scholarly journals Optimal Sensor and Relay Nodes Power Scheduling for Remote State Estimation with Energy Constraint

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1073
Author(s):  
Yufei Han ◽  
Mengqi Cui ◽  
Shaojun Liu
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Relay Node ◽  
Estimation Accuracy ◽  
Average Error ◽  
Relay Nodes ◽  
Trade Off ◽  
Error Covariance ◽  
Power Scheduling ◽  
Remote State Estimation

We study the sensor and relay nodes’ power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes’ scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.

scholarly journals Fractional baud-length coding

2011 ◽  
Vol 29 (6) ◽  
pp. 1189-1196
Author(s):  
J. Vierinen
Keyword(s):  
Estimation Error ◽  
Estimation Accuracy ◽  
Target Range ◽  
Estimation Errors ◽  
Range Resolution ◽  
Error Covariance ◽  
Novel Approach ◽  
Amplitude Estimation ◽  
Radar Measurements ◽  
Low Altitude

Abstract. We present a novel approach for modulating radar transmissions in order to improve target range and Doppler estimation accuracy. This is achieved by using non-uniform baud lengths. With this method it is possible to increase sub-baud range-resolution of phase coded radar measurements while maintaining a narrow transmission bandwidth. We first derive target backscatter amplitude estimation error covariance matrix for arbitrary targets when estimating backscatter in amplitude domain. We define target optimality and discuss different search strategies that can be used to find well performing transmission envelopes. We give several simulated examples of the method showing that fractional baud-length coding results in smaller estimation errors than conventional uniform baud length transmission codes when estimating the target backscatter amplitude at sub-baud range resolution. We also demonstrate the method in practice by analyzing the range resolved power of a low-altitude meteor trail echo that was measured using a fractional baud-length experiment with the EISCAT UHF system.

scholarly journals Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

2020 ◽  
Author(s):  
Tai-shan Lou ◽  
Dong-xuan Han ◽  
Xiao-liang Yang ◽  
Su-xia Jiang
Keyword(s):  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Sampling Methods ◽  
Estimation Error ◽  
Sensitivity Function ◽  
Estimation Accuracy ◽  
Uncertain Parameters ◽  
Excellent Performance ◽  
Deterministic Sampling

To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

scholarly journals Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

2020 ◽  
Author(s):  
Tai-shan Lou ◽  
Dong-xuan Han ◽  
Xiao-liang Yang ◽  
Su-xia Jiang
Keyword(s):  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Sampling Methods ◽  
Estimation Error ◽  
Sensitivity Function ◽  
Estimation Accuracy ◽  
Uncertain Parameters ◽  
Excellent Performance ◽  
Deterministic Sampling

To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

scholarly journals Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation

2014 ◽  
Vol 47 (3) ◽  
pp. 122-127 ◽  
Author(s):  
Yuzhe Li ◽  
Daniel E. Quevedo ◽  
Vincent Lau ◽  
Subhrakanti Dey ◽  
Ling Shi
Keyword(s):  
Energy Harvesting ◽  
State Estimation ◽  
Transmission Power ◽  
Power Scheduling ◽  
Remote State Estimation

Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation

2016 ◽  
Vol 230 (14) ◽  
pp. 2661-2672 ◽  
Author(s):  
Nan Wu ◽  
Lei Chen ◽  
Yongjun Lei ◽  
Fankun Meng
Keyword(s):  
State Estimation ◽  
Phase Trajectory ◽  
Prior Information ◽  
Estimation Error ◽  
The State ◽  
Estimation Accuracy ◽  
Unknown Input ◽  
Noise Variance ◽  
Process Noise ◽  
Variance Matrix

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

scholarly journals Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network

2021 ◽  
Vol 11 (10) ◽  
pp. 4564
Author(s):  
Yongtao Shui ◽  
Yu Wang ◽  
Yu Li ◽  
Yongzhi Shan ◽  
Naigang Cui ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
State Estimation ◽  
Target Tracking ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Consensus Method ◽  
False Data Injection ◽  
Radar Network ◽  
Global Consistency

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

scholarly journals On the Performance of Power Splitting Energy Harvested Wireless Full-Duplex Relaying Network with Imperfect CSI over Dissimilar Channels

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tan N. Nguyen ◽  
Minh Tran ◽  
Phuong T. Tran ◽  
Phu Tran Tin ◽  
Thanh-Long Nguyen ◽  
...  
Keyword(s):  
System Performance ◽  
Estimation Error ◽  
Relay Node ◽  
Full Duplex ◽  
Channel Estimation Error ◽  
Power Splitting ◽  
Imperfect Csi ◽  
Amplify And Forward ◽  
Relay Nodes ◽  
Splitting Factor

The energy harvesting amplify-and-forward full-duplex relaying network over the dissimilar fading environments in imperfect CSI condition is investigated. In this system model, the energy, and information are transferred from the source to the relay nodes by the power splitting protocol with helping of the full-duplex relay node. Firstly, the outage probability, achievable throughput, and the optimal power splitting factor in terms of the analytical mathematical expressions were proposed, analyzed, and demonstrated. Furthermore, the system performance of the proposed model on the connection with all system parameters is rigorously studied. Finally, the numerical results demonstrated and convinced one that the analytical and the simulation results are matched well with each other for all system parameter values using Monte-Carlo simulation. The results show that the system performance degrades significantly but is still in a permissible interval while the channel estimation error increases and the system performance of the mixing scenarios is better in comparison with the Rayleigh-Rayleigh scenario.

Event-based finite horizon state estimation for stochastic systems with network-induced phenomena

2018 ◽  
Vol 41 (6) ◽  
pp. 1580-1589 ◽  
Author(s):  
Li Liu ◽  
Aolei Yang ◽  
Wenju Zhou ◽  
Xiaowei Tu ◽  
Gang Wang ◽  
...  
Keyword(s):  
State Estimation ◽  
Stochastic Systems ◽  
Estimation Error ◽  
Estimation Method ◽  
Finite Horizon ◽  
Drop Out ◽  
System Model ◽  
Transmission Delays ◽  
Error Covariance ◽  
Compensation Approach

This paper investigates a finite horizon state estimation problem for a class of discrete-time stochastic systems with random transmission delays and out-of-order packets of data. Employing an event-driven signal-choosing scheme of logic zero-order-holder (LZOH), a system model is established synthetically in a unified form considering the network-induced phenomena, to drop out-of-order packets and improve system performance. By virtue of the established system model, a novel minimum error covariance matrix for the augmented state-space is obtained from the estimated variance constraint. With the aid of a finite horizon, the upper boundary of estimation error covariance is introduced during the information transmission from sensor to estimator, and the appropriate filter parameters are probed. To improve the estimation performance and alleviate the computation burden, an estimation-based compensation approach for random transmission delays is proposed using the received valid signals. Finally, the effectiveness and applicability of the proposed state estimation method are illustrated by a numerical simulation.

Dynamic sensor transmission power scheduling for remote state estimation

Automatica ◽  
2014 ◽  
Vol 50 (4) ◽  
pp. 1235-1242 ◽  
Author(s):  
Zhu Ren ◽  
Peng Cheng ◽  
Jiming Chen ◽  
Ling Shi ◽  
Huanshui Zhang
Keyword(s):  
State Estimation ◽  
Transmission Power ◽  
Power Scheduling ◽  
Remote State Estimation
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