本文選題：粒子濾波算法 + 混合濾波算法��； 參考：《長春理工大學(xué)》2017年碩士論文

【摘要】：本文以隱馬爾科夫模型頻譜算法為基礎(chǔ),針對現(xiàn)有的頻譜檢測算法中沒有對計算復(fù)雜度進行更進一步探討的現(xiàn)狀進行分析的問題,對粒子濾波算法進行改進(結(jié)合貝葉斯近似方法),使得在保持粒子濾波算法較高精確度的同時,還減少了相應(yīng)的運行時間,提升了計算效率。該基于粒子濾波的改進算法(混合濾波算法)集中了貝葉斯近似和粒子濾波的優(yōu)勢,在保證了估計精度的前提下,可以在計算復(fù)雜度和時間復(fù)雜度方面達到良好的平衡。文章中的仿真模型一方面,只利用隱馬爾科夫模型,用于模擬子信帶的狀態(tài)估計,動態(tài)估計頻帶的狀態(tài),此時不需要考慮信道的影響。另一方面,在隱馬爾科夫模型的基礎(chǔ)上,輔助自回歸模型構(gòu)成混合模型來仿真單徑衰落信道。仿真結(jié)果表明,與粒子濾波算法相比,該改進算法在與其具有相近的精確度基礎(chǔ)上,可以在頻譜感知過程中比粒子濾波減少運行時間。
[Abstract]:Based on the hidden Markov model spectrum algorithm, this paper analyzes the current situation of the existing spectrum detection algorithm, which has no further research on computational complexity. The particle filter algorithm is improved (combined with Bayesian approximation), which keeps the high accuracy of the particle filter algorithm, and reduces the corresponding running time and improves the computational efficiency. The improved particle filter algorithm (hybrid filtering algorithm) combines the advantages of Bayesian approximation and particle filter, and can achieve a good balance between computational complexity and time complexity under the premise of ensuring the estimation accuracy. On the one hand the simulation model in this paper only uses hidden Markov model to simulate the state estimation of subband and dynamically estimate the state of frequency band without considering the influence of channel. On the other hand, based on the hidden Markov model, the auxiliary autoregressive model is used to simulate the single-path fading channel. The simulation results show that compared with the particle filter algorithm, the improved algorithm can reduce the running time compared with the particle filter in the spectral sensing process on the basis of similar accuracy.
【學(xué)位授予單位】：長春理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN925

【參考文獻】

相關(guān)碩士學(xué)位論文 前1條

1 楊金浩;基于貝葉斯推斷的認知無線電頻譜檢測[D];長春理工大學(xué);2014年

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本文編號：1827074