Abstract
ABSTRACTIn this paper, the line of sight (LOS) guidance law is improved to implement tracking toward a moving target. In the presence of sensor noise, an optimal information fusion Kalman filter weighted by scalars is utilized for two-sensor information fusing, improving the trajectory tracking precision. Under the communication delay, n-step ahead Kalman predictor compensates for communication delay and provides LOS guidance law with more accurate target estimates. The results of the simulation demonstrate the feasibility and effectiveness of the proposed control strategy.
Highlights
The line of sight (LOS) guidance law is extensively applied in the field of navigation control, and it is known as three-point guidance (Lee & Lee, 1995; Shneydor, 1998; Zarchan, 1997)
Different from the control strategy proposed in Jalali-Naini and Esfahanian (2004), it is shown in Belkhouche et al (2006) that this strategy is based on the integration of the kinematics equations with geometric rules
The two-sensor information fusion nstep-ahead steady-state optimal Kalman predictor and Wiener predictor are presented in Gao, Wang, Mao, Liang, and Deng (2005), where the optimal weighting matrices and minimum fused error variance matrix are given
Summary
The line of sight (LOS) guidance law is extensively applied in the field of navigation control, and it is known as three-point guidance (Lee & Lee, 1995; Shneydor, 1998; Zarchan, 1997). The two-sensor information fusion nstep-ahead steady-state optimal Kalman predictor and Wiener predictor are presented in Gao, Wang, Mao, Liang, and Deng (2005), where the optimal weighting matrices and minimum fused error variance matrix are given. Different from the method in Belkhouche et al (2006), this paper extends the range of values for the orientation angle of the robot, and improves the LOS guidance law. Based on Gao et al (2005) and Alkharabsheh et al (2007), the n-step ahead Kalman predictor compensates for communication delay and provides the LOS guidance law with more accurate target estimates. The n-step ahead Kalman predictor compensates for communication delay and provides the LOS guidance law with more accurate target estimates.
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