雷達(dá)傳感網(wǎng)部署與融合關(guān)鍵技術(shù)
發(fā)布時(shí)間：2019-06-21 03:33
【摘要】：分布式雷達(dá)傳感器(Radar Sensor,RS)是一個(gè)獨(dú)立小型系統(tǒng),它發(fā)送已知波形,接收、分析目標(biāo)和障礙物的回波以確定與目標(biāo)相關(guān)的信息。雷達(dá)傳感網(wǎng)(Radar Sensor Network,RSN)就是由分布在廣闊的地理區(qū)域內(nèi)、具有協(xié)同工作模式的RS節(jié)點(diǎn)有機(jī)地組成的。它們可以用于監(jiān)測大面積區(qū)域,并從多個(gè)角度觀測目標(biāo)。RSN在反射物體(例如飛機(jī)、船舶、車輛以及人等)的檢測、定位、跟蹤等領(lǐng)域越來越重要。然而,RSN的目標(biāo)檢測性能受其關(guān)鍵技術(shù)(如部署方式、分簇算法、融合方式等)的影響很大。本文以提高監(jiān)測區(qū)域內(nèi)目標(biāo)檢測概率為目的,針對(duì)雷達(dá)傳感器網(wǎng)絡(luò)中的幾個(gè)關(guān)鍵技術(shù)進(jìn)行了研究。主要研究內(nèi)容包括:1.搭建了適用于雷達(dá)傳感器網(wǎng)絡(luò)的數(shù)據(jù)傳輸模型和目標(biāo)檢測系統(tǒng),確立了本文RSN關(guān)鍵技術(shù)研究的基礎(chǔ)。2.本文提出了兩種適用于RSN的節(jié)點(diǎn)部署方法:Hexagonal Deployment Strategy(HDS)和Diamond Deployment Strategy(DDS)。該兩種方法是在使RS節(jié)點(diǎn)均勻分布于監(jiān)測區(qū)域的思想下被提出的。在保證一定目標(biāo)虛警概率的前提下,通過這兩種部署方式提高目標(biāo)檢測概率。仿真實(shí)驗(yàn)證明,無論從目標(biāo)檢測概率還是消耗的平均能量的表現(xiàn)來看,DDS部署策略都優(yōu)于HDS部署策略,而這兩種部署方式都優(yōu)于Random Deployment Strategy(DDS)部署策略。所以對(duì)于RSN網(wǎng)絡(luò),DDS和HDS部署策略都是有效的部署策略。3.本文研究了無線傳感器網(wǎng)絡(luò)中的兩種經(jīng)典分簇算法:LEACH算法和HEED算法,并將其應(yīng)用于RSN中,研究它們?cè)赗SN系統(tǒng)中的適用性。本文還將這兩種分簇算法應(yīng)用到經(jīng)由DDS部署方式確定了RS位置的RSN中,從而提高系統(tǒng)性能。蒙特卡羅仿真表明,同等條件下,按HEED算法分簇的RSN網(wǎng)絡(luò)比按LEACH算法分簇的網(wǎng)絡(luò)具有更長的網(wǎng)絡(luò)生存時(shí)間。在采用HEED算法分簇時(shí),由DDS部署的RSN網(wǎng)絡(luò)的目標(biāo)檢測概率大于由RDS部署的RSN網(wǎng)絡(luò)的檢測概率。4.本文在Path-Loss信道衰落模型下,提出了兩種適用于RSN的檢測級(jí)數(shù)據(jù)融合方法:Decision Fusion Rules with Binary Transmission(BT)和Decision Fusion Rules without Binary Transmission(NBT)。針對(duì)多跳的RSN,將這兩種融合方法與兩種部署方式相結(jié)合,進(jìn)一步提高RSN目標(biāo)檢測性能。仿真結(jié)果表明,相比于NBT融合算法,BT融合算法下RSN的檢測概率高,消耗的能量少。
[Abstract]:Distributed Radar Sensor (Radar Sensor,RS) is an independent small system, which transmits known waveforms, receives and analyzes the echoes of targets and obstacles to determine the information related to the targets. Radar sensor network (Radar Sensor Network,RSN) is composed of RS nodes which are distributed in a wide geographical area and have cooperative working mode. They can be used to monitor large areas and observe targets from many angles. RSN is becoming more and more important in the fields of detection, positioning and tracking of reflected objects (such as aircraft, ships, vehicles and people, etc.). However, the target detection performance of RSN is greatly affected by its key technologies (such as deployment mode, clustering algorithm, fusion mode, etc.). In order to improve the detection probability of targets in the monitoring area, several key technologies in radar sensor networks are studied in this paper. The main research contents are as follows: 1. The data transmission model and target detection system suitable for radar sensor network are built, and the foundation of RSN key technology research in this paper is established. 2. In this paper, two node deployment methods: Hexagonal Deployment Strategy (HDS) and Diamond Deployment Strategy (DDS). For RSN are proposed. The two methods are proposed under the idea that RS nodes are evenly distributed in the monitoring area. On the premise of ensuring the false alarm probability of a certain target, the target detection probability is improved by these two deployment methods. The simulation results show that the DDS deployment strategy is superior to the HDS deployment strategy in terms of the target detection probability and the average energy consumed, and both of them are superior to the Random Deployment Strategy (DDS) deployment strategy. So for RSN networks, DDS and HDS deployment policies are effective deployment strategies. In this paper, two classical clustering algorithms in wireless sensor networks, LEACH algorithm and HEED algorithm, are studied and applied to RSN, and their applicability in RSN system is studied. In this paper, the two clustering algorithms are also applied to the RSN where the location of RS is determined by DDS deployment, so as to improve the performance of the system. Monte Carlo simulation shows that under the same conditions, the RSN network clustering according to HEED algorithm has longer network survival time than the network grouped according to LEACH algorithm. When HEED algorithm is used for clustering, the target detection probability of RSN network deployed by DDS is higher than that of RSN network deployed by RDS. 4. In this paper, two detection level data fusion methods: Decision Fusion Rules with Binary Transmission (BT) and Decision Fusion Rules without Binary Transmission (NBT). For Path-Loss channel fading model are proposed, which are suitable for RSN. For multi-hop RSN, these two fusion methods are combined with the two deployment methods to further improve the performance of RSN target detection. The simulation results show that compared with NBT fusion algorithm, BT fusion algorithm has higher detection probability and less energy consumption.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN958;TP212.9
本文編號(hào)：2503754
[Abstract]:Distributed Radar Sensor (Radar Sensor,RS) is an independent small system, which transmits known waveforms, receives and analyzes the echoes of targets and obstacles to determine the information related to the targets. Radar sensor network (Radar Sensor Network,RSN) is composed of RS nodes which are distributed in a wide geographical area and have cooperative working mode. They can be used to monitor large areas and observe targets from many angles. RSN is becoming more and more important in the fields of detection, positioning and tracking of reflected objects (such as aircraft, ships, vehicles and people, etc.). However, the target detection performance of RSN is greatly affected by its key technologies (such as deployment mode, clustering algorithm, fusion mode, etc.). In order to improve the detection probability of targets in the monitoring area, several key technologies in radar sensor networks are studied in this paper. The main research contents are as follows: 1. The data transmission model and target detection system suitable for radar sensor network are built, and the foundation of RSN key technology research in this paper is established. 2. In this paper, two node deployment methods: Hexagonal Deployment Strategy (HDS) and Diamond Deployment Strategy (DDS). For RSN are proposed. The two methods are proposed under the idea that RS nodes are evenly distributed in the monitoring area. On the premise of ensuring the false alarm probability of a certain target, the target detection probability is improved by these two deployment methods. The simulation results show that the DDS deployment strategy is superior to the HDS deployment strategy in terms of the target detection probability and the average energy consumed, and both of them are superior to the Random Deployment Strategy (DDS) deployment strategy. So for RSN networks, DDS and HDS deployment policies are effective deployment strategies. In this paper, two classical clustering algorithms in wireless sensor networks, LEACH algorithm and HEED algorithm, are studied and applied to RSN, and their applicability in RSN system is studied. In this paper, the two clustering algorithms are also applied to the RSN where the location of RS is determined by DDS deployment, so as to improve the performance of the system. Monte Carlo simulation shows that under the same conditions, the RSN network clustering according to HEED algorithm has longer network survival time than the network grouped according to LEACH algorithm. When HEED algorithm is used for clustering, the target detection probability of RSN network deployed by DDS is higher than that of RSN network deployed by RDS. 4. In this paper, two detection level data fusion methods: Decision Fusion Rules with Binary Transmission (BT) and Decision Fusion Rules without Binary Transmission (NBT). For Path-Loss channel fading model are proposed, which are suitable for RSN. For multi-hop RSN, these two fusion methods are combined with the two deployment methods to further improve the performance of RSN target detection. The simulation results show that compared with NBT fusion algorithm, BT fusion algorithm has higher detection probability and less energy consumption.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN958;TP212.9
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