Modified Kalman Filtering Research Articles

Stealthy FDI attacks on modified Kalman filtering in complex networks with non-Gaussian-Lévy noise

This paper investigates the problem of network security for stealthy false data injection (FDI) attacks under modified Kalman filtering (MKF) over complex networks with non-Gaussian-Lévy noise (NGLN). Initially, a modified Kalman filter (MKFR) is proposed to estimate system states, where the estimation function is related to the probabilistic characteristics of Lévy noise, and the saturation threshold is designed in relation to Lévy noise parameters. The upper bound of error covariance is obtained, and the boundedness of the upper bound is proven through the use of mathematical induction and iterative methods. Second, based on the MKF, this paper proposes a two-channel stealthy FDI attacks (TSFAs) strategy that is related only to the system model, which is injected into the sensor-to-controller (S-C) and controller-to-actuator (C-A) transmission channels at the same time. In addition, the attacker sets two MKFRs as observers to estimate the states of the target system, which are used to adjust the attack signal. Third, a sufficient condition is obtained that demonstrates the stability of the system. Meanwhile, TSFAs can avoid being detected by the residual-based detector to guarantee stealthiness. Finally, the effectiveness of the MKF is verified by the numerical simulation, and the stealthiness of the TSFAs and the impact on the system stability are also demonstrated.

Estimation of carrier frequency offset for FBMC/OQAM by using modified Kalman filtering

SummaryFilter bank multicarrier (FBMC) based on offset quadrature amplitude modulation (OQAM) is regarded as the prospective system for mobile communication. To utilize the potential of the system, the issues like carrier frequency offset have to be investigated extensively. The modulations in the multicarrier system draw a great interest among researchers and engineers in the telecommunication field. Despite the OFDM advantages, significant disadvantages in the system enable researches to approach into efficiently evolved FBMC system. In this paper, the detrimental effects caused by carrier frequency offset on the received signal is considered. Carrier Frequency Offset (CFO) compensation has been effectively undertaken by modified Kalman filtering. The compensation technique not only depends on the specific FBMC modification but also influenced by the selected receiver. A comprehensive analysis of the frequency offset response under three conditions (without offset, with offset, and the compensated samples) has been performed in the proposed system with performance measures as bit error rate (BER) and signal‐to‐noise ratio. 9 CFO compensation is analyzed by varying the channel characteristics (additive white Gaussian noise [AWGN] and Rayleigh Fading) and subcarrier filtering (prototype filter, Gaussian filter, and root‐raised coefficients). From the overall analysis, it is observed that the BER varies from 0.1 to 0.001.

Using Relative Orientation to Improve the Accuracy of Exterior Orientation Parameters of Low Cost POS

A simple and inexpensive <small>POS</small> for application to light airborne platforms was developed, and a modified Kalman Filtering was designed to integrate <small>GNSS/IMU/</small>Image data, which uses the relative orientation information to improve the accuracy of the exterior orientation parameters. The precise relative orientation of conjugate images was obtained using a <small>SIFT/SFM</small> matching algorithm. The relative exterior orientations were transformed from the camera frame to the navigation frame before they were used as external update information of the Kalman Filter. Combining all the relative orientation information retrieved from images, the Kalman Filter can give an improved output for the exterior orientation parameters. Airborne result from the tests of one straight strip shows that the heading accuracy of a <small>GNSS/MEMS-IMU</small> was improved from 0.26° to 0.10°, and the result from one closed strip shows that heading accuracy was improved from 1.11° to 0.85° and roll accuracy from 0.54° to 0.43°.

Inertial Sensor Based Indoor Localization and Monitoring System for Emergency Responders

This paper presents a novel indoor localization and monitoring system based on inertial sensors for emergency responders. The system utilizes acceleration, angular rate and magnetic field sensors and consists of three parts. The first part is a modified Kalman filtering which implements the sensor data fusion and meanwhile detects and minimizes the magnetic field disturbances, so as to provide a long term stable orientation solution. The second part is zero velocity updating which resets the velocity within still phase to deliver accurate position information. The last part of the system is body movement monitoring, which is achieved by calculating the relative position of each body segment based on the transformation of coordinate frame of each body segment. The experimental result shows that the system is able to track person indoors in both walking and running cases, and to monitor the body movement during whole period of experiment.

Mobile Location with NLOS Identification and Mitigation Based on Modified Kalman Filtering

In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed. Numerical results illustrate that the location errors of the proposed algorithm are all significantly smaller than those of the iterated NLOS EKF algorithm and the conventional EKF algorithm in different LOS/NLOS conditions. Moreover, this location method does not require any statistical distribution knowledge of the NLOS error. In addition, complexity experiments suggest that this algorithm supports real-time applications.

Functional mixed-effects model for periodic data

Periodic data are frequently collected in biomedical experiments. We consider the underlying periodic curves giving rise to these data, and account for the periodicity in their functional model to improve estimation and inference. We propose to incorporate the periodic constraint in the functional mixed-effects model setting. Both the fixed functional effects and random functional effects are modeled in the same periodic functional space, hence the population-average estimates and subject-specific predictions are all periodic. An efficient algorithm is given to estimate the proposed model by an O(N) modified Kalman filtering and smoothing algorithm. The proposed method is evaluated in different scenarios through simulations. Treatments to none-full period data and missing observations along the period are also given. Analysis of a cortisol data set obtained from a study on fibromyalgia is conducted as illustration.

Modified Kalman filtering

A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations. >