Phased Array Radar Seeker Pointing Error Compensation Using Multiple-model Extended Kalman Filters

2021 ◽  
Author(s):  
Qi Wang ◽  
Zhizhong Liao ◽  
Fei Yan
Keyword(s):  
Kalman Filters ◽  
Error Compensation ◽  
Phased Array ◽  
Multiple Model ◽  
Extended Kalman Filters ◽  
Pointing Error ◽  
Phased Array Radar

scholarly journals Intelligent Motion Compensation for Improving the Tracking Performance of Shipborne Phased Array Radar

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
J. Mar ◽  
K. C. Tsai ◽  
Y. T. Wang ◽  
M. B. Basnet
Keyword(s):  
Error Compensation ◽  
Phased Array ◽  
Tracking Performance ◽  
Compensation Mechanism ◽  
Tracking Accuracy ◽  
Pointing Error ◽  
Maneuvering Targets ◽  
Phased Array Radar ◽  
Planar Array ◽  
Adaptive Extended Kalman Filter

The shipborne phased array radar must be able to compensate the ship’s motion and track the maneuvering targets automatically. In this paper, the real-time beam pointing error compensation mechanism of a planar array antenna for ship’s motion is designed to combine with the Kalman filtering. The effect of beam pointing error on the tracking performance of shipborne phased array radar is examined. A compensation mechanism, which can automatically correct the beam pointing error of the planar antenna array, is proposed for shipborne phased array radar in order to achieve the required tracking accuracy over the long dwell time. The automatic beam pointing error compensation mechanism employs the parallel fuzzy basis function network (FBFN) architecture to estimate the beam pointing error caused by roll and pitch of the ship. In the simulation, the models of roll and pitch are used to evaluate the performance of beam pointing error estimation mechanism based on the proposed parallel FBFN architecture. In addition, the effect of automatic beam pointing error compensation mechanism on the tracking performance of adaptive extended Kalman filter (AEKF) implemented in ship borne phased array radar is also investigated. Simulations find out that the proposed algorithms are stable and accurate.

INFLUENCE OF A PRIORI INFORMATION ON THE MULTIPLE MODEL ALGORITHM CONVERGENCE DURING TRACKING A BALLISTIC TARGET

2020 ◽  
Author(s):  
Д.С. Голенко ◽  
М.И. Сычев
Keyword(s):  
Mathematical Modeling ◽  
Kalman Filters ◽  
A Priori ◽  
Multiple Model ◽  
A Priori Information ◽  
Extended Kalman Filters ◽  
Sigma Point ◽  
Passive Antenna ◽  
Priori Information ◽  
Model Algorithm

Рассмотрена задача сопровождения маневрирующего баллистического объекта на этапе входа в атмосферу с помощью пассивной антенной решетки. Предложено использовать многомодельный алгоритм на основе расширенного и сигма-точечного фильтров Калмана. Проанализировано влияние точности априорной информации на сходимость многомодельного алгоритма. С помощью математического моделирования проведено сравнение с одиночными фильтрами Калмана. The problem of reentry ballistic target tracking with a passive antenna array is considered. Multiple model algorithm based on extended and sigma-point Kalman filters is proposed. A priori information accuracy influence on the convergence of multiple model algorithm is analyzed. Using mathematical modeling, the results were compared with regular extended Kalman filters.

scholarly journals Ship-Borne Phased Array Radar Using GA Based Adaptiveα-β-γFilter for Beamforming Compensation and Air Target Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
J. Mar ◽  
Chen-Chih Liu ◽  
M. B. Basnet
Keyword(s):  
Mathematical Model ◽  
Phased Array ◽  
Ship Motion ◽  
Mean Square ◽  
Tracking Accuracy ◽  
Dynamic Nature ◽  
Swarm Optimization ◽  
Pointing Error ◽  
Phased Array Radar ◽  
Adaptive Extended Kalman Filter

Beam pointing error caused by ship motion over the ocean affects the tracking performance of the ship-borne phased array radar. Due to the dynamic nature of the sea environments, the ship-borne phased array radar must be able to compensate for the ship’s motion adaptively. In this paper, the adaptiveα-β-γfilter is proposed for the ship-borne phased array radar to compensate for the beam pointing error and to track the air target. The genetic algorithm (GA) and the particle swarm optimization (PSO) methods are applied to estimate the gain parameters of adaptiveα-β-γfilters, while achieving the optimum objective of minimum root mean square error (RMSE). The roll and pitch data measured from a gyroscope of the sea vehicle and generated from ship motion mathematical model are used in the experiments. The tracking accuracy of adaptiveα-β-γfilter using the GA method is compared with PSO method under different ship motion conditions. The convergent time and tracking accuracy of ship-borne phased array radar using the proposed GA based adaptiveα-β-γfilter are also compared with the adaptive extended Kalman filter (AEKF). Finally, it is proved that the proposed GA based adaptiveα-β-γfilter is a real time applicable algorithm for ship-borne phased array radar.

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