The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, the algorithms of combining channel estimation with CFO estimation are studied for mobile OFDM system. The comb-type pilot is used for channel estimation. There are some known algorithms for channel estimation such as LS (Least Square), Minimum Mean Square Error (MMSE), some adaptive estimators such as Kalman Filter, Extended Kalman Filter… To enhance performance, we modified the algorithm...
In this paper, we consider a wireless sensor network that consists of a group of sensor nodes estimating multiple independent LTI systems. Each point-to-point link between the sensor nodes is a slow frequency-flat fading channel and the states of the channel are described by a finite-state Markov channel (FSMC) model. We propose a transmission schedule of the sensors such that the overall estimation...
This paper investigates measurement scheduling for linear quadratic Gaussian (LQG) control. The measurement collected via the local sensor is sent over the bandwidth-limited channel to the remote controller for regulating the system state. An event-based scheme is designed at the local scheduler to smartly choose the time for sending the data, so as to reduce the uncertainty of system state at the...
The adaptive cubature strong tracking information filter(ACSTIF), which combines the strong tracking filter with the variational Bayesian method, has a good performance when the sudden change of state and the unknown variance of measurement noise appear. However, it also remains two problems. Firstly, the iteration of estimating the unknown variance of measurement noise is not very accurate. Secondly,...
Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target tracking. In practice, however, the noise statistics are usually unknown or not perfectly known. To deal with the estimation problem in linear discrete-time systems with Markov jump parameters, where the measurement noise covariance is unknown, a novel approach is presented in this paper. This approach...
In this paper, we consider the nonlinear filtering by using information geometric approach. Under the principle of Bayesian, the filtering problem has been converted to Bayesian estimation. Based on the estimation conditional on the measurement, the posterior probability density functions (PDFs) have constructed a statistical manifold. With the information geometric approach, the nonlinear characteristic...
This paper considers the problem of state estimations in virus/ worm epidemic dynamic system with time-dependent parameters in arbitrary sparse networks by using continuous-discrete Extended Kalman Filter (so-called Hybrid Extended Kalman Filter [1]). The virus spreading dynamic model has unmeasurable states and with highly nonlinearities which makes the state estimation complicated and not straightforward...
Flight state estimation and prediction of unmanned aerial vehicles (UAVs) are essential for safe operation, and they are primary bases of prognostics and health management (PHM). Telemetry data of UAV are the most significant resource for flight state tracking. However, telemetry data has the characters of high-dimension, non-linearity, uncertainty, and associated with noise, and it's hard to get...
This paper adresses the problem of simultaneously estimating the state and the fault of nonlinear discrete-time stochastic systems in light of the unknown input filtering framework. The fault and unknown disturbances which may cause great estimate errors and even divergence of conventional filters, affect both the system state and the measurements. Inspired by the robust two stage Kalman filter for...
Spectral Unmixing is a challenging and absorbing problem. Unmixning allows us to break down a pixel's composition into its material components. Many avenues of spectral unmixing have been attempted with considerable success. One such avenue is to frame the spectral unmixing problem as an Estimation-Measurement problem and avail the use of the well-known Kalman Filter (KF) technique. Two such recent...
We proposed a novel blind polarization de-multiplexing technique for higher order modulation signals based on unscented Kalman filter (UKF) in 3D Stokes space. Simulation results show that UKF has better performance of de-multiplexing and convergence speed compared to extended Kalman filters (EKF).
This paper presents a kind of self-tuning filter in the colored noise environment when the noise variance is unknown. The main method of this filter is whitening the colored noise, and the correlation function is used to get the estimations of the variance of the noise. An example for the target tracking system is presented to design the self-tuning filter for the position and velocity, the simulating...
We propose a novel joint tracking and mitigation scheme for linear dynamic impairments using a 3-stage extended Kalman filter. Simulation results show that it can quickly track and compensate the impairments including multi-polarization effects, frequency offset and phase noise.
We consider the problem of range-based cooperative localization for multiple agents in a GPS-denied environment. For the minimal reference purpose, there exists only one single landmark whose global position is known to a subset of agents. All agents which are equipped with Ultra Wideband (UWB) radars are able to measure the relative distance between themselves and their communicating neighbours....
The paper is concerned with the target tracking in range-only wireless sensor networks (WSNs). To integrate the separated measurements from the WSN, a sequential fusion estimation method is presented in the sense of linear minimum mean squared error (LMMSE). Moreover, the un-scented transformation is used to implement the recursion of means and covariances, and this kind estimator is termed as sequential...
In this paper, we propose a novel distributed Kalman filter (KF) over sensor networks using partial diffusion strategy, where each sensor only communicates with its neighbours and individual estimation vectors are partially transmitted to obtain local ones through convex combinations under communication bandwidth constraints. Proposed solution can obtain a trade-off between communications burden and...
In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter (CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more...
For multi-model multisensor system with uncertain variance linearly correlated white noises, the problems of designing robust weighted fusion Kalman estimators (predictor, filter, smoother) are addressed. According to the minimax robust estimation principle, applying Lyapunov equation approach, a unified design approach to obtain the local and three weighted fusion robust Kalman estimators of the...
This paper deals with the scenario of two multi-UAV systems (so-called teams), each flying from a starting point to a target point. The teams meet on their ways and have to avoid collisions in the horizontal plane. Both teams work independent of each other but use the same formation-control and obstacle-collision-avoidance algorithm. The leaders measure obstacles' positions, use a Kalman Filter to...
In this paper an Extended Kalman Filter (EKF) is used as an observer for temperature monitoring, like a virtual sensor, of a metal-polymer fibre based heater structure. Metal-coated polymers are relevant for the realisation of smart systems (capable of both sensing and actuating). A real-time implementation of the temperature estimator is important to guarantee a gentle, fault-free operation of the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.