High Likelihood Region Research Articles

Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking

As a commonly used filtering method for nonlinear non-Gaussian systems, particle filters (PFs) have been successfully applied in the field of maneuvering target tracking. However, particle impoverishment is a major obstacle to the PF performance. To overcome this defect, this paper combines the hummingbirds optimization algorithm (HOA) with a standard PF and proposes an HOA-based PF (HOA-PF) for maneuvering target tracking. The proposed filter treats the particles as individual hummingbirds, simulates the honey-collecting process of hummingbirds in nature and moves the particles as a whole to the high-likelihood region by performing self-searching and guided-searching phases. Moreover, to enhance the particle diversity, the mutation method of the following birds in the HOA is improved. Thus, the distribution of particles in the HOA-PF is reasonable. The results of experiments on the univariate nonstationary growth model and the maneuvering target tracking problem demonstrate the effectiveness of the proposed method.

Target tracking based on improved square root cubature particle filter via underwater wireless sensor networks

Target tracking in underwater wireless sensor networks (UWSNs) has two fundamental issues that tracking accuracy and energy consumption. Although the application of particle filter in target tracking is considered in recent years, degeneracy phenomenon and particle impoverishment always restrict its capacity and application. This paper proposes an improved square root cubature particle filter (ISRCPF) to improve tracking accuracy. The authors employ self-adaptive artificial fish swarm algorithm (AFSA) to optimise the particles, which makes the particles move towards to high likelihood region and maintains the diversity of the particles. Moreover, a sensor selection scheme is provided, which reduces energy consumption of the network by exactly waking up four sensor nodes at each time, while preserving tracking accuracy. Additionally, the authors propose a novel fusion method called similarity fusion method (SFM) to fuse local estimates together and then obtain a better result for distributed fusion architecture (DFA). The simulation results demonstrate that the proposed methods have superior performance.

Are extreme dissipation events predictable in turbulent fluid flows?

We derive precursors of extreme dissipation events in a turbulent channel flow. Using a recently developed method that combines dynamics and statistics for the underlying attractor, we extract a characteristic state that precedes laminarization events that subsequently lead to extreme dissipation episodes. Our approach utilizes coarse statistical information for the turbulent attractor, in the form of second order statistics, to identify high-likelihood regions in the state space. We then search within this high probability manifold for the state that leads to the most finite-time growth of the flow kinetic energy. This state has both high probability of occurrence and leads to extreme values of dissipation. We use the alignment between a given turbulent state and this critical state as a precursor for extreme events and demonstrate its favorable properties for prediction of extreme dissipation events. Finally, we analyze the physical relevance of the derived precursor and show its robust character for different Reynolds numbers. Overall, we find that our choice of precursor works well at the Reynolds number it is computed at and at higher Reynolds number flows with similar extreme events.

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood of occurrence of a particular event. To identify hot spots, location data for those events is required, which is typically collected by telematics devices. These sensors are constantly gathering information, generating very large volumes of data. Current state-of-the-art solutions are capable of identifying hot spots from big static batches of data by means of variations of clustering or instance selection techniques that pre-process the original input data, providing the most relevant locations. However, these approaches neglect to address changes in hot spots over time. This paper presents a dynamic bio-inspired approach to detect hot spots in big data streams. This computational intelligence method is designed and applied to the transportation sector as a case study to identify incidents in the roads caused by heavy goods vehicles. We adapt an immune-based algorithm to account for the temporary aspect of hot spots inspired by the idea of pheromones, which is then subsequently implemented using Apache Spark Streaming. Experimental results on real datasets with up to 4.5 million data points—provided by a telematics company—show that the algorithm is capable of quickly processing large streaming batches of data, as well as successfully adapting over time to detect hot spots. The outcome of this method is twofold, both reducing data storage requirements and demonstrating resilience to sudden changes in the input data (concept drift).

Target tracking based on improved cubature particle filter in UWSNs

In this study, an improved cubature particle filter based on the artificial bee colony (ABC) algorithm is proposed and applied to target tracking via underwater wireless sensor networks (UWSNs). In the proposed method, the square root cubature Kalman filter is used to generate the proposal distribution and the ABC algorithm is employed to optimise the particles before resampling, which makes the particles move toward the high likelihood region and maintain the diversity of the particles. Moreover, linear minimum variance criterion is utilised to fuse local estimates together in distributed fusion architectures of UWSNs. The simulation results show that the proposed method outperforms other classical algorithms in tracking accuracy.

Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest

The Lyman-alpha forest provides strong constraints on both cosmological parameters and intergalactic medium astrophysics, which are forecast to improve further with the next generation of surveys including eBOSS and DESI. As is generic in cosmological inference, extracting this information requires a likelihood to be computed throughout a high-dimensional parameter space. Evaluating the likelihood requires a robust and accurate mapping between the parameters and observables, in this case the 1D flux power spectrum. Cosmological simulations enable such a mapping, but due to computational time constraints can only be evaluated at a handful of sample points; “emulators” are designed to interpolate between these. The problem then reduces to placing the sample points such that an accurate mapping is obtained while minimising the number of expensive simulations required. To address this, we introduce an emulation procedure that employs Bayesian optimisation of the training set for a Gaussian process interpolation scheme. Starting with a Latin hypercube sampling (other schemes with good space-filling properties can be used), we iteratively augment the training set with extra simulations at new parameter positions which balance the need to reduce interpolation error while focussing on regions of high likelihood. We show that smaller emulator error from the Bayesian optimisation propagates to smaller widths on the posterior distribution. Even with fewer simulations than a Latin hypercube, Bayesian optimisation shrinks the 95% credible volume by 90% and, e.g., the 1σ error on the amplitude of small-scale primordial fluctuations by 38%. This is the first demonstration of Bayesian optimisation applied to large-scale structure emulation, and we anticipate the technique will generalise to many other probes such as galaxy clustering, weak lensing and 21cm.

Particle Filter and Its Application in the Integrated Train Speed Measurement

Particle filter (PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter (KF) and those improved KFs such as extended KF (EKF) and unscented KF (UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF (BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature (being stuck into local optimum) and increase the diversity of particles, Levy flight is designed to increase the randomness of particle’s movement. Finally, the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.

An Improved Particle Filter With a Novel Hybrid Proposal Distribution for Quantitative Analysis of Gold Immunochromatographic Strips

In this paper, a novel statistical pattern recognition method is proposed for accurately segmenting test and control lines from the gold immunochromatographic strip (GICS) images for the benefits of quantitative analysis. A new dynamic state-space model is established, based on which the segmentation task of test and control lines is transformed into a state estimation problem. Especially, the transition equation is utilized to describe the relationship between contour points on the upper and the lower boundaries of test and control lines, and a new observation equation is developed by combining the contrast of between-class variance and the uniformity measure. Then, an innovative particle filter (PF) with a hybrid proposal distribution, namely, deep-belief-network-based particle filter (DBN-PF) is put forward, where the deep belief network (DBN) provides an initial recognition result in the hybrid proposal distribution, and the particle swarm optimization algorithm moves particles to regions of high likelihood. The performance of proposed DBN-PF method is comprehensively evaluated on not only an artificial dataset but also the GICS images in terms of several indices as compared to the PF and DBN methods. It is demonstrated via experiment results that the proposed approach is effective in quantitative analysis of GICS.

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Network and Bat-Based Particle Filter

Predicting the remaining useful life (RUL) is an effective way to indicate the health of lithium-ion batteries, which can help to improve the reliability and safety of battery-powered systems. To predict the RUL, the line of research focuses on using the empirical degradation model followed by the particle filter (PF) algorithm, which is used for online updating the model’s parameters. However, this works well for specific batteries under specific discharge conditions. When the degradation trends cannot be presented by the chosen empirical model or the standard PF encounters impoverishment and degeneracy problem, the RUL prediction would be inaccurate. To improve the RUL prediction accuracy, we propose a novel approach by enhancing the existing method from two aspects. First, we introduce a neural network (NN) to model battery degradation trends under various operation conditions. As NN’s generalization and nonlinear representing ability, it outperforms the typical empirical degradation model. Second, the NN model’s parameters are recursively updated by the bat-based particle filter. The bat algorithm is used to move the particles to the high likelihood regions, which optimizes the particle distribution and thus reduces the degeneracy and impoverishment of PF. In this paper, quantitative evaluation is presented using two datasets with different batteries under different aging conditions. The results indicate that the proposed the approach can achieve higher RUL prediction accuracy than conventional empirical model and standard PF.

Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning.

This paper presents a new Gaussian Processes (GPs)-based particle filter tracking framework. The framework non-trivially extends Gaussian process regression (GPR) to transfer learning, and, following the tracking-by-fusion strategy, integrates closely two tracking components, namely a GPs component and a CFs one. First, the GPs component analyzes and models the probability distribution of the object appearance by exploiting GPs. It categorizes the labeled samples into auxiliary and target ones, and explores unlabeled samples in transfer learning. The GPs component thus captures rich appearance information over object samples across time. On the other hand, to sample an initial particle set in regions of high likelihood through the direct simulation method in particle filtering, the powerful yet efficient correlation filters (CFs) are integrated, leading to the CFs component. In fact, the CFs component not only boosts the sampling quality, but also benefits from the GPs component, which provides re-weighted knowledge as latent variables for determining the impact of each correlation filter template from the auxiliary samples. In this way, the transfer learning based fusion enables effective interactions between the two components. Superior performance on four object tracking benchmarks (OTB-2015, Temple-Color, and VOT2015/2016), and in comparison with baselines and recent state-of-the-art trackers, has demonstrated clearly the effectiveness of the proposed framework.

Line-Constrained Shape Feature for Building Change Detection in VHR Remote Sensing Imagery

Buildings represent the most relevant features of human activity in urban regions, but their change detection using very-high-resolution (VHR) remote sensing imagery is still a major challenge. Effective representation of the building is the key point in building change detection. The linear feature can indirectly represent the structure and distribution of man-made objects. Thus, this study proposes a shape feature-based building change detection method. Specifically, a line-constrained shape (LCS) feature is developed to capture the shape characteristics of buildings. This feature improves the discriminability between buildings and other ground objects by integrating the pixel shape feature and line segments. The building candidate area (BCA) is created in accordance with the distribution of the line segments in two-phase images. The problem space is constrained in a high-likelihood region of buildings because of the BCA. Comparative experimental results demonstrate that the combination of the spectral feature and the developed LCS feature achieves the best performance in object-based building change detection in VHR imagery.

Infrared small target detection and tracking algorithm based on new closed-loop control particle filter

The problem of particle impoverishment could be always found in standard particle filter, additionally a large number of particles are required for accurate estimation. as it is difficult to meet the demand of modern infrared search and tracking system. To solve this problem, an improved infrared small target detection and tracking method based on closed-loop control bat algorithm optimized particle filter is proposed. Firstly, bat algorithm is introduced into the particle filtering in this method. Particles are used to simulate the process that an individual bat hunts and avoids obstacles so that particles move towards the high-likelihood region. Meanwhile, the improved algorithm takes the proportion of particles accepting a new state as the feedback quantity and proposes to conduct dynamic control on global and local search ability of particle filtering by closed-loop control strategy, which further improves the overall quality of particle distribution. The performance of the improved detection and tracking algorithm is tested in simulation scene and real scene of infrared small target. Experimental results show that the improved algorithm improves the performance of the infrared searching and tracking system.

Object tracking with improved firefly algorithm

Resampling particle filter is easy to lead to particle impoverishment, and requires a large number of particles for state estimation. The standard firefly algorithm loses the tracking target easily when optimising particle filter. Therefore, this paper proposes target tracking with improved firefly algorithm. The proposed method utilises the movement trend of the target, combines the learning principles of the firefly and the movement characteristics of the tracking target, and designs a new position updating formula of the firefly. In the proposed method, the particles gradually move to the high likelihood region through iterative optimisation. Thus the estimated target state is closer to the true value, and the accuracy of targets is improved. The experiment results show that the tracking performance and the stability of the improved firefly algorithm are better than that of the standard firefly algorithm, and the particle number has certain influences on the experimental results.

Global fits of GUT-scale SUSY models with GAMBIT

We present the most comprehensive global fits to date of three supersymmetric models motivated by grand unification: the constrained minimal supersymmetric standard model (CMSSM), and its Non-Universal Higgs Mass generalisations NUHM1 and NUHM2. We include likelihoods from a number of direct and indirect dark matter searches, a large collection of electroweak precision and flavour observables, direct searches for supersymmetry at LEP and Runs I and II of the LHC, and constraints from Higgs observables. Our analysis improves on existing results not only in terms of the number of included observables, but also in the level of detail with which we treat them, our sampling techniques for scanning the parameter space, and our treatment of nuisance parameters. We show that stau co-annihilation is now ruled out in the CMSSM at more than 95% confidence. Stop co-annihilation turns out to be one of the most promising mechanisms for achieving an appropriate relic density of dark matter in all three models, whilst avoiding all other constraints. We find high-likelihood regions of parameter space featuring light stops and charginos, making them potentially detectable in the near future at the LHC. We also show that tonne-scale direct detection will play a largely complementary role, probing large parts of the remaining viable parameter space, including essentially all models with multi-TeV neutralinos.

A Study on Particle Filter based on KLD-Resampling for Wireless Patient Tracking

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system’s data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

A Study on Particle Filter based on KLD-Resampling for Wireless Patient Tracking

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

Performance of Sampling/Resampling-based Particle Filters Applied to Non-Linear Problems

In this work, we propose a wireless body area sensor network (WBASN) to monitor patient position. Localization and tracking are enhanced by improving the effect of the received signal strength (RSS) variation. First, we propose a modified particle filter (PF) that adjusts resampling parameters for the Kullback-Leibler distance (KLD)-resampling algorithm to ameliorate the effect of RSS variation by generating a sample set near the high-likelihood region. The key issue of this method is to use a resampling parameter lower bound for reducing both the root mean square error (RMSE) and the mean number of particles used. To determine this lower bound, an optimal algorithm is proposed based on the maximum RMSE between the proposed algorithm and the KLD-resampling algorithm or based on the maximum mean number of particles used of these algorithms. Finally, PFs based on KLD-sampling and KLD-resampling are proposed to minimize the efficient number of particles and to reduce the estimation error compared to traditional algorithms.

A modified variable rate particle filter for maneuvering target tracking

To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

Improving the efficiency of Monte Carlo Bayesian calibration of hydrologic models via model pre-emption

Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling and sequential Monte Carlo (SMC) sampling are popular methods for uncertainty analysis in hydrological modelling. However, application of these methodologies can incur significant computational costs. This study investigated using model pre-emption for improving the computational efficiency of MCMC and SMC samplers in the context of hydrological modelling. The proposed pre-emption strategy facilitates early termination of low-likelihood simulations and results in reduction of unnecessary simulation time steps. The proposed approach is incorporated into two samplers and applied to the calibration of three rainfall–runoff models. Results show that overall pre-emption savings range from 5 to 21%. Furthermore, results indicate that pre-emption savings are greatest during the pre-convergence ‘burn-in’ period (i.e., between 8 and 39%) and decrease as the algorithms converge towards high likelihood regions of parameter space. The observed savings are achieved with absolutely no change in the posterior set of parameters.

An Improved Particle Filtering Algorithm Based on Mean Shift Algorithm for Infrared Target Tracking

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles