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Tropical Cyclones (TCs) are highly destructive weather phenomena that can cause significant social and economic damage. With the development of meteorological monitoring technology and the updating of database, accurately forecasting the track of TC movement is one of the effective ways to minimize losses. However, traditional movement track forecasting methods suffer the disadvantages of low efficiency and low accuracy. To address the these problems, a novel Convolutional Neural Network-Temporal Convolutional Network (CNN-TCN) model based on Multidimensional Features and Time Difference Series (MT-CNN-TCN) is presented in this paper. First, different types of meteorological data are processed and then the feature differences between adjoining moments are extracted. Second, a two-branch structure based on Two Dimensional Convolutional Neural Network (2DCNN), 3DCNN and TCN is taken to effectively integrate different types of meteorological features to strengthen its forecasting effect. Finally, experiments are conducted using Northwest Pacific TC data from years 2000–2019. Test results show that the proposed model MT-CNN-TCN can perform well at all three forecast periods (12 h, 24 h, and 48 h), with a significant improvement in accuracy by 7 %, 13 %, and 16 % respectively, compared with current forecasting methods such as Long Short Term Memory (LSTM).

In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicle (AUV) works autonomously in complex marine environments. After a severe accident, an AUV will lose its power and rely on its small buoyancy to ascend at a slow speed. If the reserved buoyancy is insufficient, when reaching the thermocline, the buoyancy will rapidly decrease to zero. Consequently, the AUV will experience prolonged lateral drift within the thermocline. This study focuses on developing a prediction method for the drift trajectory of an AUV after a long-term power loss accident. The aim is to forecast the potential resurfacing location, providing technical support for surface search and salvage operations of the disabled AUV. To the best of our knowledge, currently, there is no mature and effective method for predicting long-term AUV underwater drift trajectories. In response to this issue, based on real AUV catastrophes, this paper studies the prediction of long-term AUV underwater drift trajectories in the cases of power loss. We propose a three-dimensional trajectory prediction method based on the Lagrange tracking approach. This method takes the AUV's longitudinal velocity, the time taken to reach different depths, and ocean current data at various depths into account. The reason for the AUV's failure to ascend to sea surface lies that the remaining buoyancy is too small to overcome the thermocline. As a result, AUV drifts long time within the thermocline. To address this issue, a method for estimating thermocline currents is proposed, which can be used to predict the lateral drift trajectory of the AUV within the thermocline. Simulation is conducted to compare the results obtained by the proposed method and that in a real accident. The results demonstrate that the proposed approach exhibits small directional and positional errors. This validates the effectiveness of the proposed method.

In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs.

Tropical cyclone (TC) track forecasting is critical for wind risk assessment. This work proposes a novel probabilistic TC track forecasting model based on mixture density network (MDN) and multitask learning (MTL). The existing NN-based probabilistic TC track prediction models focus on directly modeling the distribution of the future TC positions. Multitask learning has been shown to boost the performance of single tasks when the tasks are relevant. This work divides the probabilistic track prediction task into two sub-tasks: a deterministic prediction of the future TC position and a probabilistic prediction of the residual between the deterministic prediction and the actual TC location. The MDN is employed to realize the probabilistic prediction task. Since the target values of the MDN in this work are the residuals, which depend on the prediction result of the deterministic task, a novel training method is developed to train the MTL model properly. The proposed model is tested against statistical and other learning-based models on historical TC data. The results show that the proposed model outperforms other models in making probabilistic predictions. This approach advances TC track forecasting by integrating MDN and MTL, showing promise in enhancing probabilistic predictions and improving disaster preparedness.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

According to the actual situation of gun-launched UAV intercepting "Low-slow-small" target and the specific maneuverability of gun-launched UAV, an enhanced real proportion guidance law (RTPN) guidance interception method is designed. The traditional RTPN method does not consider the saturation overload limit and the capture region of arbitrary maneuvering target. In addition, aiming at the measurement error and the dynamic response delay of the gun-launched UAV during the interception, the EKF data fusion track prediction algorithm is proposed. Simulation results show that the proposed method can effectively solve the problem.

In this paper, a career track recommender system was proposed using Deep Neural Network model. This study aims to assist guidance counselors in guiding their students in the selection of a suitable career track. It is because a lot of Junior High school students experienced track uncertainty and there are instances of shifting to another program after learning they are not suited for the chosen track or course in college. In dealing with the selection of the best student attributes that will help in the creation of the predictive model, the feature engineering technique is used to remove the irrelevant features that can affect the performance of the DNN model. The study covers 1500 students from the first to the third batch of the K-12 curriculum, and their grades from 11 subjects, sex, age, number of siblings, parent’s income, and academic strand were used as attributes to predict their academic strand in Senior High School. The efficiency and accuracy of the algorithm depend upon the correctness and quality of the collected student’s data. The result of the study shows that the DNN algorithm performs reasonably well in predicting the academic strand of students with a predic-tion accuracy of 83.11%. Also, the work of guidance counselors became more efficient in handling students’ concerns just by using the proposed system. It is concluded that the recommender system serves as a decision tool for counselors in guiding their stu-dents to determine which Senior High School track is suitable for students with the utilization of the DNN model.

ObjectivesIn order to meet the needs of long-distance sea target recognition and tracking through the navigation radar and integrated optoelectronic equipment of an unmanned surface vehicle (USV) operating in a marine environment, a novel USV autonomous sensing system is developed with heterogeneous sensor association, target track prediction and photoelectric camera attitude compensation.MethodsBy using the target track prediction algorithm based on a Kalman filter for the target position information output by the navigation radar, target positioning accuracy can be improved and real-time target information can be provided to the photoelectric camera. The posture compensation algorithm based on the ship's posture in the photoelectric camera is used to complete the tasks of target image collection, recognition and tracking. An USV equipped with our proposed sensing system has completed dynamic target recognition and tracking tasks under Sea State 3 conditions.ResultsThe target tracking error is reduced by 6% and the target recognition success rate is increased to 96.25%, which verifies the environmental adaptability of this sensing system. ConclusionsThrough testing experiments, the proposed recognition and tracking system can effectively solve the problems of difficult image acquisition and poor recognition effects of sea surface targets, effectively improving the success rate of target recognition.

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic control centers, according to ship position monitoring, ship position prediction and early warning, can effectively avoid collisions. However, the prediction accuracy and computational efficiency of the ship's future position are the key problems to be solved. In this paper, a path prediction model (GA–ACO–BP) combining a genetic algorithm, an ant colony algorithm and a BP neural network is proposed. The model is first used to perform deep pretreatment of raw AIS data, with the main body of the BP neural network as a prediction model, focused on the complementarity between genetic and ant colony algorithms, to determine the ant colony initialization pheromone concentration by the genetic algorithm, design the hybrid genetic–ant colony algorithm, and optimize this to the optimal weight and threshold of the BP neural network, in order to improve the convergence speed and effect of the traditional BP neural network. The test results show that the model greatly improves the fitness of track prediction, with higher accuracy and within a shorter time, and has a certain real-time and extensibility for track prediction of different river segments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although tropical cyclone (TC) track forecast errors (TFEs) of operational warning centres have substantially decreased in recent decades, there are still many cases with large TFEs. The International Grand Global Ensemble (TIGGE) data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction (NWP) models. Forty-four TCs in the western North Pacific during the period 2007–2014 with TFEs (+24 to +120 h) larger than the 75th percentile of the annual error distribution (with a total of 93 cases) are identified.Four categories of situations are found to be associated with large TFEs. These include the interaction of the outer structure of the TC with tropical weather systems, the intensity of the TC, the extension of the subtropical high (SH) and the interaction with the westerly trough. The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions, the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE. The performance of different models in different situations is discussed.

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51-331 km) is lower than 2020 (82-359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76-332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin. [ABSTRACT FROM AUTHOR]

The detection and alarm of air threat situation is the key to the early warning for UAV flight collision avoidance. For this reason, a detection and alarm method of air threat situation based on track prediction is proposed for UAV collision avoidance. In the method, firstly, the sliding window polynomial fitting method is used to dynamically predict the track of the intruder. Then, on the basis of the track prediction, using the flight information of UAV and intruder, the dynamic collision avoidance zone of UAV is established to the intruder. Finally, combined with static protection zone principle, the detection and alarm for flight conflict trend, near midair collision trend and flight collision trend between UAV and intruder is carried out in a specific flight scenario. Simulation results validate the effectiveness of the proposed method and the feasibility of application in the early warning for UAV flight collision avoidance.

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic control centers, according to ship position monitoring, ship position prediction and early warning, can effectively avoid collisions. However, the prediction accuracy and computational efficiency of the ship’s future position are the key problems to be solved. In this paper, a path prediction model (GA–ACO–BP) combining a genetic algorithm, an ant colony algorithm and a BP neural network is proposed. The model is first used to perform deep pretreatment of raw AIS data, with the main body of the BP neural network as a prediction model, focused on the complementarity between genetic and ant colony algorithms, to determine the ant colony initialization pheromone concentration by the genetic algorithm, design the hybrid genetic–ant colony algorithm, and optimize this to the optimal weight and threshold of the BP neural network, in order to improve the convergence speed and effect of the traditional BP neural network. The test results show that the model greatly improves the fitness of track prediction, with higher accuracy and within a shorter time, and has a certain real-time and extensibility for track prediction of different river segments.

Accurate track prediction is the basis for improving the early warning capability for air threat situation of anti-collision, for this reason, an improved sliding window polynomial fitting track prediction method is proposed to intrusion aircraft. Two improvements are mainly made in the method: one is to construct a suitable polynomial fitting equation for each predicted value in the sliding window for the prediction of several future values after the current values; the other is to adjust and fit the polynomial order and sliding window length adaptively according to the current track value and the target motion mode information reflected by previous finite continuous track value. Simulation result shows that, compared with traditional sliding window polynomial fitting method, the proposed method has better track prediction accuracy. It can improve the track prediction accuracy of non-cooperative aircraft to some extent, and validate its feasibility and effectiveness in track prediction.

Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically adjust the emission energy because the dynamic UAV coordinates cannot be accurately acquired. In addition, the fixed emission energy makes the EDs have poor endurance. To address this challenge, in this paper, we propose a deep learning-based energy optimization algorithm (DEO) to dynamically adjust the emission energy of the ED so that the received energy of the mobile relay UAV is, as much as possible, equal to the sensitivity of the receiver. Specifically, the edge server provides the computing platform and uses deep learning (DL) to predict the location information of the relay UAV in dynamic scenarios. Then, the ED emission energy is adjusted according to the predicted position. It enables the ED to communicate reliably with the mobile relay UAV at minimum energy. We analyze the performance of a variety of predictive networks under different time-delay systems through experiments. The results show that the Weighted Mean Absolute Percentage Error (WMAPE) of this algorithm is 0.54%, 0.80% and 1.15% under the effect of a communication delay of 0.4 s, 0.6 s and 0.8 s, respectively.

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result, the target cannot be detected and tracked for a period of time under the influence of strong clutter, which causes broken trajectories. To solve this problem, we propose an HF radar-vessel trajectory-prediction method based on a multi-scale convolutional neural network (MSCNN) that combines a gated recurrent unit and attention mechanism (GRU-AM) and a fusion with an autoregressive (AR) model. The vessel’s latitude and longitude information obtained by the HF radar is sent into the convolutional neural network (CNN) with different window lengths in parallel, and feature fusion is performed on the extracted multi-scale features. The deep GRU model is built to learn the time series with the GRU structure to preserve historical information. Different weights are given to the features using the temporal attention mechanism (AM), which helps the network learn the key information. The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. To make full use of the HF radar tracking information, the broken trajectory prediction is carried out by forward and backward computation using data from before and after the fracture, respectively. Weights are then assigned to the two predicted results by the entropy-value method to obtain the final ship trajectory by weighted summation. Field experiments show that the proposed method can accurately forecast the trajectories of vessels concealed in clutter. In comparison with other mainstream methods, the new method performs better in estimation accuracy for HF radar vessels concealed in clutter.

Autonomous Underwater Vehicle (AUV) works autonomously in complex marine environments. After a severe accident, an AUV will lose its power and rely on its small buoyancy to ascend at a slow speed. If the reserved buoyancy is insufficient, when reaching the thermocline, the buoyancy will rapidly decrease to zero. Consequently, the AUV will experience prolonged lateral drift within the thermocline. This study focuses on developing a prediction method for the drift trajectory of an AUV after a long-term power loss accident. The aim is to forecast the potential resurfacing location, providing technical support for surface search and salvage operations of the disabled AUV. To the best of our knowledge, currently, there is no mature and effective method for predicting long-term AUV underwater drift trajectories. In response to this issue, based on real AUV catastrophes, this paper studies the prediction of long-term AUV underwater drift trajectories in the cases of power loss. We propose a three-dimensional trajectory prediction method based on the Lagrange tracking approach. This method takes the AUV's longitudinal velocity, the time taken to reach different depths, and ocean current data at various depths into account. The reason for the AUV's failure to ascend to sea surface lies that the remaining buoyancy is too small to overcome the thermocline. As a result, AUV drifts long time within the thermocline. To address this issue, a method for estimating thermocline currents is proposed, which can be used to predict the lateral drift trajectory of the AUV within the thermocline. Simulation is conducted to compare the results obtained by the proposed method and that in a real accident. The results demonstrate that the proposed approach exhibits small directional and positional errors. This validates the effectiveness of the proposed method.

Two grey residual modification models are proposed for the track prediction precision of brainpower antitank (BAT) submunition based on grey theory, which are grey residual modification model (GRMM) and grey Verhulst residual modification model (GVRMM). A grey model of target track prediction is established, and the limitations of this model are analyzed. GRMM and GVRMM are used to correct the grey forecast model on line, respectively. It shows that the proposed method based on realtime residual modification mechanism can be used to reduce the prediction error effectively, and GVRMM has better efficiency in improving track prediction precision. [ABSTRACT FROM AUTHOR]

In this paper, a realistic true proportional navigation (RTPN) law–guided interception method was designed based on anti-packet loss collaborative filtering track prediction, which solves the problems that traditional RTPN method not considers the interceptor saturation overload limit and the determination of the acquisition area of any maneuvering target. First, a distributed cooperative filtering algorithm is formulated to improve accuracy and robustness of data fusion caused by the measurement error of target trajectory and the packet loss of cooperative detection data in the process of interception. Then, a track prediction algorithm is proposed to improve the data transmission and the dynamic response delay of the interceptor itself. Finally, the simulation results are performed to illustrate the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

Specific to deficiency existing in predicting strong maneuvering target of Unmanned Aerial Vehicle, an improved algorithm of target tracking algorithm is proposed. The algorithm uses the filtering residuals to adjust the adjustment coefficients adaptively and an improved fuzzy membership function is proposed. Fuzzy self-adaptive filtering algorithm of statistical model and improved fuzzy self-adaptive filtering algorithm of statistical model are finally used to conduct simulation experiment of track prediction with maneuvering target. The results of both simulation experiments are analyzed and compared. The result of simulation experiments shows that fuzzy self-adaptive filtering algorithm of statistical model keeps track prediction performance for the general maneuvering target of improved fuzzy self-adaptive filtering algorithm of statistical model and also improves the track prediction effect for strong maneuvering target. [ABSTRACT FROM AUTHOR]

To solve FACSAF algorithm’s problem, this paper proposed a modified algorithm called MFACSAF. It presented a modified fuzzy membership function which used the filtering residual to adjust the adjustment coefficient adaptively. At last, it did a simulation experiment which used MFACSAF algorithm and FACSAF algorithm to predict the track of maneuvering target, and compared their results. The results of simulation experiment show that, MFACSAF algorithm has the same performance as FACSAF algorithm when it predicts general maneuvering targets, and has a better performance than FACSAF algorithm when it predicts strong maneuvering targets. [ABSTRACT FROM AUTHOR]

In the present study, a new approach is discussed to find out the residual steering flow from the high-resolution global Numerical Weather Prediction (NWP) model-forecasted wind fields, which have been used in the Lagrangian advection model to determine the track of tropical cyclones formed in the Indian Ocean. The Lagrangian advection model is newly developed model and conceptually closer to the dynamical models, which utilizes environmental steering flow and the effect due to earth's rotation (the beta-effect) to determine the motion of cyclone. In this approach, the effect of environmental flow on the cyclone track is examined by removing the existing cyclone vortex from the steering flow which is determined by potential vorticity approach. A new approach based on vortex pattern matching has been used to identify the cyclone vortex and to remove it from the steering flow. The tracks of five tropical cyclones (viz., Nargis, Khai_Muk, Nisha, Aila and Phyan) which were formed in the North Indian basin during the period 2008-2009 have been generated by the Lagrangian advection model using the proposed scheme. The position errors were computed with respect to the Joint Typhoon Warning Center (JTWC) best track analysis positions and compared with that of without-vortex-removal scheme. The results show that the mean track errors for five cyclones are reduced by 6-35 % for 12-72 h forecast in case of vortex-removal scheme as compared to the without-vortex-removal scheme. [ABSTRACT FROM AUTHOR]

A multimodel ensemble (MME) technique for predicting track of tropical cyclones over the North Indian Sea has been proposed. The technique is developed applying multiple linear regression procedure. Parameters of the ensemble technique are determined from the forecast datasets on the tracks of tropical cyclones over the North Indian Sea during the year 2008-2009. The parameters selected as predictors are: forecast latitude and longitude positions at 12-hour interval up to 72-hours forecast of five operational numerical weather prediction models. The dynamical models included for development of the ensemble technique are: (i) forecasts from the European Centre for Medium- Range Weather Forecasts (ECMWF), (ii) the National Centers for Environmental Prediction Global Forecast System (NCEP), (iii) the MM5 model, (iv) the Quasi-Lagrangian model (QLM) and (v) the model of Japan Meteorological Agency (JMA). A collective bias correction is included in the ensemble technique in which a multiple linear regression based minimization principle for the model forecast position against to the observed position is applied. These bias factors are described by separate weights at every 12-hours interval up to the 72-hour forecasts for each of the member model. When the technique is tested with the independent samples, forecast skill of the MME technique is found to be reasonably good. The average error ranges from of the order of 74 km to 290 km for forecasts up to 72-hour. Performance of the MME technique shows that there are skill improvements up to 30 km for the position errors over the best model at 72-hour forecast. The forecast skill of the MME technique for forecasts up to 72-hour also shows an improvement as compared to the forecasts from member models and the simple ensemble mean (ENM). [ABSTRACT FROM AUTHOR]

This paper develops an approach for probabilistic prediction of the future locations of a moving ocean acoustic source based on probability distributions for past source locations as determined by Bayesian acoustic tracking inversion. The Bayesian track estimation for past times considers both source and environmental parameters as unknown random variables constrained by noisy acoustic data and prior information, and numerically samples the posterior probability density (PPD) using Markov-chain Monte Carlo (MCMC) methods. Applying a probabilistic prediction model for constant-velocity source motion to each of the PPD samples produces source location probability distributions for future times. These prediction distributions account for both the uncertainty of the source-motion model and the uncertainty in the state of knowledge of past source locations including the effects of environmental uncertainty. Results of Bayesian track estimation and prediction are represented as a sequence of joint marginal probability distributions over source range and depth, and as the most probable track with uncertainties. Probability distribution for the time and range of the closest point of approach (CPA) are also computed for inbound tracks. The approach is illustrated with synthetic acoustic data at two noise levels and with measured data from a shallow-water site in the Mediterranean Sea. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of automatically extracting frequently used pedestrian pathways from video sequences of natural outdoor scenes. Path models are learnt from the accumulation of trajectory data over long time periods, and can be used to augment the classification of subsequent track data. In particular, labelled paths provide an efficient means for compressing the trajectory data for logging purposes. In addition, the model can be used to compute a probabilistic prediction of the pedestrian''s location many time steps ahead, and to aid the recognition of unusual behaviour identified as atypical object motion. [Copyright &y& Elsevier]

According to the actual situation of gun-launched UAV intercepting "Low-slow-small" target and the specific maneuverability of gun-launched UAV, an enhanced real proportion guidance law (RTPN) guidance interception method is designed. The traditional RTPN method does not consider the saturation overload limit and the capture region of arbitrary maneuvering target. In addition, aiming at the measurement error and the dynamic response delay of the gun-launched UAV during the interception, the EKF data fusion track prediction algorithm is proposed. Simulation results show that the proposed method can effectively solve the problem.

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result, the target cannot be detected and tracked for a period of time under the influence of strong clutter, which causes broken trajectories. To solve this problem, we propose an HF radar-vessel trajectory-prediction method based on a multi-scale convolutional neural network (MSCNN) that combines a gated recurrent unit and attention mechanism (GRU-AM) and a fusion with an autoregressive (AR) model. The vessel's latitude and longitude information obtained by the HF radar is sent into the convolutional neural network (CNN) with different window lengths in parallel, and feature fusion is performed on the extracted multi-scale features. The deep GRU model is built to learn the time series with the GRU structure to preserve historical information. Different weights are given to the features using the temporal attention mechanism (AM), which helps the network learn the key information. The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. To make full use of the HF radar tracking information, the broken trajectory prediction is carried out by forward and backward computation using data from before and after the fracture, respectively. Weights are then assigned to the two predicted results by the entropy-value method to obtain the final ship trajectory by weighted summation. Field experiments show that the proposed method can accurately forecast the trajectories of vessels concealed in clutter. In comparison with other mainstream methods, the new method performs better in estimation accuracy for HF radar vessels concealed in clutter. [ABSTRACT FROM AUTHOR]