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The combination is defined linearly with two gains. When the gains are chosen a priori, a family of consistent estimates is presented in the note. The member in this family having minimal trace is said to be \"family-optimal.\" When the gains are to be optimized in order to achieve minimal trace of the family-optimal estimate of the covariance matrix, it is proved that the global optimal solution is actually given by the covariance intersection algorithm, which conducts the search only along a one-dimensional curve in the n-squared-dimensional space of combination gains.","publishedDate","2002-11-01","publishedYear",2002,"venue","IEEE Transactions on Automatic Control",248,"authors","keywords",[189,190,191],"pdfUrl","topVenue","Target Tracking and Data Fusion in Sensor Networks","Sparse and Compressive Sensing Techniques","Control Systems and Identification","paper_W2119300641","Forest fire monitoring with multiple small UAVs","{\"openalex\":\"W2119300641\",\"doi\":\"10.1109/acc.2005.1470520\"}","2026-05-20T08:50:16.200436441","2026-05-20T08:50:16.20849459","10.1109/acc.2005.1470520","W2119300641","Frequent updates concerning the progress of a forest fire are essential for effective and safe fire fighting. Since a forest fire is typically inaccessible by ground vehicles due to mountainous terrain, small unmanned air vehicles (UAVs) are emerging as a promising means of monitoring large forest fires. We present an effective UAV path planning algorithm utilizing infrared images that are collected on-board in real-time. To demonstrate the effectiveness of our path planning algorithm in realistic scenarios, we simulated the propagation of a forest fire with the EMBYR model. A new cooperative control mission concept is introduced where multiple low-altitude, short-endurance (LASE) UAVs are used for fire monitoring. By employing multiple UAVs, the effectiveness of the mission in terms of information update rate can be improved dramatically.","2005-08-10",2005,341,[204,205,206],"Robotic Path Planning Algorithms","UAV Applications and Optimization","Robotics and Sensor-Based Localization","paper_W2021436740","Optimal input signals for parameter estimation in dynamic systems--Survey and new results","{\"openalex\":\"W2021436740\",\"doi\":\"10.1109/tac.1974.1100701\"}","2026-05-15T18:19:12.194465087","2026-05-21T13:24:11.18486068","10.1109/tac.1974.1100701","W2021436740","This paper surveys the field of optimal input design for parameter estimation as it has developed over the last two decades. Many of the developments covered are only recent and have not appeared in the open literature elsewhere. After a brief introduction, the paper discusses the historical background of the subject both in the engineering and in the statistical literature. The concepts of optimality and input design are then discussed, followed by a derivation of the Fisher information matrix for multiinput multioutput systems with process noise. The design procedures are divided into the categories of time-domain methods and frequency-domain methods, with the former being more general, but also more time consuming (computationally). Several extensions to state constraints, continuous-time systems, etc., are discussed. A number of examples are given to illustrate the nature of optimal inputs. The results on time-domain synthesis with state constraints and their relationship to \"dual control\" are new.","1974-12-01",1974,557,[191,219,220],"Probabilistic and Robust Engineering Design","Advanced Multi-Objective Optimization Algorithms","paper_W2143030612","Approaches to adaptive filtering","{\"openalex\":\"W2143030612\",\"doi\":\"10.1109/tac.1972.1100100\"}","2026-05-14T17:03:49.360574405","2026-05-21T20:22:26.188277194","10.1109/tac.1972.1100100","W2143030612","The different methods of adaptive filtering are divided into four categories: Bayesian, maximum likelihood (ML), correlation, and covariance matching. The relationship between the methods and the difficulties associated with each method are described. New algorithms for the direct estimation of the optimal gain of a Kalman filter are given.","1972-10-01",1972,927,[189,233,234],"Inertial Sensor and Navigation","Distributed Sensor Networks and Detection Algorithms","paper_W2156064084","On the identification of variances and adaptive Kalman filtering","{\"openalex\":\"W2156064084\",\"doi\":\"10.1109/tac.1970.1099422\"}","2026-05-14T07:54:14.291663055","2026-05-21T11:10:17.202411412","10.1109/tac.1970.1099422","W2156064084","A Kalman filter requires an exact knowledge of the process noise covariance matrix Q and the measurement noise covariance matrix R . Here we consider the case in which the true values of Q and R are unknown. The system is assumed to be constant, and the random inputs are stationary. First, a correlation test is given which checks whether a particular Kalman filter is working optimally or not. If the filter is suboptimal, a technique is given to obtain asymptotically normal, unbiased, and consistent estimates of Q and R . This technique works only for the case in which the form of Q is known and the number of unknown elements in Q is less than n \\times r where n is the dimension of the state vector and r is the dimension of the measurement vector. For other cases, the optimal steady-state gain K op is obtained directly by an iterative procedure without identifying Q . As a corollary, it is shown that the steady-state optimal Kalman filter gain K op depends only on n \\times r linear functionals of Q . The results are first derived for discrete systems. They are then extended to continuous systems. A numerical example is given to show the usefulness of the approach.","1970-04-01",1970,1368,[189,234,247],"Scientific Measurement and Uncertainty Evaluation",{"_59":249,"_61":250},[],[251],{"_64":252,"_66":253,"_68":254,"_70":255},1784586813306,{"_73":257,"_75":76,"_77":258,"_78":-5,"_79":26,"_80":26,"_81":26,"_82":-5,"_83":-5,"_84":85,"_35":86,"_87":88},[72,256],"[\"portal\",\"industries\"]","industries",[259,260,261,262,263,264,265,266,267,268,269,270],1784586813305,{"_15":332,"_29":333,"_31":-5,"_32":334,"_33":335,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":336,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":333,"_58":335},{"_15":328,"_29":329,"_31":-5,"_32":-5,"_33":330,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":331,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":329,"_58":330},{"_15":322,"_29":323,"_31":-5,"_32":324,"_33":325,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":326,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":323,"_58":325},{"_15":318,"_29":319,"_31":-5,"_32":-5,"_33":320,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":321,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":319,"_58":320},{"_15":314,"_29":315,"_31":-5,"_32":-5,"_33":316,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":317,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":315,"_58":316},{"_15":308,"_29":309,"_31":-5,"_32":310,"_33":311,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":312,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":309,"_58":311},{"_15":303,"_29":304,"_31":-5,"_32":-5,"_33":305,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":306,"_283":26,"_284":307,"_285":26,"_286":-5,"_57":304,"_58":305},{"_15":299,"_29":300,"_31":-5,"_32":-5,"_33":301,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":302,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":300,"_58":301},{"_15":295,"_29":296,"_31":-5,"_32":-5,"_33":297,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":298,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":296,"_58":297},{"_15":291,"_29":292,"_31":-5,"_32":-5,"_33":293,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":294,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":292,"_58":293},{"_15":287,"_29":288,"_31":-5,"_32":-5,"_33":289,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":290,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":288,"_58":289},{"_15":271,"_29":272,"_31":-5,"_32":-5,"_33":273,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":274,"_42":-5,"_44":-5,"_275":276,"_277":-5,"_278":76,"_279":280,"_281":282,"_283":26,"_284":85,"_285":26,"_286":-5,"_57":272,"_58":273},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[313],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[327],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[337,338],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_341":24},"SITE_BASE_URL"]

R.К. Mehra 人物

R.К. Mehra · 相关文章