Abstract
Aiming at the problem that the strapdown imaging seeker cannot measure line-of-sight (LOS) rate directly, this paper presents an effective algorithm for LOS rate estimation. To address the problem, the reference frames and angles are defined. According to the relative kinematics and attitude relationship between missile and target, the nonlinear state equations and measurement equations of LOS rate are derived, and the estimation algorithm based on unscented Kalman filter (UKF) is proposed. Considering the estimation accuracy mainly depends on body LOS (BLOS) angle accuracy and gyro accuracy, the paper is unprecedented to research how these two factors impact the LOS angle and rate accuracy by simulation. Semiphysical simulation experiment verifies the correctness and accuracy of the algorithm, and the result shows that the estimation algorithm can meet the accuracy and real-time requirements of guidance system simultaneously. Thus LOS rate estimation algorithm based on UKF provides a theoretical basis for engineering applications of strapdown imaging seeker.
Highlights
Optical imaging seekers have been widely used in military fields due to the high precision, such as the threemode cooled imaging seekers of JAVELIN missile and the uncooled infrared strapdown seeker Direct Attack Munition Affordable Seeker (DAMASK) of JDAM missile
Aiming at the problem that the strapdown imaging seeker cannot measure line-of-sight (LOS) rate directly, this paper presents an effective algorithm for LOS rate estimation
Only body LOS (BLOS) angle information can be measured directly; the LOS angles will couple with body attitude motion which leads to strong nonlinearity
Summary
Optical imaging seekers have been widely used in military fields due to the high precision, such as the threemode cooled imaging seekers of JAVELIN missile and the uncooled infrared strapdown seeker Direct Attack Munition Affordable Seeker (DAMASK) of JDAM missile. The relative relationship and frame transformation between missile and target were employed to derive the relationship among the LOS rate, BLOS rate, and attitude angles, where the BLOS rate can be obtained by a differential network [5]. All mentioned algorithms above are either mainly based on differential network and the reconstruction method or ignoring too much relative motion information between missile and target; nonlinear Kalman filtering algorithms are employed to estimate LOS rate, which failed to get high accuracy and cannot meet the real-time requirement. This paper proposes an effective nonlinear algorithm to estimate the LOS rate, we take the relative motion between the missile and target and the missile attitude into account to derive the estimation model, and LOS rate is estimated using UKF.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
