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

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.

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A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

Discrete Dynamics in Nature and Society ◽

10.1155/2016/3795961 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Shaolong Chen ◽

Renyu Yang ◽

Renhuan Yang ◽

Liu Yang ◽

Xiuzeng Yang ◽

...

Keyword(s):

Parameter Estimation ◽

Nonlinear Systems ◽

Fitness Function ◽

System Modeling ◽

Estimation Method ◽

Estimation Accuracy ◽

Function Parameter ◽

Swarm Optimization ◽

Modeling And Control ◽

Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

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Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

Timing & Time Perception ◽

10.1163/22134468-00002047 ◽

2015 ◽

Vol 3 (1-2) ◽

pp. 32-51 ◽

Cited By ~ 4

Author(s):

Nori Jacoby ◽

Peter E. Keller ◽

Bruno H. Repp ◽

Merav Ahissar ◽

Naftali Tishby

Keyword(s):

Parameter Estimation ◽

Lower Bound ◽

Computational Models ◽

Linear Models ◽

Estimation Error ◽

Explanatory Power ◽

Estimation Method ◽

Sensorimotor Synchronization ◽

Estimation Methods ◽

Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

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Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

10.36227/techrxiv.11966079.v1 ◽

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.

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VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Sensors ◽

10.3390/s21186302 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6302

Author(s):

Xupei Zhang ◽

Zhanzhuang He ◽

Zhong Ma ◽

Peng Jun ◽

Kun Yang

Keyword(s):

Neural Network ◽

Network Architecture ◽

Feature Fusion ◽

Estimation Error ◽

Estimation Method ◽

Signal Interference ◽

Estimation Accuracy ◽

Measurement Unit ◽

End To End ◽

Altitude Estimation

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

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Tension Estimation Method for Cable With Damper Using Natural Frequencies

Frontiers in Built Environment ◽

10.3389/fbuil.2021.603857 ◽

2021 ◽

Vol 7 ◽

Author(s):

Aiko Furukawa ◽

Katsuya Hirose ◽

Ryosuke Kobayashi

Keyword(s):

Natural Frequencies ◽

Numerical Models ◽

Estimation Error ◽

Estimation Method ◽

Higher Order ◽

Bending Stiffness ◽

Estimation Accuracy ◽

Test Cases ◽

Numerical Verification ◽

Vibration Method

In the maintenance of cable structures, such as cable-stayed bridges and extra-dosed bridges, it is necessary to estimate the tension acting on the cables. The safety of a cable is confirmed by checking whether the tension acting on the cable is within the allowable value. In current Japanese practice, the tension of a cable is estimated using the vibration method or the higher-order vibration method, which considers the natural frequencies of the cable. However, in recent years, the aerodynamic vibration of cables caused by wind has become a problem owing to the recent increase in the cable length and low damping performance of the cable itself. To suppress the aerodynamic vibration of cables, dampers are installed onto the cables. Because the damper changes the cable’s natural frequencies, the vibration method and higher-order vibration method are inappropriate for measuring the tension of a cable with a damper. In this paper, a new tension estimation method for a cable with a damper is proposed. To model a cable with a tensioned Bernoulli-Euler beam, theoretical equations for estimating the natural frequencies were derived. The proposed method inversely estimates the tension and bending stiffness of the cable and damper parameters, simultaneously, from the natural frequencies. The validity of the proposed method was confirmed by conducting numerical simulations and experiments. In the numerical verification, the performance of the proposed method was investigated using 80 numerical models. In the experimental verification, the estimation accuracy of the proposed method was investigated by considering 16 test cases. Thus, it was confirmed that the tension estimation accuracy was high, whereas the bending stiffness and damper parameter estimation accuracy was unsatisfactory. The tension estimation error was within 10% in all experimental cases, and within 5% if two test cases are excluded. The results obtained by the numerical and experimental verifications confirmed the effectiveness of the proposed method in tension estimation.

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Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

10.36227/techrxiv.11966079 ◽

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

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Secure State Estimation for Motion Monitoring of Intelligent Connected Vehicle Systems

Sensors ◽

10.3390/s20051253 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1253

Author(s):

Xiulan Song ◽

Xiaoxin Lou ◽

Junwei Zhu ◽

Defeng He

Keyword(s):

State Estimation ◽

Estimation Method ◽

Estimation Problem ◽

Connected Vehicles ◽

Connected Vehicle ◽

Monitoring Networks ◽

Cruise Control ◽

False Data Injection ◽

Vehicle Systems ◽

False Data Injection Attack

This paper considers the state estimation problem of intelligent connected vehicle systems under the false data injection attack in wireless monitoring networks. We propose a new secure state estimation method to reconstruct the motion states of the connected vehicles equipped with cooperative adaptive cruise control (CACC) systems. First, the set of CACC models combined with Proportion-Differentiation (PD) controllers are used to represent the longitudinal dynamics of the intelligent connected vehicle systems. Then the notion of sparseness is employed to model the false data injection attack of the wireless networks of the monitoring platform. According to the corrupted data of the vehicles’ states, the compressed sensing principle is used to describe the secure state estimation problem of the connected vehicles. Moreover, the L1 norm optimization problem is solved to reconstruct the motion states of the vehicles based on the orthogonaldecomposition. Finally, the simulation experiments verify that the proposed method can effectively reconstruct the motion states of vehicles for remote monitoring of the intelligent connected vehicle system.

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

Sensors ◽

10.3390/s20041073 ◽

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.

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SIS-BAM Algorithm for Angular Parameter Estimation of 2-D Incoherently Distributed Sources

10.21203/rs.3.rs-599025/v1 ◽

2021 ◽

Author(s):

Fulai Liu ◽

Kai Tang ◽

Hao Qin

Keyword(s):

Parameter Estimation ◽

Signal To Noise Ratio ◽

Estimation Method ◽

Estimation Accuracy ◽

Angular Parameter ◽

Invariant Structure ◽

Closed Form Solutions ◽

Low Dimension ◽

Incoherently Distributed Sources ◽

Shift Invariance

Abstract For two-dimensional (2-D) incoherently distributed sources, this paper presents an effective angular parameter estimation method based on shift invariant structure (SIS) of the beamspace array manifold (BAM), named as SIS-BAM algorithm. In the proposed method, a shift invariance structure (SIS) of the observed vectors is firstly established utilizing a generalized array manifold of an uniform linear orthogonal array (ULOA). Secondly, based on Fourier basis vectors and the SIS, a beamspace transformation matrix can be performed. It projects received signals into the corresponding beamspace, so as to carry out dimension reduction of observed signals in beamspace domain. Finally, according to the SIS of beamspace observed vectors, the closed form solutions of the nominal azimuth and elevation are derived. Compared with the previous works, the presented SIS-BAM method provides better estimation performance, for example: 1) the computational complexity is reduced due to dealing with low-dimension beamspace signals and avoiding spectral search; 2) it can not only improve the angular parameter estimation accuracy but also have excellent robustness to the change of signal-to-noise ratio (SNR) and snapshot number. The theoretical analysis and simulation results confirm the effectiveness of the proposed method.

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

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410016630000 ◽

2016 ◽

Vol 230 (14) ◽

pp. 2661-2672 ◽

Cited By ~ 2

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.

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