本文選題：跑道入侵 + 地磁傳感技術(shù)　； 參考：《南京航空航天大學(xué)》2016年碩士論文

【摘要】：場面運動目標(biāo)檢測與跟蹤預(yù)測作為跑道入侵系統(tǒng)最為基礎(chǔ)的系統(tǒng)監(jiān)視功能部分,能夠為目標(biāo)識別和入侵控制提供信息支持,增強管制員的交通態(tài)勢感知意識。針對現(xiàn)有場面監(jiān)視設(shè)備的固有缺陷,本文展開基于地磁傳感技術(shù)的場面運動目標(biāo)檢測與跟蹤預(yù)測問題的研究。首先,本文采用單節(jié)點雙AMR傳感器方案對檢測節(jié)點進行設(shè)計。結(jié)合場面運動目標(biāo)背景及特點的先驗信息,選擇合理的布置方式構(gòu)成檢測節(jié)點網(wǎng)絡(luò)以感知機場交通態(tài)勢。其次,本文對自適應(yīng)閾值檢測算法進行改進,結(jié)合場面移動目標(biāo)對地磁干擾信號的實際特征,設(shè)計了一種基于雙AMR傳感器的狀態(tài)互補融合檢測算法;根據(jù)不同的檢測方式對目標(biāo)的運行方向信息進行提取;本文提取了目標(biāo)的平均速度和瞬時速度,提出了基于雙地磁信號特征點的瞬時速度提取方法。再次,本文設(shè)計一種基于I-IMM的場面運動目標(biāo)跟蹤預(yù)測算法。在小樣本速度觀測信息的情況下,算法通過對殘差均值加權(quán)求和重新構(gòu)造模型概率似然函數(shù),后驗信息更新馬爾科夫模型轉(zhuǎn)移概率,加快了模型切換的速度并增加了模型辨識度。在目標(biāo)狀態(tài)不可感知階段,利用可感知階段辨識的運動模型及自適應(yīng)的模型轉(zhuǎn)移概率可實現(xiàn)對目標(biāo)航跡的記憶跟蹤預(yù)測。最后,本文設(shè)計開發(fā)了場面運動目標(biāo)地磁信號采集系統(tǒng)。利用實地采集的目標(biāo)地磁信號驗證了本文提出的狀態(tài)互補融合檢測算法以及基于雙地磁信號特征點提取目標(biāo)瞬時速度方法的有效性。
[Abstract]:As the most basic monitoring function of runway intrusion system, scene motion target detection and tracking can provide information support for target recognition and intrusion control, and enhance traffic situation awareness of controllers. In view of the inherent defects of the existing scene monitoring equipment, this paper studies the detection and tracking of moving targets based on geomagnetic sensing technology. Firstly, the single node and double AMR sensor scheme is used to design the detection node. Based on the prior information of the background and characteristics of the scene motion target, the reasonable layout is chosen to form the detection node network to perceive the airport traffic situation. Secondly, this paper improves the adaptive threshold detection algorithm and designs a state complementary fusion detection algorithm based on dual AMR sensors. In this paper, the average velocity and instantaneous velocity of the target are extracted, and the instantaneous velocity extraction method based on the feature points of the double geomagnetic signal is proposed. Thirdly, this paper designs a scene motion target tracking and prediction algorithm based on I-IMM. In the case of small sample velocity observation, the algorithm reconstructs the probability likelihood function of the model by weighted sum of the residual mean, and updates the Markov model transfer probability by the posteriori information. The speed of model switching is accelerated and the model identification is increased. In the imperceptible phase of the target state, the motion model identified by the perceptible phase and the adaptive model transition probability can be used to predict the track of the target by memory tracking. Finally, a geomagnetic signal acquisition system is designed and developed. The effectiveness of the proposed state complementary fusion detection algorithm and the instantaneous velocity extraction method based on the feature points of the double geomagnetic signals are verified by using the geomagnetic signals collected in the field.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：V355

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本文編號：1911421