Kinematic Prediction for Intercept Using a Neural Kalman Filter

Stubberud, Stephen C.; Kramer, Kathleen A.

doi:10.3182/20050703-6-cz-1902.01102

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…There are some classical and novel prediction methods in target tracking, such as: least square methods, Kalman filter [7], particle filter [8], and so on. The least square method is one of the simplest methods and has found successful implementations in engineering.…”

Section: Location Prediction Of the Targetmentioning

confidence: 99%

Visual Tracking in Engineering Based on Sampling Rate Conversion and Trace Forecast

Cai

Wang

et al. 2008

2008 International Conference on MultiMedia and Information Technology

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To cope with the occlusion and intersection of target, trace forecast is employed in the visual tracking. Considering the continuity of location, velocity and acceleration of the target, sampling rate conversion based on cubic spline interpolation is proposed to cope with the variance of sampling rate in the multirate system. Target trace is fitted and the location of the object in the future is predicted by subsection least square method with the latest composed locations of the target. The best number of input data to fit the trace of the target and predict the location of the target is determined by experiments and analyses of tracking performance with variant numbers of input data. Experiment results show that the proposed sampling rate conversion algorithm and trace forecasting algorithm are effective in real time visual tracking.

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Section: Location Prediction Of the Targetmentioning

confidence: 99%

Visual Tracking in Engineering Based on Sampling Rate Conversion and Trace Forecast

Cai

Wang

et al. 2008

2008 International Conference on MultiMedia and Information Technology

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“…Berker et al [15] applied a two-dimensional (2D) obstacle motion-tracking module to a dynamometer tracking algorithm to improve data quality for navigation purposes. Stubberud and Kramer [16] used a neural-extension Kalman filter to dynamically predict a target state online, thus improving the state estimation capability of existing models. Sang et al [17] built a prediction model by using change of speed (COS), rate of turn (ROT), speed over ground (SOG), and course over ground (COG) to develop the closest point of approaching (CPA) searching method.…”

Section: Introductionmentioning

confidence: 99%

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Gao

Shi

2018

Sensors

109

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The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

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“…The NEKF has been used in tracking to improve, on-line, localization of maneuvering targets [2]. More recently, the improved motion model was used to improve the prediction of the target location [3] [4] so that it could be used as a more accurate reference signal for target interceptor using predictive guidance [5]. In Figure 1, the implementation of this approach is shown.…”

Section: Introductionmentioning

confidence: 99%

System Identification using the Neural-Extended Kalman Filter for Control Modification

Stubberud¹,

Kramer

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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The neural extended Kalman filter has been shown to be able to work and train on-line in a control loop and as a state estimator for maneuver target tracking. Often, however, the design of a control system does not have a state estimator in the feedback loop. The ability of the NEKF to learn dynamics in an open-loop implementation, such as with target tracking and intercept prediction, can be used to identify mis-modeled dynamics. The improved system model can then be used to adapt the control law to provide better performance based on the actual system dynamics. This new approach to neural extended Kalman filter control operations is introduced in this work using applications to the nonlinear version of the standard cart-pendulum system.

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Kinematic Prediction for Intercept Using a Neural Kalman Filter

Cited by 5 publications

References 7 publications

Visual Tracking in Engineering Based on Sampling Rate Conversion and Trace Forecast

Visual Tracking in Engineering Based on Sampling Rate Conversion and Trace Forecast

Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

System Identification using the Neural-Extended Kalman Filter for Control Modification

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