Abstract
Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.
Highlights
IntroductionThe spatial registration is vital for current and near-future cooperative combat missions through the vehicle network to estimate and compensate the sensor errors by measuring the common target [1,2,3]
The spatial registration is vital for current and near-future cooperative combat missions through the vehicle network to estimate and compensate the sensor errors by measuring the common target [1,2,3].Depending on the dimension of the error model, the algorithm is usually divided into a two-dimensional (2-D) system and a three-dimensional (3-D) system [4]
Many typical 2-D algorithms have been proposed in literatures. e.g., Real Time Quality Control (RTQC) [5], Least Square (LS) [6], Generalized Least Square (GLS) [7], Exact Maximum Likelihood (EML) [8]
Summary
The spatial registration is vital for current and near-future cooperative combat missions through the vehicle network to estimate and compensate the sensor errors by measuring the common target [1,2,3]. In the case of two missiles cooperative target tracking, this paper employs an improved Kalman Filter on Earth-Centered Earth-Fixed coordinate (ECEF-KF) algorithm which transforms the measurements to public target from body systems into the ECEF system so as to isolate the motion of sensors. A new spatial registration algorithm is first proposed for sensors on high-speed moving vehicles, realizing the simultaneous estimation for system and attitude biases which are compensated to the biased measurements of the tracking schemes. Inspired by the ideal of integral controller, a Low-pass filter is used when the position relationship between missiles and target meets the special condition to inhibit the jitter of estimations This skill improve the adaptability of tracking system without time-delay caused by the common integral controllers.
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