A multiframe assignment algorithm for single sensor bearings-only tracking

Maneuvering Target Tracking in Dense Clutter Based on Particle Filtering

Surveillance of underwater for maritime warfare is traditionally being carried out by bearings-only tracking from many decades. The measurements used for state estimation here are nonlinear. Also the noise in the measurements and the process cannot be always Gaussian. The traditional nonlinear filtering algorithms like extended Kalman filter and modified gain extended Kalman filter use the linearisation of the system. The unscented Kalman filter (UKF) uses the sigma point approach based on Gaussian distribution to deal with nonlinearity. The particle filter (PF) uses the randomly generated particles based on the pdf of the state. PF is highly complex to implement and it also suffers from sample impoverishment. Hence, ensemble Kalman filter (EnKF) which is a simplified form of PF will be tried out for bearings-only tracking in this research work. The performance of EnKF is compared with PF and UKF and the results obtained using these filters in Matlab are presented.

This paper compares state estimation techniques for nonlinear stochastic dynamic systems, which are important for target tracking. Recently, several methods for nonlinear state estimation have appeared utilizing various random-point-based approximations for global filters (e.g., particle filter and ensemble Kalman filter) and local filters (e.g., Monte-Carlo Kalman filter and stochastic integration filters). A special emphasis is placed on derivations, algorithms, and commonalities of these filters. All filters described are put into a common framework, and it is proved that within a single iteration, they provide asymptotically equivalent results. Additionally, some deterministic-point-based filters (e.g., unscented Kalman filter, cubature Kalman filter, and quadrature Kalman filter) are shown to be special cases of a random-point-based filter. The paper demonstrates and compares the filters in three examples, a random variable transformation, re-entry vehicle tracking, and bearings-only tracking. The results show that the stochastic integration filter provides better accuracy than the Monte-Carlo Kalman filter and the ensemble Kalman filter with lower computational costs.

In this work, we develop shifted Rayleigh filter (SRF) for continuous-discrete (CD) systems. The proposed filter is applied to a real-life underwater passive bearings-only target tracking problem, where the discretization error is accounted for. The stochastic differential equation describing the process dynamics is approximated using Ito-Taylor expansion of order 1.5. Inorder to make a fair comparative study, the performance of the proposed algorithm, which we refer to as CD-SRF, is examined with other popular filters like the unscented Kalman filter (UKF), cubature Kalman filter (CKF) and Gauss-Hermite filter (GHF) in the CD domain. The filtering performance has been studied in terms of root mean square error (RMSE), track-loss and computational time. Theoretical Cramer-Rao lower bound (CRLB) is also implemented to determine the maximum achievable accuracy.

A multiple model based Unscented Kalman Filter (MMUKF) approach for position tracking of CNC servo drives is presented in this paper. The motion controller in the CNC machine has to generate motion profile to drive servo motor based on the feed back from the Encoder. The output of the feed back is affected by measurement variations due to friction and non linear behavior of tool motion. The unscented kalman filter(UKF)gives better results in non-linear motion applications by deterministic sampling of sigma points. The MM algorithm gives good estimate by combining the individual estimates of parallel filters matched to different motion models of the CNC Tool. In this paper, an Unscented Kalman Filter is used inside the multiple model algorithm to improve the estimation accuracy of tool motion at different dynamic regions of motion profile. The motion profile of tool is simulated by including fixed velocity and turn model and the MM based UKF gives better results compared to traditional kalman filters.

The paper proposes a novel approach for time delay and channel gain estimation for multipath fading Code Division Multiple Access (CDMA) signals using the Unscented Kalman Filter (UKF). Given the nonlinear dependency of the channel parameters on the received signals in multiuser/multipath scenarios, we show that the UKF achieves better performance than its linear counterparts. The UKF, which is a derivative-free Kalman filtering approach, avoids the errors associated with linearization in the conventional Extended Kalman Filter (EKF). The Numerical results also show that the proposed UKF is simpler to implement, and more resilient to near-far interference in CDMA networks.

It is presented a vision system based on a standard RGB digital camera to track an unmanned aerial vehicle (UAV) during the landing process aboard a ship. The developed vision system is located on the ship's deck and is used to track the UAV during all the landing process. A Ground-based vision system makes it possible to use an UAV with small size and weight, since the UAV will have less computer requirements. The proposed method uses a particle filter (PF) for pose estimation and an unscented Kalman filter (UKF) approach for filtering. This combination provides an adapted filtering framework for tracking. The implemented particle filter is inspired in the evolution strategies present in genetic algorithms with modifications in the mutation and crossover operators to avoid sample impoverishment. Results show that position and angular estimates precision is compatible with the automatic landing system requirements.

We herein proposed an adaptive track fusion algorithm based on unscented kalman filter (UKF) to improve the tracking accuracy of ground combat targets. This algorithm improved the distributed multi-sensor data fusion system and was used to fuse the data collected from the light, radar and vision sensors on one single combat platform to obtain local track estimation. Then, the local trajectory estimates of all combat platforms in the cluster are combined using an adaptive track synthesis algorithm to obtain the target track. Simulation results showed that this method can obtain more accurate trajectories of tracking targets.

Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.

The problem of tracking with very long range radars is studied in this paper. First, the measurement conversion from a radar's r-u-v coordinate system to the Cartesian coordinate system is discussed. Although the nonlinearity of this coordinate transformation appears insignificant based on the evaluation of the bias of the converted measurements, it is shown that this nonlinearity can cause significant covariance inconsistency in the conventionally converted measurements (CM1). Since data association depends critically on filter consistency, this issue is very important. Following this, it is shown that a suitably corrected conversion (CM2) eliminates the inconsistency. Then, initialized with the converted measurements (using CM2), four Cartesian filters are evaluated. It is shown that, among these filters, the converted measurement Kalman filter with second order Taylor expansion (CM2KF) is the only one that is consistent for very long range tracking scenarios. Another two approaches, the range-direction-cosine extended Kalman filter (ruvEKF) and the unscented Kalman filter (UKF) are also evaluated and shown to suffer from consistency problems. However, the CM2KF has the disadvantage of reduced accuracy in the range direction. To fix this problem, a consistency-based modification for the standard extended Kalman filter (E1KF) is proposed. This leads to a new filtering approach, designated as measurement covariance adaptive extended Kalman filter (MCAEKF). For very long range tracking scenarios, the MCAEKF is shown to produce consistent filtering results and be able to avoid the loss of accuracy in the range direction. It is also shown that the MCAEKF meets the posterior Carmer-Rao lower bound for the scenarios considered.

A new framework for estimation of lumped (for instance, Windkessel) model parameters from uncertain clinical measurements is presented. The ultimate aim is to perform patient-specific haemodynamic analysis. This framework is based on sensitivity analysis tools and the sequential estimation approach of the unscented Kalman filter. Sensitivity analysis and parameter estimation are performed in lumped parameter models, which act as reduced order surrogates of the 3D domain for haemodynamic analysis. While the goal of sensitivity analysis is to assess potential identifiability problems, the unscented Kalman filter estimation leads to parameter estimates based on clinical measurements and modelling assumptions. An application of such analysis and parameter estimation methodology is demonstrated for synthetic and real data. Equality constraints on various physiological parameters are enforced. Since the accuracy of the Windkessel parameter estimates depends on the lumped parameter representativeness, the latter is iteratively improved by running few 3D simulations while simultaneously improving the former. Such a method is applied on a patient-specific aortic coarctation case. Less than 3% and 9% errors between the clinically measured quantities and 3D simulation results for rest and stress are obtained, respectively. Knowledge on how these Windkessel parameters change from rest to stress can thus be learned by such an approach. Lastly, it is demonstrated that the proposed approach is capable of dealing with a wide variety of measurements and cases where the pressure and flow clinical measurements are not taken simultaneously.

Nonstationary phase boundary estimation in electrical impedance tomography using unscented Kalman filter

Two-dimensional temperature distribution estimation for a cross-flow planar solid oxide fuel cell stack

This paper presents an approach to test and validate a number of power system dynamic state estimation (PSDSE) algorithms, utilizing Real Time Digital Simulator (RTDS), a real-time simulation tool. WSCC 9-bus and IEEE 30-bus systems have been utilized to demonstrate the proposed approach. The test systems are developed on the RSCAD software of the RTDS. The conventional remote terminal unit (RTU) measurements and the phasor measurement unit (PMU) measurements are sent from the RTDS runtime environment to the MATLAB based PSDSE application at regular intervals for the estimation of the states, using software-in-the-loop (SIL) configuration. The PSDSE is solved by utilizing the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the cubature Kalman filter (CKF) approaches, and their relative performances are studied with the help of the simulation results on the two test systems.

In the atomic gravimeter, the interference fringe is obtained by scanning the chirp rate of the Raman lasers to compensate for the gravity induced Doppler frequency shift and then the gravitational acceleration can be obtained by extracting the phase of the atomic interference fringe. The phase of the interference fringe is proportional to the chirp rate, therefore the accurate estimation of the chirp rate is the key issue to obtain accurate gravitational acceleration. In practice, the model for estimating the chirp rate is not perfect, and there is system state error. In this paper, we propose a square root unscented Kalman filter (SR-UKF) approach for estimating gravitational acceleration, which can take the system state error into account. The experiments on simulated and real data are performed to demonstrate the feasibility and effectiveness of the SR-UKF method, which can provide an alternative approach for the gravity estimation. In addition, it can be combined with the tidal model for gravity estimation, and can also be used for real-time gravity measurement.