基于HMM的雷达状态转移估计方法
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摘要
针对传统识别模型存在的参数规律描述不全面的问题,提出一种适用于多功能雷达(MFR)的层级模型,该模型通过任务、状态、参数3个层级反映了MFR系统的运行机制,并依据不同的参数变化规律,设定多种函数进行描述,能够反映信号的联合变化和统计信息,较统计和脉冲样本图模型具备更好的识别效果。在层级模型基础上,针对MFR状态转移估计方法存在的鲁棒性、估计准确率不佳的问题,引入目标运动状态信息,构建双链隐马尔可夫模型(HMM),进而利用D-S(Dempster-Shafer)证据理论优化估计结果,提出一种基于HMM的雷达状态转移估计方法,实验结果表明,提出的方法较改进前具备更优异的鲁棒性和估计准确率。
Aimed at the problem of traditional recognition models that parameter rule description is not complete,a hierarchical model suitable for multi-function radar( MFR) is proposed in this paper. This model reflects the operating mechanism of MFR system through three levels of task,state and parameter. Then according to parameter features,a variety of functions are used to describe the change rule of parameters,and signal joint changes and statistical information can be reflected. This model has better recognition effect compared with statistic and pulse sample diagram model. On the basis of the hierarchical model,to solve the problem of poor robustness and low accuracy of MFR state transfer estimation method,double chain hidden Markov model( HMM) was built by introducing target motion state information. D-S( Dempster-Shafer) evidence theory was used to optimize estimated results,and a radar state transfer estimation method based on HMM was proposed. The experimental results show that the proposed algorithm has more excellent robustness and higher estimation accuracy rate than that before improvement.
Aimed at the problem of traditional recognition models that parameter rule description is not complete,a hierarchical model suitable for multi-function radar( MFR) is proposed in this paper. This model reflects the operating mechanism of MFR system through three levels of task,state and parameter. Then according to parameter features,a variety of functions are used to describe the change rule of parameters,and signal joint changes and statistical information can be reflected. This model has better recognition effect compared with statistic and pulse sample diagram model. On the basis of the hierarchical model,to solve the problem of poor robustness and low accuracy of MFR state transfer estimation method,double chain hidden Markov model( HMM) was built by introducing target motion state information. D-S( Dempster-Shafer) evidence theory was used to optimize estimated results,and a radar state transfer estimation method based on HMM was proposed. The experimental results show that the proposed algorithm has more excellent robustness and higher estimation accuracy rate than that before improvement.
引文
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[14]代鹂鹏,王布宏,蔡斌,等.基于SCFG建模的多功能雷达状态估计算法[J].空军工程大学学报(自然科学版),2014,15(3):24-28.DAI L P,WANG B H,CAI B,et al.Latest status&development trends of airborne AESA fire-control radar[J].Journal of Air Force Engineering University(Natural Science Edition),2014,15(3):24-28(in Chinese).
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[16]WANG A,KRISHNAMURTHY V.Signal interpretation of multifunction radars:Modeling and statistical signal processing with stochastic context free grammar[J].IEEE Transactions on Signal Processing,2008,56(3):1106-1119.
[2]VISNEVSKI N.Syntactic modeling of multi-function radars[D].Hamilton:Mc Master University,2005:47-58.
[3]VISNEVSKI N,KRISHNAMURTHY V,WANG A,et al.Syntactic modeling and signal processing of multifunction radars:A stochastic context-free grammar approach[J].Proceedings of the IEEE,2007,95(5):1000-1025.
[4]刘海军.雷达辐射源识别关键技术研究[D].长沙:国防科学技术大学,2010:95-106.LIU H J.Researches on identification key technology for radar emitter[D].Changsha:National University of Defense Technology,2010:95-106(in Chinese).
[5]刘海军,樊昀,李悦,等.多功能雷达建模中的雷达字提取技术研究[J].国防科技大学学报,2010,32(2):91-96.LIU H J,FAN Y,LI Y,et al.Research on extracting of radar words in modeling of multi-function radar[J].Journal of National University of Defense Technology,2010,32(2):91-96(in Chinese).
[6]ARASARATNAM I,HAYKIN S,KIRUBARAJAN T,et al.Tracking the mode of operation of multi-function radars[C]∥IEEE Conference on Radar.Piscataway,NJ:IEEE Press,2006:233-238.
[7]马爽.多功能雷达电子情报信号处理关键技术研究[D].长沙:国防科技大学,2013:101-125.MA S.Research on ELINT signal processing key technologies for multifunction radar research on extracting of radar words in modeling of multi-function radar[D].Changsha:National University of Defense Technology,2013:101-125(in Chinese).
[8]RYAN M S,NUDD G R.The Viterbi algorithm[J].Proceedings of the IEEE,2015,61(5):268-278.
[9]ATTOUCH H,PEYPOUQUET J,REDONT P.A dynamical approach to an inertial forward-backward algorithm for convex minimization[J].SIAM Journal on Optimization,2014,24(1):232-256.
[10]WELCH L R.Hidden Markov models and the Baum-Welch algorithm[J].IEEE Information Theory Society Newsletter,2003,53(2):194-211.
[11]周德强,陈卫东.基于Viterbi算法的扩频码与信息序列联合估计[J].飞行器测控学报,2014,33(5):441-447.ZHOU D Q,CHEN W D.Joint blind estimation of spreading code and information sequence based on Viterbi algorithm[J].Journal of Spacecraft TT&C Technology,2014,33(5):441-447(in Chinese).
[12]LIVANI H,JAFARZADEH S,EVRENOSOGLU C Y,et al.A unified approach for power system predictive operations using Viterbi algorithm[J].IEEE Transactions on Sustainable Energy,2014,5(3):757-766.
[13]刘韬.基于隐马尔可夫模型与信息融合的设备故障诊断与性能退化评估研究[D].上海:上海交通大学,2013:46-52.LIU T.Study of hidden Markov model and information fusion in equipment fault diagnosis and performance degradation assessment[D].Shanghai:Shanghai Jiao Tong University,2013:46-52(in Chinese).
[14]代鹂鹏,王布宏,蔡斌,等.基于SCFG建模的多功能雷达状态估计算法[J].空军工程大学学报(自然科学版),2014,15(3):24-28.DAI L P,WANG B H,CAI B,et al.Latest status&development trends of airborne AESA fire-control radar[J].Journal of Air Force Engineering University(Natural Science Edition),2014,15(3):24-28(in Chinese).
[15]章登义,欧阳黜霏,吴文李.针对时间序列多步预测的聚类隐马尔科夫模型[J].电子学报,2014,42(12):2359-2364.ZHANG D Y,OUYANG C F,WU W L.Cluster-based hidden Markov model in time series multi-step prediction[J].Acta Electronica Sinica,2014,42(12):2359-2364(in Chinese).
[16]WANG A,KRISHNAMURTHY V.Signal interpretation of multifunction radars:Modeling and statistical signal processing with stochastic context free grammar[J].IEEE Transactions on Signal Processing,2008,56(3):1106-1119.