本文關(guān)鍵詞：基于隱馬爾可夫模型的無線傳感器網(wǎng)絡(luò)非視距定位研究　出處：《東北大學》2014年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 無線傳感器網(wǎng)絡(luò) 非視距 隱馬爾可夫模型 交互式多模型 粒子群優(yōu)化算法

【摘要】：近年來,無線傳感器網(wǎng)絡(luò)這一新興技術(shù)受到國際學術(shù)界、工業(yè)界的廣泛關(guān)注,在軍事、環(huán)境、工業(yè)等領(lǐng)域顯現(xiàn)出良好的發(fā)展態(tài)勢。其中,獲取移動節(jié)點的位置信息是無線傳感器網(wǎng)絡(luò)的基本功能。無論是在災難救援還是在智能家居領(lǐng)域,如何精確地測算出移動節(jié)點的坐標信息,對整個傳感器網(wǎng)絡(luò)系統(tǒng)都有著舉足輕重的作用。本文針對室內(nèi)陌生環(huán)境,研究非視距的誤差的消除策略和算法,并結(jié)合仿真實驗對其性能進行了分析。針對D/TA(Detection/Tracking Algorithm)算法的不足,本文結(jié)合節(jié)點自身的運動特點,提出了一種結(jié)合了運動慣性的(Improved-Detection/Tracking Algorithm, I-D/TA)定位算法。在完善隱馬爾可夫模型(H idden Markov Model, HMM)算法的基礎(chǔ)上,相繼提出了修正隱馬爾科夫模型(Modified-Hidden Markov Model, M-HMM)和更新修正隱馬爾科夫模型(Renewal Modified-Hidden Markov Model, RM-HMM)兩種改進的定位算法。仿真實驗表明,上述三種算法在距離估計和坐標計算環(huán)節(jié)都有很好的效果,算法定位精度逐步提高,并具有很好的穩(wěn)定性�？紤]到移動節(jié)點的速度運動特點,本文提出了一種HMM與IMM混合定位算法,將其速度模型劃分為高速模型和低速模型,讓節(jié)點在移動時不斷地評估自己處于兩種狀態(tài)的概率,利用交互式多模型(IMM)對HMM定位的結(jié)果進行融合,然后以HMM算法的改進形式與IMM進行融合,以達到更好的精度。仿真實驗表明,HMM與IMM混合定位算法在距離估計和坐標計算環(huán)節(jié)都有很好的效果,所獲得的定位精度高于卡爾曼濾波、粒子濾波等其它算法,而且定位結(jié)果穩(wěn)定,具有良好的魯棒性。在不同的定位環(huán)境下,HMM初值定位的條件通常各不相同。針對這一客觀事實,本文提出了一種基于粒子群(PSO)和模擬退火混合優(yōu)化算法,對HMM的初始條件進行優(yōu)化。并提出了一種降維的優(yōu)化策略在不影響精確度的情況下加快運行效率,針對傳統(tǒng)的邊界處理方式容易產(chǎn)生局部最優(yōu)解的情況提出了兩種邊界改進處理方法。仿真結(jié)果表明,本文提出的初值優(yōu)化測量與其它的策略相比,具有更快的優(yōu)化速度,更穩(wěn)定的優(yōu)化精度。
[Abstract]:In recent years, wireless sensor network (WSN), as a new technology, has received extensive attention from the international academia and industry, and has shown a good trend of development in military, environmental, industrial and other fields. Obtaining location information of mobile nodes is the basic function of wireless sensor networks. Whether in disaster relief or in the field of smart home, how to accurately calculate the coordinate information of mobile nodes. It plays an important role in the whole sensor network system. In this paper, the non-line-of-sight error elimination strategy and algorithm are studied for the unfamiliar indoor environment. Combined with the simulation experiments, the performance of the algorithm is analyzed, aiming at the deficiency of the D/ TA detection / tracking algorithm. In this paper, according to the motion characteristics of nodes themselves, a novel improved improved detection / tracking Algorithm is proposed, which combines the motion inertia. Based on the improvement of hidden Markov model and H idden Markov model (HMMM) algorithm. Modified-Hidden Markov Model has been proposed one after another. M-HMMM and Renewal Modified-Hidden Markov Model. Simulation results show that the above three algorithms have good effect in distance estimation and coordinate calculation, and the accuracy of the algorithm is improved gradually. Considering the characteristics of velocity movement of mobile nodes, this paper proposes a hybrid localization algorithm of HMM and IMM, which can be divided into high speed model and low speed model. Let the nodes continuously evaluate their probability of being in two states while moving, and fuse the results of HMM localization by using interactive multi-model (IMM). Then the improved form of HMM algorithm is fused with IMM to achieve better precision. The hybrid localization algorithm of HMM and IMM has a good effect in distance estimation and coordinate calculation. The positioning accuracy obtained is higher than that of other algorithms such as Kalman filter particle filter and so on and the localization results are stable. It has good robustness. The initial location conditions of hmm are usually different in different location environment. In view of this objective fact. A hybrid optimization algorithm based on particle swarm optimization (PSO) and simulated annealing is proposed in this paper. The initial conditions of HMM are optimized, and a dimensionality reduction optimization strategy is proposed to speed up the operation efficiency without affecting the accuracy. In this paper, two improved boundary processing methods are proposed to solve the problem that the traditional boundary processing methods are easy to produce local optimal solutions. The simulation results show that the proposed initial value optimization measurement is compared with other strategies. With faster optimization speed, more stable optimization accuracy.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN929.5;TP212.9

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