摘要
水下航行器作为探索海洋的重要手段，无论在军事上还是在民用上都得到了广泛的应用，同时随着海洋研究的不断深入和军事需求的不断复杂化，多水下航行器协作系统已成为水下航行器领域研究的一个新的热点。而高精度的导航定位是水下航行器安全、可靠地执行水下作业的技术保障，因此本文分别从单体水下航行器导航定位和多水下航行器协同导航定位展开深入研究。论文的主要工作有：
1、从贝叶斯估计的观点出发，分析了高斯域贝叶斯估计的基本原理，基于高斯域贝叶斯估计理论框架，推导了经典卡尔曼滤波算法，针对高斯域非线性系统，推导了传统的非线性近似滤波算法，包括扩展卡尔曼滤波算法和无迹卡尔曼滤波算法，然后从数值积分的观点，推导了最新提出的一种高斯域非线性滤波算法，即容积卡尔曼滤波算法。最后通过仿真实验对各种高斯域非线性滤波算法进行性能比较。
2、基于欧拉平台误差角的概念，推导了捷联惯导系统非线性误差模型。针对水下航行器捷联惯导系统大初始失准角情况下的动基座初始对准，提出了采用多普勒计程仪速度辅助捷联惯导系统实现运动中对准。同时针对捷联惯导系统误差模型的非线性，提出了一种基于非线性预测滤波的容积卡尔曼滤波算法，并将其应用于水下航行器动基座初始对准中，最后进行了仿真实验，验证了该方法的有效性。
3、对水下航行器组合导航技术进行研究，建立了SINS/DVL组合导航数学模型，分析了各种组合校正方式的优缺点，提出了一种适合于SINS/DVL组合导航的混合校正滤波方法。研究了Sage-Husa自适应滤波算法和强跟踪卡尔曼滤波算法，针对实际工作环境下，多普勒计程仪存在量测噪声未知或时变的情况，提出了量测噪声时变的强跟踪自适应滤波算法，并将其应用于SINS/DVL组合导航滤波解算中，最后通过试验数据对其进行仿真验证。
4、为了进一步提高水下航行器的导航定位精度，研究了多水下导航传感器信息融合技术。理论推导和分析了联邦滤波算法，建立了水下航行器组合导航数学模型，针对常规联邦滤波器中局部滤波器对量测噪声未知或时变的敏感性，设计了一个将量测噪声时变的强跟踪自适应滤波算法作为局部滤波器估计算法的自适应联邦滤波器，并将其应用于水下航行器多导航传感器信息融合中，最后进行仿真实验与分析。
5、针对多水下航行器协同作业的需求，研究了多水下航行器协同导航定位技术。首先分析了多领航者协同导航定位的基本原理，建立了相应的数学模型，提出了基于扩展卡尔曼滤波的协同导航定位算法，并进行了一系列的仿真实验。为了进一步简化系统结构，研究了单领航者协同导航定位技术，分析了单领航者协同导航定位原理，建立了单领航者协同导航定位数学模型，基于非线性系统可观测性理论，在直角坐标系下对单领航者协同导航定位系统进行可观测性分析，给出了单领航者协同导航系统可观性条件，最后通过仿真实验对单领航者协同导航定位算法的有效性进行验证。
The underwater vehicle is applied widely as improtant device for exploring ocean inmilitary use and in civil use. With the deepening of ocean research and the sophistication ofmilitary requirement, the multiple underwater vehicle cooperation system is a hot issue inunderwater vehicle domain research.The high precision navigation and positioning istechnical support for underwater vehicle working securely and reliably under water. So thisdissertation investigates two aspects including single underwater vehicle navigation andpositioning and multiple underwater vehicle navigation and positioning. The major work is asfollow:
1、From the Bayesian estimation viewpoint, Bayesian estimation theory under gaussiandomain is analysied. Baesd on the gussian Bayesian estimation framwork, the celebratedKalman filter is derived. With the nonlinear system under guassian domain, traditionalnonlinear approximate filtering algorithms including the extended kalman filter andunsecented kalman filter, and a new nonlinear filter, namely cubature kalman filter, arediscussed. The filter performance is compared for all kinds of nonllinear filtering algorithmsthrough simulation experiment.
2、Based on the concept of Enler platform error angle, the nonlinear error model isdeduced. In view of initial alignment of strapdown inertial navigation system with largealignment on dynamic base, the velocity of Doopler Velocity Log is used to help implementinitial alignment in motion. Meanwhile, due to nonlinear of error model of strapdown inertialnavigation system, a new cubature kalman filter based on nonlinear prediction filter, namelyNPF-CKF, is put forward, and is applied for alignment of underwater vehicle on dynamicbase. The simulation is carried out and shows that the method is efficient.
3、The integrated navigation of underwater vehicle is investigated. The mathematicalmodel of the integrated navigation system of SINS/DVL is established. The revising methodsare discussed, and the mixed revising method of output revising and feedback revising isproposed and is applied SINS/DVL integrated navigation. The Sage-Husa adaptive filteralgorithm and strong tracking kalman filter are studied. In view of unkown or time-varyingfor measurement noise of Doppler Velocity Log in actual working environment, The strongtracking adaptive filter algorithm of time-varying measurement noise is proposed, andapplied SINS/DVL integrated navigation. The simulation is carried out through test data.
4、In order to enhance the navaigation and positioning precision of underwater vehicle,mutiple underwater navigation sensor information fusion technology is discussed. The federal filter is deduced and analyzed theoretically. The mathematical model of integrated navigationof underwater vehicle is established. In view of the susceptibility to measurement noiseunkown or time-varying of local filter in traditional federal filter, the adaptive federal filter isproposed in which strong tracking adaptive filter algorithm is applied in local filter. Then thefederal filter proposed is applied in mutiple navigation sensor information fusion.Finally, thesimulation is carried out.
5、In view of requirement of mutiple underwater vehicles collboration, the cooperativenavigation of multiple underwater vehicles is discussed. The fundamental principle ofcooperative navigation of mutiple underwater vehicles based on many leaders is analyzed,andthe mathematical model of cooperative navigation of mutiple underwater vehicles based onmany leaders is established. The cooperative navigation algorithm based on EKF is proposed.In order to simply the system structure, the cooperative navigation of multiple underwatervehicles is discussed. The fundamental principle of cooperative navigation based on singleleader is analyzed,and the mathematical model of cooperative navigation based on singleleader is established. Based on the observability theory of nonlinear system, observabilityanalysis of cooperative navigation based on single leader is done under Cartesian coordinate.The observability condition is obtained. Finally, the simulation is carried out and proves thatthe validity of algorithm of cooperative navigation based on single leader.
引文
[1]朱海,莫军.水下导航信息融合技术.北京:国防工业出版社.2002
[2]赵辉.基于水下航行器导航定位及信息融合技术研究.南京:南京理工大学博士学位论文.2007
[3]张爱军.水下潜器组合导航定位及数据融合技术研究.南京:南京理工大学博士学位论文.2009
[4]李佩娟,徐晓苏,张涛.信息融合技术在水下组合导航中的应用.中国惯性技术学报.2009,17(3):344-349页
[5]王其,徐晓苏.多传感器信息融合技术在水下组合导航中的应用.中国惯性技术学报.2007,15(6):667-671页
[6]王其,徐晓苏.联邦卡尔曼滤波在水下航行器组合导航中的应用.
[7]陈玮,吴泽伟,吴晓峰. UUV协同探测的现状与发展前景.舰船电子工程.2009,29(2):6-8页
[8]徐真珍,封锡盛.多UUV协作系统的研究现状与发展.机器人.2007,29(2):186-192页
[9]冀大雄,李一平,刘健.主从式UUV群的随从UUV导航.仪器仪表学报.2009,30(6):631-634页
[10]张红梅,赵建虎,杨鲲,等.水下导航定位技术.武汉:武汉大学出版社.2010
[11]彭富清,霍立业.海洋地球物理导航.地球物理学进展.2007,22(3):759-764页
[12]李俊,徐德民,宋保维,等.自主式水下潜器导航技术发展现状与展望.中国造船.2004,45(3):70-76页
[13]严卫生,徐德明,李俊,等.自主水下航行器导航技术.火力与指挥控制.2004,29(6):11-15页
[14]以光衢.惯性导航原理.北京:航空工业出版社.1987
[15]全伟,刘百奇,宫晓琳,等.惯性/天文/卫星组合导航技术.北京:国防工业出版社.2011
[16]陈哲.捷联惯导系统原理.北京:宇航出版社.1986
[17]张天光,王秀萍,王丽霞,等.捷联惯性导航技术.北京:国防工业出版社.2007
[18]秦永元.惯性导航.北京:科学出版社.2009
[19]张立川,刘明雍,徐德明,等.基于水声传播延迟的主从式多无人水下航行器协同导航定位研究.兵工学报.2009,32(12):1674-1678页
[20]刘明雍,李闻白,刘富樯,等.基于单信标测距的水下导航系统可观测性分析.西北工业大学学报.2011,29(1):87-93页
[21]李闻白,刘明雍,李虎雄,等.基于单领航者相对位置测量的多AUV协同导航系统定位性能分析.自动化学报.2011,37(6):724-736页
[22]李闻白,刘明雍,张立川,等.单领航者相对位移测量的多自主水下航行器协同导航.兵工学报.2011,32(8):1002-1006页
[23]李闻白,刘明雍,雷小康,等.未知洋流干扰下基于单领航者的多自主水下航行器协同导航.兵工学报.2011,32(3):292-297页
[24]张立川,徐德明,刘明雍.基于双水听器的多自主水下航行器协同导航方法.系统工程与电子技术.2011,33(7):1603-1606页
[25]张立川,徐德明,刘明雍,等.基于移动长基线的多AUV协同导航.机器人.2009,31(6):581-585页
[26]刘毅,刘志检.捷联惯性导航系统传递对准技术研究现状及发展趋势.航天控制.2004,22(5):50-55页
[27] Kain J, Cloutier J. Rapid transfer alignment for tactical weapon applications. AIAAguidance navigation and control conference. Boston, MA.1989:1290-1300P.
[28]李俊,赵豪,赵涛.水下航行器速度和姿态匹配的动基座快速对准滤波方法.鱼雷技术.2008,16(2):13-16页
[29]刘明雍,赵豪.基于Potter算法的水下航行器动基座初始对准滤波方法.西北工业大学学报.2008,26(6):738-742页
[30]顾冬晴,秦永元.船用捷联惯导系统运动中对准的UKF设计.系统工程与电子技术.2006,28(8):1218-1220页
[31]程向红,李伯龙,王宇.基于PF的SINS动基座初始对准.中国惯性技术学报.2009,17(3):267-271页
[32]杨静,张洪钺,李骥.预测滤波理论在惯导非线性对准中的应用.中国惯性技术学报.2003,11(6):44-52页
[33]魏春玲,张洪钺,郝曙光.捷联惯导系统大方位失准角下的非线性对准.航天控制.2003,21(4):25-35页
[34]严恭敏,严卫生,徐德明.基于欧拉平台误差角的SINS非线性误差模型的研究.西北工业大学学报.2009,27(4):511-516页
[35]孙枫,袁赣南,张晓红.组合导航系统.哈尔滨:哈尔滨工程大学出版社.1996
[36]卞鸿巍,李安,覃方君,等.现代信息融合技术在组合导航中的应用.北京:国防工业出版社.2010
[37]张志贤.最优状态估计与系统辨识.西安:西北工业出版社.2004
[38]秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理.西安:西北工业大学出版社.1998
[39]付梦印,邓志红,闫莉萍. Kalman滤波理论及其在导航系统中的应用.北京:科学出版社.2010
[40]沈云峰,朱海,莫军,等.简化的Sage-Husa自适应滤波算法在组合导航中的应用及仿真.青岛大学学报.2001,16(1):44-47页
[41]鲁平,赵龙,陈哲.改进的Sage-Husa自适应滤波及其应用.系统仿真学报.2007,19(15):3503-3505页
[42]梁松,徐晓苏,黄永亮. Sage-Husa自适应滤波算法在捷联组合导航系统中的应用.2011,25(4):327-331页
[43]曾静,张国良,宋海涛.时变噪声系统的自适应强跟踪滤波器研究.航天控制.2009,27(1):3-6页
[44]邓自立.自校正滤波理论及其应用.哈尔滨:哈尔滨工业大学出版社.2003
[45]刘江,蔡伯根.一种组合导航的自适应信息融合算法.北京交通大学学报.2008,32(2):88-93页
[46]黄晓瑞,崔平远,崔祜涛. GPS/INS组合导航自适应滤波算法与仿真研究.飞行力学.2001,19(2):69-72页
[47]耿延睿,崔中兴,张洪钺,等.衰减因子自适应滤波及在组合导航中的应用.北京航空航天大学学报.2004,30(5):434-437页
[48]周东华,席裕庚,张钟俊.非线性系统带次优渐消因子的扩展卡尔曼滤波.控制与决策.1990,1-6页
[49]向礼.非线性滤波方法及其在导航中的应用研究.哈尔滨:哈尔滨工业大学博士学位论文.2009
[50]柴霖,袁建平,罗建军,等.非线性估计理论的最新进展.宇航学报.2005,26(3):380-384页
[51]武元新.对偶四元数导航算法与非线性高斯滤波研究.武汉:国防科技大学博士学位论文.2005
[52]李涛.非线性滤波方法在导航系统中的应用研究.武汉:国防科技大学博士学位论文.2003
[53]聂琦.非线性滤波及其在导航系统中的应用.哈尔滨:哈尔滨工程大学博士学位论文.2008
[54]谭红力.航弹族低成本捷联惯导系统关键技术研究.武汉:国防科技大学博士学位论文.2007
[55] Haykin S, Yee Paul, Derbez. Optimum Nonlinear Filter. IEEE Transactions on SignalProcessing.1997,45(11):2774-2786P
[56] Ito K, Xiong K. Gaussian Filter for Nonlinear Filtering Problems. IEEE Transactionson Automatic Control.2000,45(5):710-727P
[57] Julier S, Uhlmann J, Durrant-Whyte H F. A new method for Nonlinear Transformationof means and Covariances in Filters and Estimators. IEEE Transactions on AutomaticControl.2000,45(3):477-482P
[58] Li X R, Jilkov V P. A Survey of Maneuvering Target Tracking: ApproximationTechniques for Nonlinear Filtering. Proceedings of2004SPIE Conference on Signaland Data processing of Small Targets.2004:537-550P
[59] Arasaratnam I, Haykin S, Hurd T R. Cubatrue Kalman Filtering forContinuous-Discrete Systems: Theory and Simulations. IEEE Transaction on SignalProcessing.2010,58(10):4977-4993P
[60] Arasaratnam I, Haykin S. Cubature Kalman Filters. IEEE Transactions on AutomaticControl.2009,54(6):1254-1269P
[61] Solin A. Cubature Integration Methods in Non-Linear Kalman Filtering andSmoothing. Bachelor thesis, Aalto University school of Science and Technogy,Finland.2010
[62]穆静,蔡远利,张俊敏.容积粒子滤波算法及其应用.西安交通大学学报.2011,45(8):
[63]穆静,蔡远利.迭代容积卡尔曼滤波算法及其应用.系统工程与电子技术.2011,33(7):1454-1457页
[64]刘江,蔡伯根,唐涛,等.基于CKF的GNSS/INS列车组合定位鲁棒滤波算法.交通运输工程学报.2010,10(5):102-107页
[65]蔡伯根.低成本列控的列车组合定位理论与方法.北京:北京交通大学博士学位论文.2010
[66]郭辉.基于非线性滤波的目标跟踪算法研究.西安:西安电子科技大学硕士学位论文.2010
[67]王其,徐晓苏,张涛,等.模糊自适应滤波在水下航行器组合导航系统中的应用.中国惯性技术学报.2008,16(3):320-325页
[68]王其,徐晓苏.自适应联邦H∞滤波在水下组合导航中的应用.系统仿真学报.2009,21(4):1003-1006页
[69]范欣,张福斌,张永清,等.多传感器信息融合的水下航行器组合导航方法.火力与指挥控制.2011,36(3):78-81页
[70]刘明雍,周志远,赵涛.联邦滤波器的SINS/GNS/DVS水下组合导航.火力与指挥控制.2009,34(12):41-43页
[71]贺娟,张芳芳,贺晓蓉.一种自适应联邦滤波在车载组合导航中的应用.计算机工程与应用.2008,44(7):238-241页
[72]华冰,刘建业.联邦自适应滤波在SINS/北斗/星敏感器组合导航系统中的应用.东南大学学报(自然科学版).2004,34:190-194页
[73]蔡毅,张怡,杨舸,等.基于联邦滤波的自适应算法研究.计算机工程与应用.2009,45(1):108-110页
[74]赵琳,王吓弟,高伟.联邦滤波器理论及其在组合导航系统中的应用.哈尔滨工程大学学报.2000,21(6):5-9页
[75]杨常松,徐晓苏,汪丽云,等.信息融合技术在INS/GPS/DVL组合导航中的应用研究.中国惯性技术学报.2006,14(5):39-43页
[76]张崇猛,陈超英,庄良杰,等.信息融合理论及其在INS/GPS/Doppler组合导航系统中的应用.中国惯性技术学报.1999,7(3):1-8页
[77] Benchuan Zhou, Xianghong Cheng. Federated filtering algorithm based on fuzzyadaptive UKF for marine SINS/GPS/DVL integrated system. Control and DecisionConference. Chinese,2010:2082-2085P
[78]陶俊勇,邱静,温熙森,等.自适应联合滤波模型及其在车载SINS/GPS组合导航系统中的应用.信息与控制.2000,29(2):168-172页
[79]肖乾.多传感器组合导航系统信息融合技术研究.哈尔滨:哈尔滨工程大学博士学位论文.2005年
[80]马云峰. MSISN/GPS组合导航系统及其数据融合技术研究.南京:东南大学博士学位论文
[81]向礼,刘雨,苏宝库.基于QR分解的自适应差分滤波在SINS大方位失准角初始对准中的应用.宇航学报.2010,509-513页
[82]丁杨斌,申功勋. Unscented粒子滤波在静基座捷联惯导系统大方位失准角初始对准中的应用研究.航空学报.2007,28(4):397-401页
[83]龙瑞,秦永元,夏家和. CDKF在捷联惯导系统大失准角初始对准中的应用.西北工业大学学报.2010,28(3):364-368页
[84]丁杨斌,王新龙,王镇,等. Unscented卡尔曼滤波在SINS静基座大方位失准角初始对准中的应用研究.宇航学报.2006,27(6):1201-1204页
[85]严恭敏,严卫生,徐德明.简化UKF滤波在SINS大失准角初始对准中的应用.中国惯性技术学报.2008,16(3):253-264页
[86] Crassidis J L, Markley F L. Predictive Filtering for Nonlinear Systems. Journal ofGuidance, Control and Dynamics,1997,20(3):556-572P
[87]杨静,冀红霞,魏明坤.一种非线性滤波器的误差分析及应用.航空学报.2011,32(8):1469-1476页
[88]张惟,林宝军.改进粒子滤波与预测滤波相结合的单星敏姿态估计.控制与决策.2011,26(5):655-660页
[89]张红梅,邓正隆,林玉荣.一种基于模型预测的UKF方法.航空学报.2004,25(6):598-601页
[90]王晓旭,赵琳.基于MEP-UKF的组合导航滤波算法.宇航学报.2010,31(2):432-439页
[91]曹娟娟,房建成,盛蔚.大失准角下IMU空中快速对准技术.航空学报.2007,28(6):1395-1400页
[92]宫晓琳,房建成.基于预测滤波的捷联惯导任意双位置对准方法.北京航空航天大学学报.2008,34(5):533-536页
[93]刘百奇,宫晓琳,房建成.基于GPS观测量和模型预测滤波的机载SINS/GPS空中对准.中国惯性技术学报.2007,12(5):567-572页
[94]郁丰,刘建业,熊智.预测滤波算法在微小卫星姿态确定中的应用.宇航学报.2008,29(1):110-114页
[95] Myung H, Bang H. Spacecraft Parameter Estimation by Using Predictive FilterAlgorithm. Proceedings of the14th World Congress The international Federation ofAutomatic Control.2008:3452-3457P
[96]秦瑞,王顺伟,袁晓峰,等.多普勒计程仪/捷联惯导组合导航技术研究.战术导弹技术.2006,(6):68-72页
[97]刘明雍,胡俊伟,李闻白.一种基于改进无迹卡尔曼滤波的自主水下航行器组合导航方法研究.兵工学报.2011,32(2):252-256页
[98]董渊科,刘明雍,胡俊伟.基于非线性技术的AUV组合导航新方法.火力与指挥控制.2011,36(12):95-98页
[99]曹洁,刘繁明,陈勤,等. AUV中SINS/DVL组合导航技术研究.中国航海.2004,2:55-59页
[100]于旭.无人水下航行器SINS/DVL组合导航方法研究.辽东学院学报.2009,16(1):64-67页
[101]张常云.自适应滤波方法研究.航空学报.1998,19(7):96-99页
[102]张汉国,张洪钺.防止自适应Kalman滤波发散的补救方法.控制与决策.1991,53-56页
[103]梁霄,张均东,吴利红.基于改进强跟踪滤波器微小型机器人航位推算.大连海事大学学报.2011,37(2):64-68页
[104]夏启军,孙优贤,周春晖.渐消卡尔曼滤波的最佳自适应算法及其应用.自动化学报.1990,16(3):210-216页
[105]顾启泰,王颂.联邦滤波器理论研究.中国惯性技术学报.2003,10(6):34-40页
[106]顾启泰,王颂.联邦滤波器信息分配系数的优选算法.中国惯性技术学报.2003,11(6):1-6页
[107]王宇飞,黄显林,胡恒章.组合导航系统中基于特征值分解的自适应信息融合滤波算法.航空学报.2000,21(3):274-276页
[108]刘瑞华,刘建业.联邦滤波信息分配新方法.中国惯性技术学报.2001,9(2):28-32页
[109]王荣颖.高敬东.卞鸿巍.基于奇异值的联邦滤波器信息分配新方法.测试技术学报.2006,20(4):330-334页
[110] Vaganay J, Leonard J J, Curcio J A, et al. Experimental Validation of Moving LongBase-Line Navigation Concept. IEEE/OES Autonomous Underwater Vehicles.Piscataway, NJ, USA:IEEE,2004:59-65P
[111] Freitag L, Grund M, Singh S, et al. The WHOI Micro-Modem: An AcousticCommunications and Navigation System for Multiple Platforms. Proceedings of theOCEANS. Boston, USA: IEEE,2006:1-4P
[112] Antonelli G, Arrichiello F, Chiaverini S, et al. Observability analysis of relativelocation for AUVs based on ranging and depth measurements. IEEE InternationalConference on Robotics and Automation.2010:4276-4281P
[113] Papadopoulos G, Fallon M F, Leonard J J, et al. Cooperative location of MarineVehicles using Nonlinear State Estimation. IEEE/RSJ International Conference onRobotics and Systems.2010:4874-4879P
[114] Maczka D, Mach J, Stilwell D. Implementation of Cooperative Navigation Algorithmon a Platoon of Autonomous Underwater Vehicles [J] Ocean’s2007MTS/IEEE,Conference on Exhibition, Vancouver: Proceedings Ocean,2007:1-6P
[115] Eustice R M, Whitcomb L L, Singh H, et al. Recent Advances in Synchonous-ClockOne-Way-Travel-Time Acoustic Navigation. Proceeding of the OCEANS,2006:1-6P
[116] Grade A S, Stilwell D J. A Complete Solution to Underwater Navigation in thepresence of Unknown Currents Based on Range Measurements From a SingleLocation. Proceeding of the IEEE/RSJ Inernational Conference on IntelligentRobotics and Systems.2005:1420-1425P
[117] Stutters L, Liu H, Tiltman C, et al. Navigation Technologies for AutonomousUnderwater Vehicles. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Application and Reviews.2008,38(4):581-589P
[118] Bahr A. Cooperative Localization for Autonomous Underwater Vehicles. PhD thesis,Massachusetts Institute of Techoology and Woods Holes Oceanographic Institution,USA.2009
[119] Eustice R M, Whitcomb L L, Singh H, et al. Experimental Results inSynchronous-Clock One-Way-Travel-Time Acoustic Navigation for AutonomousUnderwater Vehicles. IEEE international Conference on Robotics and Automation.2007:4257-4264P
[120] Fallon M F, Papadopoulos G, Leonard J J. A Measurement Distribution Framework forCooperative Navigation using Multiple AUVs.2010IEEE International Conferenceon Robotics and Automation.2010:4256-4263P
[121] Bahr A, Walter M R, Leonard J J. Consistent Cooperative Localization.2009IEEEInternational Conference on Robotics and Automation.2009:3415-3422P
[122] Curcio J, Leonard J, Vaganay J, et al. Experiments in Moving Baseline Navigationusing Autonomous Surface Craft. in OCEANS, Proceedings of MTS/IEEE.2005:730-735P
[123] Walls J M, Eustice R M. Experimental Comparison of Synchronous-ClockCooperative Acoustic Navigation Algorithms.2007IEEE International Conference onRobotics and Automation.2007:4257-4264P
[124] Baccou P, Jouvencel B, Creuze V, et al. Cooperative positioning and navigation formutiple AUV operations. Ocean,2001, MTS/IEEE Conference and Exhibition.2001:1816-1820P
[125] Baccou P, Jouvencel B. Homing and navigation using one transponder for AUV,post-processing comparisons results with Long Base-Line navigation. Proceedings ofthe2002IEEE International Conference on Robotics&Automation.2002:4004-4009P
[126]洪奕光,程代展.非线性系统的分析与控制.北京:科学出版社.2005
[127]唐惠骏.非线性系统理论.北京:机械工业出版社.2010
[128] Hermann R, Krener A J. Nonlinear Controllability and Observability. IEEETransaction on Automatic Control.1997,22(5):728-740P