Flood water level prediction and tracking using particle filter algorithm

Particle filtering with adaptive resampling scheme for modal frequency identification and dispersion curves estimation in ocean acoustics

This paper considers an effective method for nonlinear acoustic echo cancellation (NL-AEC). More specifically, we model the nonlinear echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse response filter. To estimate the posterior probability distribution of the state vector, an elitist particle filter based on evolutionary strategies (EPFES) has been proposed, which evaluates realizations of the latent state vector based on long-term fitness measures. This method includes a manually-tuned recursive calculation of the probabilities that the observation has been produced by the state-vector realizations. For avoiding this manual tuning, we introduce a new approach denoted as Elitist Resampling Particle Filtering (ERPF) which can also be shown to combine the advantages of the Sequential Importance Sampling Particle Filter (SIS-PF) and the Sequential Importance Sampling/Resampling Particle Filter (SIR-PF). This new approach allows universal use and leads to superior system identification performance compared to both the original EPFES as well as the SIR-PF, as verified for a simulated scenario and a real smartphone recording.

Fatigue crack growth prognosis is a fundamental task for ensuring structural integrity. Recently, attentions have been gradually paid to methods that combine on-line measurements of structural health monitoring (SHM) with the particle filter based prognostics. However, most studies used the basic particle filter algorithm, which has the intrinsic particle impoverishment problem. There are different kinds of improvements made to the basic particle filter in the field of particle filtering. Few studies have been carried out to discuss their applicability to on-line fatigue crack growth prognosis combining with the SHM technique. Therefore, this paper compares four different improved particle filter algorithms for the on-line application of fatigue crack growth prognosis by integrating the guide wave based SHM. Studies are carried out on the basis of fatigue tests performed on the attachment lug, which is a key structural element in engineering structures. Two cases are involved that the SHM measurement has relatively large or small errors, which are influenced by whether the measurement equation is accurately trained with historical data. The prognostic accuracy of these improved particle filters is discussed. Moreover, effects of the particle number on the performance and computational cost are analyzed.

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.

In this paper, the problem of attitude estimation of a quad-copter system equipped with a multi-rate camera and gyroscope sensors is addressed through extension of a sampling importance re-sampling (SIR) particle filter (PF). Attitude measurement sensors, such as cameras, usually suffer from a slow sampling rate and processing time delay compared to inertial sensors, such as gyroscopes. A discretized attitude kinematics in Euler angles is employed where the gyroscope noisy measurements are considered the model input, leading to a stochastic uncertain system model. Then, a multi-rate delayed PF is proposed so that when no camera measurement is available, the sampling part is performed only. In this case, the delayed camera measurements are used for weight computation and re-sampling. Finally, the efficiency of the proposed method is demonstrated through both numerical simulation and experimental work on the DJI Tello quad-copter system. The images captured by the camera are processed using the ORB feature extraction method and the homography method in Python-OpenCV, which is used to calculate the rotation matrix from the Tello’s image frames.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Particle filtering is a standard method for parameter estimation in passive location and has great application value in nonlinear and non-Gaussian systems. The standard particle filter (PF) is prone to the problem of particle weight degradation and particle dilution as the number of iterations increases, which affects the overall performance of the location algorithm. Aiming at this problem, a PF algorithm based on differential squirrel search algorithm (DSSA) optimization is proposed. The particles are divided into optimal individuals, sub-optimal individuals, and ordinary individuals according to the weight of the particles. The low-weight particles are moved closer to the position of the high-weight particles by simulating the predation behavior of squirrels, so that the location information of most particles can be retained. And seasonal monitoring condition is used to avoid the algorithm falling into local optimal. The simulation results show that the improved algorithm has a lower root mean square error (RMSE) than the standard PF algorithm and the improved PF algorithms of other intelligent optimization algorithms under non-Gaussian noise. The improved algorithm can accurately achieve the passive location of the moving target.

To deal with the problem of ballistic reentry target trajectory tracking, a novel particle filter algorithm is proposed to mitigate the particle degeneracy. It can eliminate the inefficient particles by judging the degree of particle degradation at each time and generates new particles based on the estimation state values and their covariance according to the degeneracy ratio of particles. Compared with Standard Particle Filter (SPF) algorithm and Unscented Particle Filter (UPF) algorithm, simulation results show that the proposed algorithm can achieve better tracking performance with lower computational burden for the trajectory reentry target tracking model. Especially in the case of ballistic target maneuver, the performance stability of the proposed algorithm is even more significant.

Accurate prediction of flood water level is a difficult task to achieve due to the nonlinearity of the water level itself and lacking of input parameters to the neural network model. Although Artificial Neural Network is proven to be the best model of flood water level prediction, suitable model parameters need to be chosen for training purposes in order to arrive to an optimal model with smallest error. A new Back Propagation Neural Network model (BPN) for the prediction of flood water level 3 hours ahead of time is developed in this study. This optimized BPN model offers advantages of parameter analysis method instead of trial and error method for choosing the optimized BPN model parameters. However, the simulated results of BPN model required improvement as the model could not able to track the actual water level precisely. Hence, this paper proposes BPN model with integration of EKF at the output. Performance indices result such as Akaike's Final Prediction Error(FPE), Loss Function(V) and Root Mean Square Error (RMSE) from this hybrid model outperform the BPN model result.

Flood is a most dangerous natural disaster that can cause enormous threat to human life and property. Thus, an accurate flood water level prediction is very prominent prior to develop a reliable flood water level prediction. Despite the widespread use of nonlinear model for flood water level prediction, linear model is still a new model among researchers around the world. Therefore, this paper present MISO linear system identification model namely Autoregressive Exogenous Input (ARX). The river branch treated in this study was Kelang river, located at Petaling bridge that originated from three upstream rivers which were Kelang river at Sulaiman bridge, Kelang river at Tun Perak bridge and Gombak river at Jalan Parlimen. The result obtained was not promising enough and there still rooms for improvement. This is due to the dynamics of flood water level itself is characterized as highly nonlinear. Thus, flood water level prediction using nonlinear model, Nonlinear Autoregressive Model with Exogenous Input (NARX) was proposed and results showed significant improvement.

PurposeThe purpose of this paper is to focus on applications related to the hyperthermia treatment of cancer, with heating imposed either by a laser in the near-infrared range or by radiofrequency waves. The particle filter algorithms are compared in terms of computational time and solution accuracy.Design/methodology/approachThe authors extend the analyses performed in their previous works to compare three different algorithms of the particle filter, as applied to the hyperthermia treatment of cancer. The particle filters examined here are the sampling importance resampling (SIR) algorithm, the auxiliary sampling importance resampling (ASIR) algorithm and Liu & West’s algorithm.FindingsLiu & West’s algorithm resulted in the largest computational times. On the other hand, this filter was shown to be capable of dealing with very large uncertainties. In fact, besides the uncertainties in the model parameters, Gaussian noises, similar to those used for the SIR and ASIR filters, were added to the evolution models for the application of Liu & West’s filter. For the three filters, the estimated temperatures were in excellent agreement with the exact ones.Practical implicationsThis work may help medical doctors in the future to prescribe treatment protocols and also opens the possibility of devising control strategies for the hyperthermia treatment of cancer.Originality/valueThe natural solution to couple the uncertain results from numerical simulations with the measurements that contain uncertainties, aiming at the better prediction of the temperature field of the tissues inside the body, is to formulate the problem in terms of state estimation, as performed in this work.

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

Improved particle filter based on fine resampling algorithm

Detection and Tracking of Moving Objects for Automotive Driver Assistance System