Optimal linear estimators for systems with multiple random measurement delays and packet dropouts

2014 ◽  
Author(s):  
Yu Luyang ◽  
Ma Jing ◽  
Sun Shuli
Keyword(s):  
Packet Dropouts ◽  
Random Measurement ◽  
Linear Estimators ◽  
Optimal Linear

scholarly journals Optimal Linear Estimators for Time-Delay Systems with Fading Measurements and Correlated Noises

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yazhou Li ◽  
Jiayi Li ◽  
Xin Wang
Keyword(s):  
Time Delay ◽  
Minimum Variance ◽  
Delay Systems ◽  
Multiplicative Noises ◽  
Linear Estimators ◽  
Discrete Linear Systems ◽  
Estimation Problems ◽  
Optimal Linear ◽  
State Augmentation ◽  
Correlated Noises

The optimal linear estimation problems are investigated in this paper for a class of discrete linear systems with fading measurements and correlated noises. Firstly, the fading measurements occur in a random way where the fading probabilities are regulated by probability mass functions in a given interval. Furthermore, time-delay exists in the system state and observation simultaneously. Additionally, the multiplicative noises are considered to describe the uncertainty of the state. Based on the projection theory, the linear minimum variance optimal linear estimators, including filter, predictor, and smoother are presented in the paper. Compared with conventional state augmentation, the new algorithm is finite-dimensionally computable and does not increase computational and storage load when the delay is large. A numerical example is provided to illustrate the effectiveness of the proposed algorithms.

scholarly journals Optimal linear quadratic control for wireless sensor and actuator networks with random delays and packet dropouts

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771877956
Author(s):  
Zhuwei Wang ◽  
Lihan Liu ◽  
Chao Fang ◽  
Xiaodong Wang ◽  
Pengbo Si ◽  
...  
Keyword(s):  
Frequency Control ◽  
Optimal Solution ◽  
Load Frequency Control ◽  
Wireless Sensor ◽  
Linear Quadratic Control ◽  
Linear Quadratic ◽  
Relay Nodes ◽  
Packet Dropouts ◽  
Frequency Control System ◽  
Optimal Linear

In this article, the optimal linear quadratic control problem is considered for the wireless sensor and actuator network with stochastic network-induced delays and packet dropouts. Considering the event-driven relay nodes, the optimal solution is obtained, which is a function of the current plant state and all past control signals. It is shown that the optimal control law is the same for all locations of the controller placement. Since the perfect plant state information is available at the sensor, the optimal controller should be collocated with the sensor. In addition, some issues such as the plant state noise and suboptimal solution are also discussed. The performance of the proposed scheme is investigated by an application of the load frequency control system in power grid.

MSTd Neuronal Basis Functions for the Population Encoding of Heading Direction

2003 ◽  
Vol 90 (2) ◽  
pp. 549-558 ◽  
Author(s):  
S. Ben Hamed ◽  
W. Page ◽  
C. Duffy ◽  
A. Pouget
Keyword(s):  
Optic Flow ◽  
Basis Functions ◽  
Eye Position ◽  
Nonlinear Functions ◽  
Medial Superior Temporal ◽  
A Value ◽  
Heading Direction ◽  
Linear Estimators ◽  
Average Accuracy ◽  
Optimal Linear

Basis functions have been extensively used in models of neural computation because they can be combined linearly to approximate any nonlinear functions of the encoded variables. We investigated whether dorsal medial superior temporal (MSTd) area neurons use basis functions to simultaneously encode heading direction, eye position, and the velocity of ocular pursuit. Using optimal linear estimators, we first show that the head-centered and eye-centered position of a focus of expansion (FOE) in optic flow, pursuit direction, and eye position can all be estimated from the single-trial responses of 144 MSTd neurons with an average accuracy of 2–3°, a value consistent with the discrimination thresholds measured in humans and monkeys. We then examined the format of the neural code for the head-centered position of the FOE, eye position, and pursuit direction. The basis function hypothesis predicts that a large majority of cells in MSTd should encode two or more signals simultaneously and combine these signals nonlinearly. Our analysis shows that 95% of the neurons encode two or more signals, whereas 76% code all three signals. Of the 95% of cells encoding two or more signals, 90% show nonlinear interactions between the encoded variables. These findings support the notion that MSTd may use basis functions to represent the FOE in optic flow, eye position, and pursuit.

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