【摘要】：隨著我國城市化的加快，城市中機(jī)動車保有量的持續(xù)高速增長，交通問題日益嚴(yán)峻。國內(nèi)大中城市的停車調(diào)查數(shù)據(jù)表明，城市路邊停車問題尤其突出，特別是繁忙時段，動態(tài)交通擁堵嚴(yán)重。在此背景下，智能交通系統(tǒng)（ITS）逐步形成。 在智能交通系統(tǒng)中，交通信息的采集（如車輛檢測）占有重要地位，是交通流預(yù)測、控制及緊急事件快速反應(yīng)的基礎(chǔ)。由于車輛是鐵磁性物質(zhì)，車輛在空間的存在會對地磁場產(chǎn)生擾動，故而可以通過裝置在地面上的各向異性磁阻（AMR）傳感器探測該擾動，從而達(dá)到車輛檢測的目的。將AMR傳感器節(jié)點與無線網(wǎng)絡(luò)相連，構(gòu)成無線傳感器網(wǎng)絡(luò)（WSN），可以廣泛應(yīng)用于智能交通系統(tǒng)。目前，基于無線磁阻傳感器網(wǎng)絡(luò)的車輛檢測技術(shù)還不成熟，檢測車輛泊車等基礎(chǔ)性問題依然沒有得到很好的解決。 本文針對上述問題開展研究工作，，首先通過對車輛泊車對地磁場擾動信號的提取及考察，分析信號的特征及其識別方法；其次通過提取的信號特征數(shù)據(jù)，利用協(xié)同信息處理策略，融合相鄰節(jié)點數(shù)據(jù)，在現(xiàn)有的車輛檢測算法的基礎(chǔ)上，針對單節(jié)點檢測提出局部極值檢測算法（REA），針對多節(jié)點檢測提出協(xié)同決策檢測算法（CDA）。局部極值檢測算法采用了基于過程的方法，考慮泊車信號變化過程的情況，通過狀態(tài)機(jī)實時提取信號的波動特征，根據(jù)特征數(shù)據(jù)設(shè)計相應(yīng)的判斷規(guī)則。協(xié)同決策檢測算法關(guān)注相鄰?fù)＼囄恍盘柕年P(guān)聯(lián)性，在傳感器節(jié)點檢測到的信號幅度較小時觸發(fā)檢測，根據(jù)REA算法得到的特征數(shù)據(jù)，通過路由器融合不同節(jié)點的信息做出綜合判斷。 上述兩種算法分別應(yīng)用于傳感器節(jié)點和路由器節(jié)點。算法在實際的系統(tǒng)中應(yīng)用超過6個月，節(jié)點數(shù)超過100個，通過實驗驗證和實際系統(tǒng)的反饋數(shù)據(jù)，證明了所提算法的可靠性及較高的檢測精度。
[Abstract]:With the acceleration of urbanization in China and the sustained rapid growth of motor vehicle ownership in cities, traffic problems are becoming increasingly serious. The parking survey data of large and medium-sized cities in China show that the problem of roadside parking is especially serious, especially during the peak period, the dynamic traffic congestion is serious. In this context, the Intelligent Transportation system (ITS) is gradually formed. In intelligent transportation system, traffic information collection (such as vehicle detection) plays an important role, which is the basis of traffic flow prediction, control and rapid response to emergencies. Because the vehicle is a ferromagnetic material, the presence of the vehicle in space will cause disturbance to the geomagnetic field, so the disturbance can be detected by the anisotropic magnetoresistive (AMR) sensor on the ground, thus achieving the purpose of vehicle detection. The AMR sensor node is connected to the wireless network, and the (WSN), can be widely used in the intelligent transportation system. At present, the vehicle detection technology based on wireless magnetoresistive sensor network is not mature, and the basic problems of vehicle parking detection are still not well solved. In this paper, the above problems are studied. Firstly, the characteristics of the signal and its identification method are analyzed through the extraction and investigation of the disturbance signal of vehicle parking to the geomagnetic field; secondly, the characteristic data of the signal are extracted. Based on the existing vehicle detection algorithms, a local extremum detection algorithm (REA),) is proposed based on the existing vehicle detection algorithms, and the cooperative decision detection algorithm (CDA).) for multi-node detection is proposed by using cooperative information processing strategy. The local extremum detection algorithm adopts a process-based method. Considering the changing process of parking signal, the fluctuation feature of the signal is extracted in real time by the state machine, and the corresponding judgment rules are designed according to the characteristic data. The cooperative decision detection algorithm focuses on the correlation of the adjacent parking space signals, and triggers the detection at the sensor node with a small amplitude. According to the characteristic data obtained by the REA algorithm, The router fuses the information of different nodes to make a comprehensive judgment. The two algorithms are applied to sensor node and router node respectively. The algorithm has been applied in the actual system for more than 6 months and the number of nodes is over 100. The reliability and high detection accuracy of the proposed algorithm are proved by the experimental verification and the feedback data of the actual system.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U495;TP212.9;TN929.5

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