A practical adaptive nonlinear tracking algorithm with range rate measurement

It is difficult to answer the problem whether the range rate measurement should be adopted to track a target in a tracking scenario. A practical adaptive nonlinear tracking algorithm with the range rate measurement is proposed, which avoids this problem and achieves good accuracy of target state estimation. First, three popular nonlinear filtering algorithms only with the position measurement are surveyed. Second, three popular nonlinear filtering algorithms with the position and range rate measurements are surveyed. Then, a novel tracking algorithm with range rate measurement is proposed based on the cumulative sum detector and the above two kinds of nonlinear algorithms. The results of simulation experiment demonstrate that the range rate measurement could reduce accuracy of the target state estimation in mismatch tracking scenarios. The results of simulation experiment also verify that the performance of proposed algorithm is better than the current state and the art interacting multiple-model algorithm and can well follow the state estimation output of the measurement equation matching the tracking scenario.

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Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement

International Journal of Distributed Sensor Networks ◽

10.1177/1550147717747848 ◽

2017 ◽

Vol 13 (12) ◽

pp. 155014771774784 ◽

Cited By ~ 4

Author(s):

Hongqiang Liu ◽

Zhongliang Zhou ◽

Haiyan Yang

Keyword(s):

Kalman Filter ◽

Interacting Multiple Model ◽

Square Root ◽

Multiple Model ◽

Rate Measurement ◽

Cubature Kalman Filter ◽

Range Rate

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Asymptotically Optimal Nonlinear Filtering: Theory and Examples with Application to Target State Estimation

IFAC Proceedings Volumes ◽

10.3182/20080706-5-kr-1001.01456 ◽

2008 ◽

Vol 41 (2) ◽

pp. 8611-8617 ◽

Cited By ~ 2

Author(s):

Tayfun Çimen ◽

A.O. Merttopçuoğlu

Keyword(s):

State Estimation ◽

Nonlinear Filtering ◽

Filtering Theory ◽

Asymptotically Optimal ◽

Target State ◽

Optimal Nonlinear Filtering

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Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target

Entropy ◽

10.3390/e23050550 ◽

2021 ◽

Vol 23 (5) ◽

pp. 550

Author(s):

Wasiq Ali ◽

Wasim Ullah Khan ◽

Muhammad Asif Zahoor Raja ◽

Yigang He ◽

Yaan Li

Keyword(s):

Neural Network ◽

Neural Networks ◽

Kalman Filter ◽

State Estimation ◽

Real Time ◽

Nonlinear Filtering ◽

Actual State ◽

Filtering Algorithms ◽

Feedback Neural Network ◽

Nonlinear Autoregressive

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

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Robust adaptive target state estimation for missile guidance using the interacting multiple model Kalman filter

IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062) ◽

10.1109/plans.2000.838325 ◽

2002 ◽

Cited By ~ 4

Author(s):

C. Rago ◽

R.K. Mehra

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Interacting Multiple Model ◽

Missile Guidance ◽

Multiple Model ◽

Target State ◽

Robust Adaptive

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The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽

10.3390/s21062085 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2085

Author(s):

Xue-Bo Jin ◽

Ruben Jonhson Robert RobertJeremiah ◽

Ting-Li Su ◽

Yu-Ting Bai ◽

Jian-Lei Kong

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Future Development ◽

Data Driven ◽

Estimation Methods ◽

Multiple Model ◽

Development Trends ◽

Research Results ◽

Model Driven ◽

Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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