transmit- ted as a single packet by applying x-or Operations which reduces the energy but may cause of slighter delay. To apply this technique sensor nodes must be smarter and have ability to do this quickly.REFERENCES[1]I. F. Akyildiz, T. Melodia and K. Chowdhury, “A Surveyon Wireless Multimedia Sensor Networks,” ComputerNetworks, Vol. 51, No. 4, 2007, pp. 921-960.doi:10.1016/net.2006.10.002[2]J. M. Kahn, R. H. Katz and K. S. J. Pister, “EmergingChallenges: Mobile Networking for Smart Dust,” Inter-national Journal of Communication Networks, Vol. 2, No.3, 2000, pp. 188-196.[3]M. Cardei and D. Z. Du, “Improving Wireless SensorNetwork Lifetime through Power Aware Organization,”Wireless Networks, Vol. 11, No. 3, 2005, pp. 333-340.doi:10.1007/s11276-005-6615-6[4]Q. Hu and Z. Z. Tang, “An Adaptive Transmit PowerScheme for Wireless Sensor Networks,” 3rd IEEE Inter-national Conference on Ubi-Media Computing, Jinhua, 5-7 July 2010, pp. 12-16.[5]W. Ye, J. Heidemann and D. Estrin, “An Energy-EfficientMAC Protocol for Wireless Sensor Networks,” Proceed- ings of the IEEE INFOCOM, New York, 23-27 June 2002, pp. 1567-1576.[6]W. Ye, J. Heidemann and D. Estrin, “Medium AccessControl with Coordinated Adaptive Sleeping for Wireless Sensor Networks,” IEEE/ACM Transactions on Network- ing, Vol. 12, No. 3, 2004, pp. 493-506.doi:10.1109/TNET.2004.828953[7]Q. Hu and Z. Tang, “ATPM: An Energy Efficient MACProtocol with Adaptive Transmit Power Scheme for Wire- less Sensor Networks,” Journal of Multimedia, Vol. 6, No.2, 2011, pp. 122-128. doi:10.4304/jmm.6.2.122-128[8] A. P. Abidoye and N. A. Azeez, “ANCAEE: A Novel Clus-tering Algorithm for Energy Efficiency in Wireless Sen- sor Networks,” Journal of Wireless Sensor Networks, Vol.3, No. 9, 2011, pp. 307-312. doi:10.4236/wsn.2011.39032 [9]S. R. Gandham, M. Dawande, R. Prakash and S. Venkate-san, “Energy Efficient Schemes for Wireless Sensor Net- works with Multiple Mobile Base Stations,” Global Tele- communications Conference, San Francisco, 1-5 Decem- ber 2003, pp. 377-381.[10]M. A. M. Vieira, C. N. Coelho, D. C. Silva and J. M. Mata,“Survey on Wireless Sensor Network Devices,” Proceed- ings of IEEE International Conference on Emerging Tec- hnologies and Factory Automation (ETFA’03), Lisbon, 16-19 September 2003, pp. 537-544.[11]J. Paradiso and T. Starner, “Energy Scavenging for Mo-bile and Wireless Electronics,” Pervasive Computing, Vol.4, No. 1, 2005, pp. 18-27. doi:10.1109/MPRV.2005.9 [12]V. Gungor and G. Hancke, “Industrial Wireless SensorNetworks: Challenges, Design Principles, and Technical Approaches,” IEEE Transactions on Industrial Electron- ics, Vol. 56, No. 10, 2009, pp. 4258-4265.doi:10.1109/TIE.2009.2015754[13]CrossBow, Mica2 Data Sheet./Products/Product_pdf_files/MICA%20data%20sheet.pdf。

In the era of smartphones,social gatherings have taken on a new dimension.The ubiquitous presence of these devices has both enriched and complicated the dynamics of social interactions.The Ubiquity of Smartphones:Smartphones have become an integral part of our lives,serving as a hub for communication,entertainment,and information.They are so prevalent that its rare to see a gathering where at least one person isnt checking their device.This has led to a shift in how we interact with one another,often blending the digital and the physical realms.Enhanced Communication:On one hand,smartphones have made it easier to stay connected.Before a gathering,we can easily coordinate through group chats,share locations,and even send reminders. During the event,they can be used to take photos,share updates on social media,and even facilitate games or activities that require digital interaction.Distractions and Disconnects:However,the constant presence of smartphones can also be a source of distraction. Conversations can be interrupted by notifications,and the temptation to check for updates or messages can lead to a lack of engagement in the present moment.This can create a sense of disconnect,where people are physically present but mentally elsewhere.The Impact on Social Skills:The reliance on smartphones during social gatherings may also affect our social skills. Facetoface interactions are essential for developing empathy,reading body language,and understanding social cues.When we are preoccupied with our devices,we miss out on these learning opportunities and may become less adept at navigating social situations. Balancing the Use of Technology:To make the most of social gatherings in the smartphone era,its important to strike a balance.Setting boundaries,such as designating phonefree zones or times,can encourage more meaningful interactions.Additionally,using technology to enhance the social experience,rather than detract from it,can help maintain the essence of human connection.The Role of the Host:Hosts play a crucial role in setting the tone for gatherings.By encouraging guests to engage with one another and limiting the use of smartphones,hosts can foster a more intimate and enjoyable atmosphere.This might involve organizing activities that require group participation or simply leading by example and being present in the conversation.Conclusion:While smartphones offer numerous benefits,they also present challenges in the context of social gatherings.By being mindful of their impact and making conscious decisions about their use,we can ensure that technology serves to enhance,rather than diminish, our social experiences.The key is to find a balance that allows us to enjoy the best of both worldsthe convenience and connectivity of smartphones and the warmth and depth of human interaction.。

关于网络传感器的调查Lan F.Akyildiz,Weilian Su,Yogesh Sankarasubramaniam和Erdal Cayirci Grorgia技术机构摘要如今，随着无线通信和电子技术的发展，使得廉价的网络传感器蒸蒸日上。

网络传感器可以使用在宽阔的领域（比如，人体保健，军事，家庭日用）。

对于不同的应用领域，研究者们正在解决不同的问题。

这篇文章介绍了当前网络传感器的研究前沿，解决方法是在它们相关的协议堆积成下讨论的。

这篇文章还介绍了开放性研究的问题，并且试图激发这个领域新的兴趣和研究方向。

介绍如今，随着无线通信和电子技术的发展，使得廉价的低功耗多功能的感测节点快速发展，这些感测节点尺寸非常小并且能够在短距离里面链接交流。

这些微型传感节点由感应、数据处理和通信模块组成，充分符合了感器网络的概念。

感器网络象征着一个在传统传感器基础上的意义重大的突破。

一个感器网络是由一大堆的传感节点组成的，这些传感节点密密麻麻地排列在内部或者内部的旁边。

这样就可以随意地部署难以接触的片域和抗故障抢救操作。

从另一方面来说，这同时意味着感器网络协议和算法必须拥有自我组织能力。

感器网络的另外一个独特的特征是传感节点之间的相互协作。

传感节点和嵌入的处理器相匹配。

而不是传输原始数据给节点负责融合，它们用它们的处理能力在当局采取简单的计算和传输必须的和部分处理好的数据。

以上所描述的特性确保了网络传感器能够拥有广泛的应用。

有一些应用包括医疗，军事和家庭。

在军事方面，比如，快速发展的有自我组织和差错容限特性的网络传感器让它们在军事命令，控制，交流，计算，智能，监视，侦查和瞄准系统有宽广的前途。

在医疗方面，传感节点能管理病人并且帮助残疾人。

其他一些商业应用包括管理投资，产品质量检测，事故地区管理。

这些网络传感器应用的实现需要无线和自组织网络技术。

尽管有许多协议和算法已经被提出用来解决传统无限和自组织网络技术，它们还不能适合这个有独特特性和应用要求的网络传感器。

doi:10.3969/j.issn.1001-893x.2021.06.013引用格式:蒋平,谢跃雷.一种民用小型无人机的射频指纹识别方法[J].电讯技术,2021,61(6):737-743.[JIANG Ping,XIE Yuelei.A radio fre-quency fingerprint identification method for civil small UAVs[J].Telecommunication Engineering,2021,61(6):737-743.]一种民用小型无人机的射频指纹识别方法∗蒋㊀平∗∗,谢跃雷(桂林电子科技大学宽带与智能信息技术中心,广西桂林541004)摘㊀要:随着民用无人机的普及,无人机 黑飞 事件频频发生,给公共安全带来极大隐患㊂为了实现对 黑飞 无人机的有效监管,通过提取遥控信号指纹特征对无人机识别是一种有效的方法㊂基于民用小型无人机遥控信号通常采用跳频通信这一特性,通过分形贝叶斯变点检测算法对实测无人机遥控信号的瞬态起始点进行检测,并提取信号瞬态部分所含有的指纹特征,由主成分分析法进行特征降维,最后采用多分类支持向量算法对该信号进行分类及识别㊂实验结果表明,采用射频指纹法能够完成无人机型号的区分以及同一型号无人机的区分㊂关键词:民用小型无人机;射频指纹;遥控信号;分类识别;分形贝叶斯变点检测开放科学(资源服务)标识码(OSID):微信扫描二维码听独家语音释文与作者在线交流享本刊专属服务中图分类号:TN971㊀㊀文献标志码:A㊀㊀文章编号:1001-893X(2021)06-0737-07A Radio Frequency Fingerprint Identification Methodfor Civil Small UAVsJIANG Ping,XIE Yuelei(Research Center for Wideband and Intelligence Information Technology,Guilin University of Electronic Technology,Guilin541004,China) Abstract:With the popularization of unmanned aerial vehicles(UAVs),the illegal incident of UAV hap-pens frequently,which brings a significant threat to public security.In order to achieve effective supervision for illegal UAV,it is an effective method to extract fingerprint features of remote control signals for UAV i-dentification.Based on the characteristic that frequency hopping communication is usually used in the re-mote control signal of civil small UAV,this paper uses fractal Bayesian change point detection algorithm to detect the transient starting point of the measured UAV remote control signal,and extracts the fingerprint features contained in the transient part of the signal.The feature dimension is reduced by principal compo-nent analysis(PCA).Finally,the multi-classification support vector machine(SVM)algorithm is used to classify the signal.The experimental results show that the radio frequency distinct native attribute(RF-DNA)method can be used to distinguish the UAV model and even the same type UAV.Key words:civil small UAV;RF-DNA;remote control signal;classification and recognition;fractal Bayes-ian change point detection0㊀引㊀言随着民用小型无人机技术的高速发展,因操作人员缺乏安全意识,无人机侵入机场㊁军事基地㊁重要会场的违法事件屡有发生,给国家和社会带来了㊃737㊃第61卷第6期2021年6月电讯技术Telecommunication Engineering Vol.61,No.6 June,2021∗∗∗收稿日期:2020-07-07;修回日期:2020-08-03基金项目:广西科技重大专项(桂科AA17202022);认知无线电与信息处理教育部重点实验室主任基金项目(CRKL180105);广西研究生教育创新计划项目(2020YCXS021);桂林电子科技大学研究生优秀学位论文培育项目资助(18YJPYSS07)通信作者:879702235@严重的安全隐患[1]㊂因此,加强对无人机的管控势在必行,而如何探测和发现无人机则是实现管控的第一步[2-4]㊂探测和识别无人机的射频信号,是发现无人机的一种有效方法[5-7]㊂民用小型无人机的射频信号可分为遥控信号及图传信号,遥控信号用于无人机控制,通常采用跳频方式的扩频通信信号,而无人机图传信号则用于空中拍摄视频的传输,通常采用正交频分复用技术(Orthogonal Frequency Division Mul-tiplexing,OFDM)的调制信号㊂许多学者通过无人机遥控信号对无人机进行检测及识别,其中文献[5]给出了一种基于无线电信号特征识别的无人机监测算法设计,从跳频信号及图传信号方面对无人机进行探测,但未给出具体算法分析及更近一步的实现原理;文献[6]提出基于软件无线电平台的无人机入侵检测,通过无人机跳频信号特征对无人机进行检测与识别,能在15m内检测无人机的存在,但该方法无法完成对无人机具体型号的区分;文献[7]采用对跳频信号进行图像分类的方式完成无人机信号的检测与识别,并取得了较好的识别效果,但跳频信号易受噪声淹没造成信号丢失,导致其不能较好地进行参数估计,从而无法有效区分无人机型号,并且该方法不能区分个体㊂针对以上检测及识别所存在的缺陷,本文采用射频指纹提取法(Radio Frequency Distinct Native At-tribute,RF-DNA)[8-9]对遥控信号进行检测及识别㊂首先零中频接收机对无人机遥控信号进行侦收,随后检测遥控信号瞬态部分起始点并进行统计特征提取,构造RF-DNA指纹特征并对其进行特征降维,最后由多支持向量机(Support Vector Machine, SVM)分类器对无人机型号以及同一型号的个体进行区分㊂1　无人机遥控信号模型对于无人机的检测与识别,需从信号方面进行分析㊂民用无人机遥控信号通常采用跳频方式进行扩频通信[10-11],因此遥控信号即为用于无人机控制的跳频信号㊂跳频信号因其具有较好的抗干扰能力,广泛用于通信对抗方面,而民用无人机的控制也在其列㊂信息数据m(t)通过信号调制器得到d(t),发射的跳频信号为S(t)=d(t)S FH(t)㊂(1)式中:S FH(t)是跳频信号,表达式为S FH(t)=AðN-1k=0w T(t-kT h)cos[2πf k(t-kT h)+φn]㊂(2)式中:N为频点个数;A为振幅;w T为宽度为T h的矩形窗,T h为跳频信号的跳频周期;f0,f1,f2, ,f k为调频频率集;φn为初始相位,n=0,1,2, ,N-1㊂实测无人机遥控信号离散数据由Cool Edit Pro 软件打开,如图1所示㊂图1㊀无人机遥控信号瞬态及稳态图㊃837㊃电讯技术㊀㊀㊀㊀2021年㊀㊀从图1可知,不同厂商无人机机型具有不同瞬态部分,但同一无人机型号的瞬态部分不易区分㊂本文主要基于民用无人机遥控信号瞬态部分进行研究㊂对于无人遥控信号瞬态部分,因无人机发射设备硬件特性不同,导致瞬态部分出现细微差异,这些差异主要由无人机发射设备系统中的分立器件㊁信号混频器㊁功率放大器㊁数模转换器㊁滤波器㊁锁相环等多种硬件设备产生㊂瞬态部分不携带数据信息,只与硬件设备本身的特性有关,具有唯一性,所以常对瞬态部分进行分析㊂瞬态部分存在于信号功率由零变为额定功率之间,所以一般存在发射设备开关机时刻㊂因此采集发射设备的瞬态部分具有一定难度,尤其体现在硬件接收设备[12]㊂由于无人机遥控信号采用跳频通信方式,在操控无人机期间,信号会不停经历由功率零到额定功率的变化过程,所以采用RF -DNA 方法对无人机遥控信号进行检测及识别是一个有效的方法㊂2㊀基于RF -DNA 的无人机识别2.1㊀RF -DNA 特征提取RF -DNA 方法是近年来较为关注方法之一,最早由美国空军技术学院Temple 等人提出㊂该方法是一种采用统计方法生成射频指纹(Radio Frequen-cy Fingerprinting,RFF)特征的计算框架,可分成瞬态信号子区域划分㊁瞬态信号基础特征生成和瞬态信号统计特征生成㊂对于该算法,对其分步骤描述㊂Step 1㊀对接收信号X (n )进行希尔伯特变换,得其解析式:X (n )=I (n )+j Q (n )㊂(3)式中:I (n )㊁Q (n )为正交信号㊂Step 2㊀求信号瞬时幅度a (n )㊁瞬时相位p (n )和瞬时频率f (n ):a (n )=I (n )2+Q (n )2,(4)p (n )=arctan Q (n )I (n )éëêêùûúú,(5)f (n )=12πp (n )-p (n -1)Δn㊂(6)Step 3㊀为了消除零中频接收机偏差对瞬时信号影响,对瞬时信号进行中心化处理:a c (n )=a (n )-u a ,(7)f c (n )=f (n )-u f ㊂(8)对于瞬时相位,需在中心化处理之前对瞬时相位中的非线性分量进行逐个滤除,以保证特征提取质量:p nl =p (n )-2πu f (n )Δt ,(9)p c (n )=p nl (n )-u p nl ㊂(10)式中:u a ㊁u f 表示瞬时幅度与瞬时频率的均值,Δt 表示采样时间间隔,u p nl 为消除非线性分量后瞬时相位平均值,p nl 表示非线性相位响应,a c (n )㊁f c (n )㊁p c (n )分别为中心化处理后的瞬时幅度㊁瞬时频率㊁瞬时相位值㊂Step 4㊀将以上所求三个瞬时特征a c (n )㊁f c (n )㊁p c (n )进行分区,并对其求特征值㊂这里特征值有两种方式,第一种为求三个时域瞬时信号的方差㊁偏度和峰度,第二种为求三个时域瞬时信号的标准差㊁方差㊁偏度和峰度㊂标准差:σ=1N x ðN xn =1(x c (n )-u )2㊂(11)方差:σ2=1N x ðN xn =1(x c (n )-u )2㊂(12)偏度:r =1N x σ3ðN xn =1(x c (n )-u )3㊂(13)峰度:k =1N x σ4ðN xn =1(x c (n )-u )4㊂(14)式中:N x 表示中心化数据x c (n )的长度,u 表示x c (n )的均值㊂Step 5㊀求其特征向量,因其具有三种特征,方法一为标准RF -DNA 法,只求方差㊁偏度㊁峰度,则特征具有3ˑ3维,而方法二添加标准差这一特征,则特征具有3ˑ4维㊂每一架无人机的每一个跳频信号瞬时幅度㊁瞬时频率㊁瞬时相位特征所求标准差㊁方差㊁偏度㊁峰度的集如下:F a =[σσ2r k ]a ,(15)F p =σσ2r k []p ,(16)F f =σσ2r k []f ,(17)㊃937㊃第61卷蒋平,谢跃雷:一种民用小型无人机的射频指纹识别方法第6期F i=[F a F p F f]㊂(18)式中:F i为每一架无人机每一跳信号的特征集合㊂一架无人机所有跳频信号瞬态特征集如下:F R=F1,F2,F3, ,F N[]㊂(19)式中:N为每一架无人机跳频信号总共个数㊂所有无人机的无人机跳频信号瞬态特征集合表达式如下:F C=F R1,F R2,F R3, ,F Rj[]㊂(20)式中:j为无人机个数㊂取一组各个无人机瞬时幅度的标准差㊁方差㊁峰度㊁偏度特征进行特征统计,统计值如表1所示㊂表1㊀遥控信号瞬时幅度统计特征表特征标准差方差偏度峰度大疆精灵4pro1号8.611274.15310.0282 1.8725大疆精灵4pro2号7.633258.2650-0.6562 2.2574司马航模x8hw404.37152 1.6379ˑ105-0.2373 2.1364HM 6.021632.2594-7.231976.5866大疆悟252.0166 2.7057ˑ103 1.3153 3.8405司马航模x25pro233.0327 5.4304ˑ104-0.3815 1.6164 2.2㊀识别算法本文主要目的是从信号角度对无人机进行识别,其中识别的具体步骤如下:Step1㊀采集无人机实测数据㊂Step2㊀采用分形贝叶斯变点检测算法对无人机遥控信号瞬态部分进行提取㊂Step3㊀采用RF-DNA统计特征法进行特征提取,提取采用两种方式,第一种含有标准差,第二种不含有标准差㊂Step4㊀对Step3所提取的特征集采用主成分分析(Principal Component Analysis,PCA)算法进行特征降维,特征降维可将维数降维为二维㊁三维㊁四维等,不同维数对识别率有一定影响㊂Step5㊀通过SVM[13]分类器对降维后的数据进行分类识别㊂这里分类器采用Libsvm进行分类,该分类器具有多分类特点,采用的是一对一法完成多分类操作㊂3㊀实验分析本次实验主要采用自制硬件设备对大疆精灵4pro1号及2号㊁司马航模x8hw㊁HM㊁大疆悟2㊁司马航模x25pro无人机信号进行采集,完成相应信号预处理及分类识别,采集系统如图2所示㊂图2㊀无人机遥控信号采集系统实物图通过对5架无人机共225组信号数据段进行实验,其中每个无人机训练数据30组,测试数据15组㊂实验中,因含有三个瞬时特征且每一瞬时特征含有多种特征信息,且含有标准差的特征维数为12维,不含有标准差的为9维㊂采用PCA算法将特征集降维到二维㊁三维,其中二维散点图坐标轴F1㊁F2分别代表二维中维数特征,三维散点图中坐标轴F1㊁F2㊁F3分别代表三维中维数的特征㊂实验1:采用不含有标准差㊁特征降维维数为二维的方式进行分类识别,实测数据二维散点图如图3所示㊂图3㊀无人机遥控信号不含标准差二维特征散点图㊃047㊃电讯技术㊀㊀㊀㊀2021年该图共10类散点数据,主要是5类无人机训练数据和5类无人机测试数据,不同颜色及形状表示不同无人机㊂无人机训练数据用于建立数据单元库,无人机测试数据用于测试无人机识别率㊂从图中可知,不同无人机训练数据散点图分布区域不同,测试数据同样,但部分测试数据存在于其他组训练数据中,故该部分数据为错误识别组㊂无人机遥控信号不含标准差且二维特征识别率表如表2所示,其中无人机总识别率为80%㊂表2㊀无人机遥控信号不含标准差二维特征识别率无人机型号无人机识别率/%无人机总识别率/%大疆精灵4pro 80.000司马航模x8hw 73.333HM100.000大疆悟2无人机73.333司马航模x25pro73.33380实验2:采用不含标准差且特征维数为三维的方式进行分类识别,其散点图㊁识别率如图4及表3所示㊂图4㊀无人机遥控信号不含标准差三维特征散点图表3㊀无人机遥控信号不含标准差三维特征识别率无人机型号无人机识别率/%无人机总识别率/%大疆精灵4pro 100.000司马航模x8hw 86.666HM93.333大疆悟2无人机100.000司马航模x25pro93.33394.666通过图4及表3可知,相对于二维而言,在同样不含有标准差时,三维识别效果更佳,识别率达到94.666%㊂实验3:采用标准差且特征降维维数为二维的方式进行分类识别,其散点图及识别率如图5及表4所示㊂从图和表可知,相对于不含有标准差的二维散点图,含有标准差性能更好,且识别率达到97.333%㊂图5㊀无人机遥控信号含标准差二维特征散点图表4㊀无人机遥控信号含标准差二维特征识别率无人机型号无人机识别率/%无人机总识别率/%大疆精灵4pro 93.333司马航模x8hw100.000HM 100.000大疆悟2无人机100.000司马航模x25pro93.33397.333实验4:采用标准差,特征降维维数为三维方式进行分类识别,散点图及识别率如图6和表5所示,其中含有标准差且三维特征时,其识别率与二维特征相同㊂图6㊀无人机遥控信号含标准差三维特征散点图表5㊀无人机遥控信号含标准差三维特征识别率无人机型号无人机识别率/%无人机总识别率/%大疆精灵4pro 93.333司马航模x8hw 100.000HM100.000大疆悟2无人机100.000司马航模x25pro93.33397.333为更进一步测试识别性能,在实测数据中叠加㊃147㊃第61卷蒋平,谢跃雷:一种民用小型无人机的射频指纹识别方法第6期高斯白噪声,具体方法及步骤如下:Step 1㊀对实测训练数据建立数据单元库及训练数据特征集㊂Step 2㊀对实测测试数据叠加高斯白噪声㊂Step 3㊀通过分形贝叶斯变点检测㊁RF -DNA 统计特征提取已加高斯白噪声后数据特征集㊂Step 4㊀Step 3中已加高斯白噪声后数据特征集与Step 1中训练数据特征集进行均值中心化,产生新特征集,取新特征集中已加高斯白噪声部分特征数据组作为测试特征集㊂Step 5㊀对Step 4所得测试特征集进行PCA 降维,其中为验证维数影响,选择一维㊁二维㊁三维㊁四维作为测试变量㊂Step 6㊀采用SVM 多分类器进行分类,绘出两种识别率曲线图,一种为含有标准差二维特征㊁含有标准差三维特征㊁不含有标准差二维特征㊁不含有标准差三维特征在不同信噪比下识别率对比图,命名为无人机遥控信号不同标准差及不同维数特征识别率图;另一种为含有标准差下一维㊁二维㊁三维㊁四维特征在不同信噪比的识别率对比图,命名为无人机遥控信号含有标准差下不同维数特征识别率图㊂图7为无人机遥控信号不同标准差及不同维数特征识别率图㊂从图中可知,含有标准差识别率优于不含标准差识别率,且三维总体高于二维㊂含有标准差时,信噪比大于15dB 时,其二维及三维识别率大于70%,而不含有标准差时,信噪比大于20dB时,其识别率大于70%㊂总体而言,随着高斯白噪声的增加,识别率逐渐下降,但因对其中心化处理㊁散点图较为集中等原因,其识别率在低于40%以下呈现低识别率随机起伏等混乱状态㊂图7㊀无人机遥控信号不同标准差及不同维数特征识别率图图8是在不同信噪比且含有标准差这一特征下不同维数识别率,总体来说,四维优于三维,三维优于二维及一维㊂在信噪比小于-5dB 时,各维识别率皆低于60%;在信噪比大于20dB 时,各维数识别率且大于90%,且四维最高㊂从识别曲线总体来看,维数越高其识别率更高㊂图8㊀无人机遥控信号含有标准差下不同维数特征识别率图通过以上四个实验得出采用RF -DNA 法对实测无人机遥控信号可以完成其型号的区分,其中三维识别率最高,为97.33%㊂为了更好地验证射频指纹方法的优点,取同一型号的两架大疆精灵4pro 无人机进行个体区分实验,并得出散点图及不同维数识别曲线图㊂由图9(a)可知,同一型号无人机散点图较为紧密,区分难度较大,对分类器有一定要求㊂由图9(b)可知,一维与二维曲线相同,但整体维数对识别率无较大影响,主要受分析数量所限从而无法凸显维数优势㊂总体来说随着信噪比增加,识别率逐渐升高,当信噪比在17dB 以上时各维数识别率达到80%,因此可证明射频指纹识别法可对无人机个体进行区分㊂(a)同一型号无人机遥控信号二维散点图(b)同一型号无人机遥控信号含有标准差下不同维数特征识别率图图9㊀同一型号无人机遥控信号散点图及识别率图㊃247㊃ 电讯技术㊀㊀㊀㊀2021年4　结束语本文针对无人机 黑飞 问题,采用RF-DNA方法完成了无人机具体型号及其个体的识别,可为无人机有效监管提供帮助㊂采用是否含有标准差以及不同维数作为测试条件,验证了在含有标准差且维数为四维时对无人机的型号区分效果最好㊂而通过对两架大疆精灵4pro无人机进行同一型号个体区分实验,得出RF-DNA能够区分同一型号无人机,但是无人机型号的区分抗噪性能高于同一型号的个体区分㊂此外,由于本实验目前只做了两架无人机的同一型号区分,后面应考虑增加更多同一型号无人机,以便于验证一定数量无人机同时存在对个体区别所带来的影响㊂并且,下一步应寻求更好的特征及分类方式从而更有效地对同一型号无人机进行个体区分,增加其实用价值㊂参考文献:[1]㊀赵时轮.无人机危害及恐怖行为反制对策研究[J].中国军转民,2019(6):15-20.[2]㊀李晓文.小型无人机在战术空中控制中的应用分析[J].飞航导弹,2020(5):49-53.[3]㊀张嘉,李润文,崔铠韬.浅析无人机管控手段及无人机无线电反制设备对民航空管运行的影响[J].中国无线电,2019(8):16-18.[4]㊀罗淮鸿,卢盈齐.国外反 低慢小 无人机能力现状与发展趋势[J].飞航导弹,2019(6):32-36. [5]㊀何小勇,韩兵,张笑语,等.一种基于无线电信号特征识别的无人机监测算法设计[J].中国无线电,2019(11):72-74.[6]㊀徐淑正,孙忆南,皇甫丽英,等.基于软件无线电平台的无人机入侵检测[J].实验室研究与探索,2018,37(12):64-67.[7]㊀刘丽.民用无人机跳频信号分析与识别技术研究[D].北京:北京邮电大学,2019.[8]㊀季澈,彭林宁,胡爱群,等.基于射频信号特征的Air-max设备指纹提取方法[J].数据采集与处理,2020,35(2):331-343.[9]㊀曾勇虎,陈翔,林云,等.射频指纹识别的研究现状及趋势[J].电波科学学报,2020,35(3):305-315. [10]㊀张宝林,吕军,李彤,等.一种改进的跳频信号参数估计方法[J].电讯技术,2018,58(11):1310-1316. [11]㊀耿健,杨威.一种同步组网跳频信号的盲分离方法[J].电讯技术,2018,58(12):1464-1469. [12]㊀魏兰兰.基于设备指纹的无线设备识别研究[D].北京:北京交通大学,2019.[13]㊀梁楠,邹志红.结合新型模糊支持向量机和证据理论的多传感器水质数据融合[J].电讯技术,2020,60(3):331-337.作者简介:蒋㊀平㊀男,1994年生于四川成都,2017年于成都工业学院获工学学士学位,现为桂林电子科技大学硕士研究生,主要研究方向为无人机信号侦测与识别㊁FPGA的数字系统设计㊂谢跃雷㊀男,1975年生于河北邯郸,1997年和2003年于桂林电子科技大学分别获工学学士学位和硕士学位,现为副教授㊁硕士生导师,主要研究方向为通信信号处理㊁阵列信号处理及信号处理的VLSI设计实现㊂㊃347㊃第61卷蒋平,谢跃雷:一种民用小型无人机的射频指纹识别方法第6期。

基于边缘检测的抗遮挡相关滤波跟踪算法唐艺北方工业大学 北京 100144摘要：无人机跟踪目标因其便利性得到越来越多的关注。

基于相关滤波算法利用边缘检测优化样本质量，并在边缘检测打分环节加入平滑约束项，增加了候选框包含目标的准确度，达到降低计算复杂度、提高跟踪鲁棒性的效果。

利用自适应多特征融合增强特征表达能力，提高目标跟踪精准度。

引入遮挡判断机制和自适应更新学习率，减少遮挡对滤波模板的影响，提高目标跟踪成功率。

通过在OTB-2015和UAV123数据集上的实验进行定性定量的评估，论证了所研究算法相较于其他跟踪算法具有一定的优越性。

关键词：无人机 目标追踪 相关滤波 多特征融合 边缘检测中图分类号：TN713;TP391.41;TG441.7文献标识码：A 文章编号：1672-3791(2024)05-0057-04 The Anti-Occlusion Correlation Filtering Tracking AlgorithmBased on Edge DetectionTANG YiNorth China University of Technology, Beijing, 100144 ChinaAbstract: For its convenience, tracking targets with unmanned aerial vehicles is getting more and more attention. Based on the correlation filtering algorithm, the quality of samples is optimized by edge detection, and smoothing constraints are added to the edge detection scoring link, which increases the accuracy of targets included in candi⁃date boxes, and achieves the effects of reducing computational complexity and improving tracking robustness. Adap⁃tive multi-feature fusion is used to enhance the feature expression capability, which improves the accuracy of target tracking. The occlusion detection mechanism and the adaptive updating learning rate are introduced to reduce the impact of occlusion on filtering templates, which improves the success rate of target tracking. Qualitative evaluation and quantitative evaluation are conducted through experiments on OTB-2015 and UAV123 datasets, which dem⁃onstrates the superiority of the studied algorithm over other tracking algorithms.Key Words: Unmanned aerial vehicle; Target tracking; Correlation filtering; Multi-feature fusion; Edge detection近年来，无人机成为热点话题，具有不同用途的无人机频繁出现在大众视野。

细说移动通信中的新技术（A new technology in mobilecommunication）As people's living space, activity space and participation areas continue to expand, the functional requirements of mobile phones, not only dialogue and communication, there are many other functions. Moreover, the existing communication system there exist many unsatisfactory places, such as system capacity, voice distortion, dropped on line, power radiation and slow data transmission, the existing communication technology alone is not enough to meet the new demands of communication people. So in this case, we must have new communication technology to ensure, so that a variety of emerging communication technology came into being, below is some of the 3G communications may be used in the new technology.1. Channel coding and decoding technologyThis technique may be used in the DS-CDMA communication standard, in which channel coding and decoding is mainly to reduce the signal transmission power and solve the inevitable fading problem of the signal in the wireless communication environment. The use of codec technology combined with interleaving can improve the BER performance, compared with no encoding, convolutional codes can improve the bit error rate is two orders of magnitude reached 10-3~10-4, and the DS-CDMA communication system using Turbo code error rate can be increased to 10-6. DS-CDMA candidate channel coding techniques include Reed-Solomon and Turbo codes, and Turbo codes can be used as 3G data encoding and decoding technology because the encoding and decoding performance can approach the Shannonlimit. Convolutional codes are mainly used for low data rate speech and signaling.2. Smart antenna technologyIn the development of mobile communication technology, smart antenna has become one of the most active fields. In recent years, almost all advanced mobile communication systems will use this technology. The advantage of smart antenna technology to mobile communication system is difficult to replace by any technology at present. Smart antenna technology has become one of the most attractive technologies in mobile communications. Smart antenna technology uses adaptive beamforming technology to improve the user's direction of arrival gain, while using the zero of the pattern to reduce the interference of high-power users on the space. Its main difficulties lie in the inconsistency of multi-channel and correction technology, the complexity of RAKE receiver combining baseband processing, and the inconsistency of the uplink and downlink direction of arrival caused by FDD technology.3, multi user detection technologyIn the third generation mobile communication system, WCDMA system is a typical example of application of multi user detection technology. As one of the key technologies in WCDMA system, multi user detection technology can make the system achieve good performance in high speed channel environment. Multiuser detection technology improves system performance and increases system capacity by removing cell interference. Multiuser detection technology can effectively mitigate thefar / near effects in DSSS WCDMA systems. The difficulty is the high complexity of baseband processing.4 、 soft handover technologyThe largest mobile phone users opinions on the network is often lost, now people not only during the call by its bitter, but some people worry that if the future network fax support, will not be dropped due to the problems of wireless fax into the water". This is because the mobile phone is switched more when the "hard switching", from a base station coverage area into another base station coverage area to break the original base station and the base station to contact, and then look for new entrants into the coverage area, which is commonly referred to as "first off", of course the off time difference of only a few hundred milliseconds, under normal circumstances, people can not feel, but once the mobile phone for entering the shield area or channel busy and unable to contact with a new base station, it will fall; CDMA technology is used in "soft switching", in the handover, mobile phone and continue to fall and the original base station the contact and contact with the new base station when the mobile phone has been confirmed and the new base station, the original base station and the link is broken, "and then off, dropping may be almost nothing.5, PHS TechnologyThe English name of PHS is Personal Handyphone System,Chinese meaning is personal mobile phone system, the network system is developed by the Japan Telegraph Company, it usesdigital transmission mode, combined with advanced radio access technology and intelligent digital network capabilities. PHS uses low power to transmit radio wave signals, so it covers a smaller area and is more suitable for urban areas, and relatively low rates. PHS provides complete communications services, the integrity of the data transmission ability to support wireless multimedia communication, secondly, PHS also offers a variety of Internet interface, such as: radio access, telephone lines, fiber optic cable, and because of its base design is very light, can support such as KTV. Street。

DCWRadio Wave Guard电波卫士1 研究背景我国幅员辽阔，地理环境复杂，在部分偏远地区地面蜂窝网络不能完全覆盖。

卫星移动通信系统作为陆地蜂窝通信系统的扩展和延伸，不受地域和天气的制约，是应急通信的首选方式。

卫星移动电话利用地球静止轨道卫星或中、低轨道卫星作为中继站，实现区域乃至全球范围内的移动通信。

卫星移动电话终端具备便携性、易用性、隐蔽性、不受公众移动通信管理手段控制等特点。

自1976年Marist 系统商用以来，铱星、全球星、欧星、亚星、天通等窄带卫星移动电话持续发展[1]。

2022年下半年，中国移动联合中兴通讯等厂商发布全球首个运营商5G 非地面网络（NTN ）技术外场验证成果。

普通手机与远在3.6万千米之外的静止轨道卫星通信，像发微信一样，实现短消息和语音对话。

此成果基于3GPP 公开协议，手机终端依次通过卫星、信关站、NTN 基站，接入地面核心网和业务平台，最终实现空天地一体贯通[2]。

随着卫星移动电话在各领域的应用日益增多，对卫星移动通信系统的监测需求也日益凸显。

本文基于ITU 报告中的参数，对典型卫星移动通信系统的监测效果进行仿真分析评估。

对卫星移动通信系统上行链路和下行链路的监测，分别对应卫星通信终端和卫星空间电台的监测。

一般卫星通信系统的通信链路包含用户链路和馈电链路，本文重点研究对用户链路的监测，包括用户上行链路和用户下行链路。

2 下行链路监测效果分析卫星频谱资源为稀缺的高价值资源，对卫星频谱资源的日常使用情况开展监测和分析，有利于掌握频谱资源使用情况，进而优化频谱资源配置。

2.1 空间电台的参数根据ITU-R 报告M.2398，选取卫星通信系统空间电台的典型参数，如表1所示[3]。

表1 空间电台的系统参数卫星指标取值 工作频段（GHz ） 2.17 卫星高度（km ）36 000 卫星天线增益（dB ）50 卫星发射功率（W/5 MHz ）200 波束带宽（MHz ）5 子载波带宽（kHz ）180 天线方向图ITU-R S.672卫星移动通信系统的监测效果仿真分析丁鲜花，赵延安，刘艳洁，纽莉荣（国家无线电监测中心陕西监测站，陕西 西安 710200）摘要：文章以ITU-R报告M.2398中的静止轨道卫星移动通信系统的参数为例，研究卫星移动通信系统的监测效果。

Wireless Sensor Networks andApplicationsWireless Sensor Networks (WSNs) have gained significant attention in recent years due to their potential to revolutionize various industries and applications. These networks consist of small, low-cost sensor nodes that are wirelessly connected to collect and transmit data from the environment. The applications of WSNs are diverse, ranging from environmental monitoring, healthcare, smart homes, industrial automation, agriculture, and more. However, despite their promising potential, WSNs also face several challenges and limitations that need to be addressed for their widespread adoption and success. One of the primarychallenges of WSNs is their limited power supply. Most sensor nodes are powered by batteries, which have a finite lifespan and need to be replaced or recharged periodically. This limitation poses a significant constraint on the deployment and maintenance of WSNs, especially in remote or inaccessible areas. Researchers and engineers are actively working on developing energy-efficient protocols, algorithms, and hardware designs to prolong the battery life of sensor nodes and enable self-sustainability through energy harvesting techniques such as solar, kinetic, or thermal energy. Another critical issue facing WSNs is their vulnerability to security threats and attacks. Since WSNs are often deployed in unattended or hostile environments, they are susceptible to various security risks, including eavesdropping, data tampering, node impersonation, and denial-of-service attacks. Ensuring the confidentiality, integrity, and availability of data in WSNs is a complex and ongoing research area, requiring the development of robust encryption, authentication, key management, and intrusion detection mechanisms to protect against malicious activities and safeguard sensitive information. Furthermore, the scalability and reliability of WSNs are significant concerns, particularly as the number of deployed sensor nodes increases. As WSNs grow insize and complexity, it becomes challenging to maintain efficient communication, data aggregation, and network management. The dynamic nature of wireless communication, environmental interference, and node failures can lead to packet loss, latency, and network congestion, affecting the overall performance andreliability of WSNs. Addressing these scalability and reliability issues requires the design of adaptive routing protocols, fault-tolerant mechanisms, and quality-of-service optimizations to ensure seamless and dependable operation in diverse WSN applications. In addition to technical challenges, the real-world deployment and commercialization of WSNs also face economic, regulatory, and societal barriers. The high initial deployment costs, interoperability with existing infrastructure, compliance with industry standards, and privacy concerns are all factors that impact the widespread adoption and acceptance of WSNs in various domains. Moreover, the ethical implications of collecting and analyzing large volumes of data from WSNs, such as personal health information or environmental surveillance, raise important questions about consent, transparency, and accountability in the use of sensor-generated data. Despite these challenges, the potential benefits of WSNs in enabling smart, connected, and sustainable systems are driving continued research, innovation, and investment in this field. The development of advanced sensor technologies, wireless communication protocols, data analytics, and edge computing capabilities is unlocking new opportunities for WSNs to enhance efficiency, productivity, and quality of life in diverse applications. By addressing the technical, operational, and ethical challenges, WSNs can realize their full potential as a foundational infrastructure for the Internet of Things (IoT) and contribute to a more interconnected and intelligent world.。

海洋相关多模态传感技术
海洋相关的多模态传感技术是指利用多个传感器融合不同的感知模态，以获取更全面、准确的海洋信息的技术。

常用的海洋相关多模态传感技术包括以下几种：
1. 声学传感技术：利用声纳等装置探测海洋中的声音信号，用于测量海洋的物理参数、生物信息以及水下目标的探测等。

2. 光学传感技术：利用光学仪器、摄像头等获取海洋中的图像、视频信息，用于观察海洋生态环境、水下目标检测和成像等。

3. 电磁传感技术：利用电磁波传感器获取海洋中的电磁信号，用于测量海洋的电磁特性、水下目标探测和通信等。

4. 化学传感技术：利用化学传感器检测海洋中的化学成分和污染物，用于海洋水质监测、环境保护等。

5. 生物传感技术：利用生物传感器或生物学检测方法监测海洋中的生物信息，例如测量水中的藻类浓度、海洋生物的迁徙和分布等。

通过综合应用不同的传感器和技术，海洋相关的多模态传感技术可以实现对海洋环境的多维度、全方位的监测和观测，为海洋资源开发、环境保护、海洋科学研究等提供了重要的技术支持。

基于自相似的异常流量自适应检测方法
夏正敏;陆松年;李建华;马进
【期刊名称】《计算机工程》
【年(卷),期】2010(036)005
【摘要】根据异常流量对网络自相似的影响,通过研究在流量正常和异常情况下表征自相似程度的Hurst参数分布特点的不同,设计一种异常流量动态自适应检测方法.该方法采用小波分析估计Hurst参数,根据网络自相似程度自适应地调整检测阈值.对MIT林肯实验室的入侵检测数据测试结果表明,该检测方法具有较好的动态自适应性、较高的检测率及较快的检测速度.
【总页数】3页(P23-25)
【作者】夏正敏;陆松年;李建华;马进
【作者单位】上海交通大学电子工程系,上海,200240;上海交通大学电子工程系,上海,200240;上海交通大学信息安全工程学院上海市信息安全综合管理技术研究重点实验室,上海,200240;上海交通大学电子工程系,上海,200240;上海交通大学信息安全工程学院上海市信息安全综合管理技术研究重点实验室,上海,200240;上海交通大学信息安全工程学院上海市信息安全综合管理技术研究重点实验室,上海,200240【正文语种】中文
【中图分类】TP393
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