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Information Estimation in the Presence of Incomplete Data","{\"openalex\":\"W2802250175\"}","openalex","2026-05-14T06:07:28.293496291","2026-05-21T08:18:38.184492285","doi","arxivId","openalexId","W2802250175","title","abstractText","publishedDate","1996-01-01","publishedYear",1996,"venue","Dialnet (Universidad de la Rioja)","citationCount",1673,"authors","keywords",[138,30],"pdfUrl","topVenue","Target Tracking and Data Fusion in Sensor Networks","paper_W2126885789","A Consistent Metric for Performance Evaluation of Multi-Object Filters","{\"openalex\":\"W2126885789\",\"doi\":\"10.1109/tsp.2008.920469\"}","2026-05-14T05:06:41.386978025","2026-05-21T07:26:52.201568959","10.1109/tsp.2008.920469","W2126885789","The concept of a miss-distance, or error, between a reference quantity and its estimated/controlled value, plays a fundamental role in any filtering/control problem. Yet there is no satisfactory notion of a miss-distance in the well-established field of multi-object filtering. In this paper, we outline the inconsistencies of existing metrics in the context of multi-object miss-distances for performance evaluation. We then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.","2008-07-24",2008,"IEEE Transactions on Signal Processing",1846,[138,152,30],"Water Systems and Optimization","paper_W1650765400","Factorization Methods for Discrete Sequential Estimation","{\"openalex\":\"W1650765400\",\"doi\":\"10.1016/s0076-5392(08)x6052-7\"}","2026-05-14T05:04:34.259187136","2026-05-21T07:23:46.191504397","10.1016/s0076-5392(08)x6052-7","W1650765400","1977-01-01",1977,"Mathematics in Science and Engineering/Mathematics in science and engineering",1862,[30,165,138],"Simulation Techniques and Applications","paper_W2137585588","The Gaussian Mixture Probability Hypothesis Density Filter","{\"openalex\":\"W2137585588\",\"doi\":\"10.1109/tsp.2006.881190\"}","2026-05-14T04:53:34.187116503","2026-05-21T07:13:40.188077421","10.1109/tsp.2006.881190","W2137585588","A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise, and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density (PHD) recursion to propagate the posterior intensity, which is a first-order statistic of the random finite set of targets, in time. At present, there is no closed-form solution to the PHD recursion. This paper shows that under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture. More importantly, closed-form recursions for propagating the means, covariances, and weights of the constituent Gaussian components of the posterior intensity are derived. The proposed algorithm combines these recursions with a strategy for managing the number of Gaussian components to increase efficiency. This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters.","2006-10-18",2006,1903,[138,30,178],"Gaussian Processes and Bayesian Inference","paper_W2134332047","The Power of Convex Relaxation: Near-Optimal Matrix Completion","{\"openalex\":\"W2134332047\",\"doi\":\"10.1109/tit.2010.2044061\"}","2026-05-14T04:19:58.362772014","2026-05-21T06:16:42.225180962","10.1109/tit.2010.2044061","W2134332047","This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering. In general, accurate recovery of a matrix from a small number of entries is impossible, but the knowledge that the unknown matrix has low rank radically changes this premise, making the search for solutions meaningful. This paper presents optimality results quantifying the minimum number of entries needed to recover a matrix of rank r exactly by any method whatsoever (the information theoretic limit). More importantly, the paper shows that, under certain incoherence assumptions on the singular vectors of the matrix, recovery is possible by solving a convenient convex program as soon as the number of entries is on the order of the information theoretic limit (up to logarithmic factors). This convex program simply finds, among all matrices consistent with the observed entries, that with minimum nuclear norm. As an example, we show that on the order of nr log( n ) samples are needed to recover a random n x n matrix of rank r by any method, and to be sure, nuclear norm minimization succeeds as soon as the number of entries is of the form nr polylog( n ).","2010-04-26",2010,"IEEE Transactions on Information Theory",2144,[192,193,30],"Sparse and Compressive Sensing Techniques","Stochastic Gradient Optimization Techniques","paper_W2014787937","Multitarget bayes filtering via first-order multitarget moments","{\"openalex\":\"W2014787937\",\"doi\":\"10.1109/taes.2003.1261119\"}","2026-05-14T04:08:59.192783974","2026-05-21T06:04:08.191071315","10.1109/taes.2003.1261119","W2014787937","The theoretically optimal approach to multisensor-multitarget detection, tracking, and identification is a suitable generalization of the recursive Bayes nonlinear filter. Even in single-target problems, this optimal filter is so computationally challenging that it must usually be approximated. Consequently, multitarget Bayes filtering will never be of practical interest without the development of drastic but principled approximation strategies. In single-target problems, the computationally fastest approximate filtering approach is the constant-gain Kalman filter. This filter propagates a first-order statistical moment - the posterior expectation - in the place of the posterior distribution. The purpose of this paper is to propose an analogous strategy for multitarget systems: propagation of a first-order statistical moment of the multitarget posterior. This moment, the probability hypothesis density (PHD), is the function whose integral in any region of state space is the expected number of targets in that region. We derive recursive Bayes filter equations for the PHD that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets. We also show that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense.","2003-10-01",2003,"IEEE Transactions on Aerospace and Electronic Systems",2221,[138,30,207],"Forecasting Techniques and Applications","paper_W2164522996","Kalman Filtering With Intermittent Observations","{\"openalex\":\"W2164522996\",\"doi\":\"10.1109/tac.2004.834121\"}","2026-05-14T03:27:04.299195302","2026-05-21T05:24:53.180072802","10.1109/tac.2004.834121","W2164522996","Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs. We also give upper and lower bounds on this expected state error covariance.","2004-09-01",2004,"IEEE Transactions on Automatic Control",2508,[138,30,221],"Energy Efficient Wireless Sensor Networks","paper_W2054091988","Cubature Kalman Filters","{\"openalex\":\"W2054091988\",\"doi\":\"10.1109/tac.2009.2019800\"}","2026-05-14T02:17:57.312083015","2026-05-21T04:15:25.180647541","10.1109/tac.2009.2019800","W2054091988","In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a third-degree spherical-radial cubature rule that provides a set of cubature points scaling linearly with the state-vector dimension. The CKF may therefore provide a systematic solution for high-dimensional nonlinear filtering problems. The paper also includes the derivation of a square-root version of the CKF for improved numerical stability. The CKF is tested experimentally in two nonlinear state estimation problems. In the first problem, the proposed cubature rule is used to compute the second-order statistics of a nonlinearly transformed Gaussian random variable. The second problem addresses the use of the CKF for tracking a maneuvering aircraft. The results of both experiments demonstrate the improved performance of the CKF over conventional nonlinear filters.","2009-05-27",2009,3295,[138,234,30],"Fault Detection and Control Systems","paper_W1784695092","Signal detection theory and psychophysics","{\"openalex\":\"W1784695092\"}","2026-05-14T00:50:41.241897291","2026-05-21T02:25:33.187080967","W1784695092","Book on statistical decision theory and sensory processes in signal detection theory and psychophysics","1966-01-01",1966,10001,[246,30],"Neural Networks and Applications","paper_W2118020555","On Estimation of a Probability Density Function and Mode","{\"openalex\":\"W2118020555\",\"doi\":\"10.1214/aoms/1177704472\"}","2026-05-14T00:50:10.349576167","2026-05-21T02:24:12.278924136","10.1214/aoms/1177704472","W2118020555","Given a sequence of independent identically distributed random variables with a common probability density function, the problem of the estimation of a probability density function and of determining the mode of a probability function are discussed. Only estimates which are consistent and asymptotically normal are constructed. (Author)","1962-09-01",1962,"The Annals of Mathematical Statistics",10452,[261,30,262],"https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-33/issue-3/On-Estimation-of-a-Probability-Density-Function-and-Mode/10.1214/aoms/1177704472.pdf","Bayesian Methods and Mixture Models","Probability and Risk Models",{"_58":264,"_60":265},[],[266],{"_63":267,"_65":268,"_67":269,"_69":270},1780604855716,{"_72":272,"_74":75,"_76":267,"_77":-5,"_78":26,"_79":26,"_80":26,"_81":-5,"_82":-5,"_83":84,"_35":85,"_86":87},[71,271],"[\"portal\",\"industries\"]","industries",[273,274,275,276,277,278,279,280,281,282,283,284],{"_15":345,"_29":346,"_31":-5,"_32":347,"_33":348,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":349,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":346,"_57":348},{"_15":341,"_29":342,"_31":-5,"_32":-5,"_33":343,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":344,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":342,"_57":343},{"_15":335,"_29":336,"_31":-5,"_32":337,"_33":338,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":339,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":336,"_57":338},{"_15":331,"_29":332,"_31":-5,"_32":-5,"_33":333,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":334,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":332,"_57":333},{"_15":327,"_29":328,"_31":-5,"_32":-5,"_33":329,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":330,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":328,"_57":329},{"_15":321,"_29":322,"_31":-5,"_32":323,"_33":324,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":325,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":322,"_57":324},{"_15":316,"_29":317,"_31":-5,"_32":-5,"_33":318,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":319,"_297":26,"_298":320,"_299":26,"_101":-5,"_56":317,"_57":318},{"_15":312,"_29":313,"_31":-5,"_32":-5,"_33":314,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":315,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":313,"_57":314},{"_15":308,"_29":309,"_31":-5,"_32":-5,"_33":310,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":311,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":309,"_57":310},{"_15":304,"_29":305,"_31":-5,"_32":-5,"_33":306,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":307,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":305,"_57":306},{"_15":300,"_29":301,"_31":-5,"_32":-5,"_33":302,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":303,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":301,"_57":302},{"_15":285,"_29":286,"_31":-5,"_32":-5,"_33":287,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":288,"_42":-5,"_44":-5,"_289":290,"_291":-5,"_292":75,"_293":294,"_295":296,"_297":26,"_298":84,"_299":26,"_101":-5,"_56":286,"_57":287},"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","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",[326],"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",[340],"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",[350,351],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_354":24},"SITE_BASE_URL"]

Distributed Sensor Networks and Detection Algorithms 技术

Distributed Sensor Networks and Detection Algorithms · 相关事件