The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Xiqin Wang - One of the best experts on this subject based on the ideXlab platform.
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the Recursive Form of error bounds for rfs state and observation with p_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd <; 1), we must determine the existence or nonexistence of the state as well as its value. Following the possible detection/miss sequences within the framework of a random vector, possible observation sets sequences are first defined. Based on these sequences, the perFormance bounds can be represented by a multivariate function of some time-based auxiliary elements. Recursive relations for all the auxiliary elements are derived rigorously. For the multitarget case, this Recursive estimated bound can be applied without data association. Applications are analyzed through simulations to verify our theoretical results and show that our bounds are tighter than all other bounds for the case where detection probability Pd <; 1.
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The Recursive Form of Error Bounds for RFS State and Observation With $P_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd
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The Recursive Form of error bounds for RFS state and observation with P d < 1
2012 IEEE Radar Conference, 2012Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:In the target tracking and its engineering applications, Recursive state estimation of the target is of fundamental importance. This paper presents a Recursive perFormance bound for dynamic estimation and filtering problem, in the framework of the finite set statistics, for the first time. This perFormance limit is derived based on the meaningful distance error between two random sets, the target state set and its estimation. The estimation set is a function of the time-sequence of measurement sets. These measurement sets can be either empty or the Bernoulli random finite sets. Hence this bound can be applied when the probability of detection is less than unity. The state set model is a Markov process. The state set can be empty or not, which accounts for appearance and disappearance of the targets. Moreover, as a bound for discrete-time multidimensional filtering problems, it appears in Recursive Form. An application is presented to confirm the theory and reveal this bound is more general than previous bounds in the framework of random vector statistics.
Huisi Tong - One of the best experts on this subject based on the ideXlab platform.
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the Recursive Form of error bounds for rfs state and observation with p_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd <; 1), we must determine the existence or nonexistence of the state as well as its value. Following the possible detection/miss sequences within the framework of a random vector, possible observation sets sequences are first defined. Based on these sequences, the perFormance bounds can be represented by a multivariate function of some time-based auxiliary elements. Recursive relations for all the auxiliary elements are derived rigorously. For the multitarget case, this Recursive estimated bound can be applied without data association. Applications are analyzed through simulations to verify our theoretical results and show that our bounds are tighter than all other bounds for the case where detection probability Pd <; 1.
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The Recursive Form of Error Bounds for RFS State and Observation With $P_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd
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The Recursive Form of error bounds for RFS state and observation with P d < 1
2012 IEEE Radar Conference, 2012Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:In the target tracking and its engineering applications, Recursive state estimation of the target is of fundamental importance. This paper presents a Recursive perFormance bound for dynamic estimation and filtering problem, in the framework of the finite set statistics, for the first time. This perFormance limit is derived based on the meaningful distance error between two random sets, the target state set and its estimation. The estimation set is a function of the time-sequence of measurement sets. These measurement sets can be either empty or the Bernoulli random finite sets. Hence this bound can be applied when the probability of detection is less than unity. The state set model is a Markov process. The state set can be empty or not, which accounts for appearance and disappearance of the targets. Moreover, as a bound for discrete-time multidimensional filtering problems, it appears in Recursive Form. An application is presented to confirm the theory and reveal this bound is more general than previous bounds in the framework of random vector statistics.
Gianpaolo Vitale - One of the best experts on this subject based on the ideXlab platform.
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descriptor type kalman filter and tls exin speed estimate for sensorless control of a linear induction motor
IEEE Transactions on Industry Applications, 2014Co-Authors: F Alonge, Maurizio Cirrincione, Marcello Pucci, Filippo Dippolito, Antonino Sferlazza, Gianpaolo VitaleAbstract:This paper proposes a speed observer for linear induction motors (LIMs), which is composed of two parts: 1) a linear Kalman filter (KF) for the online estimation of the inductor currents and induced part flux linkage components; and 2) a speed estimator based on the total least squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, estimated by the KF, and provides as output the LIM linear speed, which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine. The end effects of the LIMs have been considered an uncertainty treated by the KF. The TLS EXIN neuron has been used to compute, in Recursive Form, the machine linear speed online since it is the only neural network able to solve online, in a Recursive Form, a TLS problem. The proposed KF TLS speed estimator has been tested experimentally on a suitably developed test setup, and it has been compared with the classic extended KF.
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neural sensorless control of linear induction motors by a full order luenberger observer considering the end effects
European Conference on Cognitive Ergonomics, 2012Co-Authors: Angelo Accetta, Maurizio Cirrincione, Marcello Pucci, Gianpaolo VitaleAbstract:This paper proposes a neural based full-order Luenberger Adaptive speed observer for sensorless linear induction motor (LIM) drives, where the linear speed is estimated on the basis of the linear neural network: TLS EXIN neuron. With this reference, a novel state space-vector representation of the LIM has been deduced, taking into consideration the so-called end effects. Starting from this standpoint, the state equation of the LIM has been discretized and rearranged in a matrix Form to be solved by a least-square technique. The TLS EXIN neuron has been used to compute on-line, in Recursive Form, the machine linear speed since it is the only neural network able to solve on-line in a Recursive Form a total least-squares problem. The proposed TLS full-order Luenberger Adaptive speed observer has been tested experimentally on suitably developed test setup.
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descriptor type kalman filter and tls exin speed estimate for sensorless control of a linear induction motor
3rd IEEE International Symposium on Sensorless Control for Electrical Drives (SLED 2012), 2012Co-Authors: F Alonge, Maurizio Cirrincione, Marcello Pucci, Filippo Dippolito, Antonino Sferlazza, Gianpaolo VitaleAbstract:This paper proposes a speed observer for linear induction motors which is composed of two parts: 1) a Kalman filter (KF for the on-line estimation of the machine state variables (inductor currents and induced part flux linkage components), 2) a speed estimator based on the total least-squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, as estimated by the KF, and provides as output the linear LIM speed which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine (RIM). The TLS EXIN neuron has been used to compute, in Recursive Form, the machine linear speed on-line, since it is the only neural network able to solve on-line in a Recursive Form a total least-squares problem. The proposed KF-TLS speed observer has been tested experimentally on a suitably developed test setup.
Huadong Meng - One of the best experts on this subject based on the ideXlab platform.
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the Recursive Form of error bounds for rfs state and observation with p_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd <; 1), we must determine the existence or nonexistence of the state as well as its value. Following the possible detection/miss sequences within the framework of a random vector, possible observation sets sequences are first defined. Based on these sequences, the perFormance bounds can be represented by a multivariate function of some time-based auxiliary elements. Recursive relations for all the auxiliary elements are derived rigorously. For the multitarget case, this Recursive estimated bound can be applied without data association. Applications are analyzed through simulations to verify our theoretical results and show that our bounds are tighter than all other bounds for the case where detection probability Pd <; 1.
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The Recursive Form of Error Bounds for RFS State and Observation With $P_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd
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The Recursive Form of error bounds for RFS state and observation with P d < 1
2012 IEEE Radar Conference, 2012Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:In the target tracking and its engineering applications, Recursive state estimation of the target is of fundamental importance. This paper presents a Recursive perFormance bound for dynamic estimation and filtering problem, in the framework of the finite set statistics, for the first time. This perFormance limit is derived based on the meaningful distance error between two random sets, the target state set and its estimation. The estimation set is a function of the time-sequence of measurement sets. These measurement sets can be either empty or the Bernoulli random finite sets. Hence this bound can be applied when the probability of detection is less than unity. The state set model is a Markov process. The state set can be empty or not, which accounts for appearance and disappearance of the targets. Moreover, as a bound for discrete-time multidimensional filtering problems, it appears in Recursive Form. An application is presented to confirm the theory and reveal this bound is more general than previous bounds in the framework of random vector statistics.
Hao Zhang - One of the best experts on this subject based on the ideXlab platform.
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the Recursive Form of error bounds for rfs state and observation with p_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd <; 1), we must determine the existence or nonexistence of the state as well as its value. Following the possible detection/miss sequences within the framework of a random vector, possible observation sets sequences are first defined. Based on these sequences, the perFormance bounds can be represented by a multivariate function of some time-based auxiliary elements. Recursive relations for all the auxiliary elements are derived rigorously. For the multitarget case, this Recursive estimated bound can be applied without data association. Applications are analyzed through simulations to verify our theoretical results and show that our bounds are tighter than all other bounds for the case where detection probability Pd <; 1.
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The Recursive Form of Error Bounds for RFS State and Observation With $P_d
IEEE Transactions on Signal Processing, 2013Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:This paper presents Recursive perFormance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd
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The Recursive Form of error bounds for RFS state and observation with P d < 1
2012 IEEE Radar Conference, 2012Co-Authors: Huisi Tong, Hao Zhang, Huadong Meng, Xiqin WangAbstract:In the target tracking and its engineering applications, Recursive state estimation of the target is of fundamental importance. This paper presents a Recursive perFormance bound for dynamic estimation and filtering problem, in the framework of the finite set statistics, for the first time. This perFormance limit is derived based on the meaningful distance error between two random sets, the target state set and its estimation. The estimation set is a function of the time-sequence of measurement sets. These measurement sets can be either empty or the Bernoulli random finite sets. Hence this bound can be applied when the probability of detection is less than unity. The state set model is a Markov process. The state set can be empty or not, which accounts for appearance and disappearance of the targets. Moreover, as a bound for discrete-time multidimensional filtering problems, it appears in Recursive Form. An application is presented to confirm the theory and reveal this bound is more general than previous bounds in the framework of random vector statistics.
