Abstract: A multipath suppression method based on a steepest descent method includes stripping, according to carrier Doppler shift information fed back by a phase-locked loop, a carrier from an intermediate-frequency signal input into a tracking loop; constructing, on the basis of the autocorrelation characteristics of a ranging code, a quadratic cost function related to a measurement deviation of the ranging code, the cost function being not affected by a multipath signal; and finally, designing a new tracking loop of the ranging code according to the quadratic cost function and the principle of the steepest descent method, such that the loop has a multipath suppression function without increasing the computational burden. Compared with a narrow-distance correlation method, the current method reduces computing resources by ?, the design and adjustment of parameters are simple and feasible, a multipath suppression effect is superior, and a high engineering application value is obtained.

Abstract: A multipath suppression method based on a steepest descent method includes stripping, according to carrier Doppler shift information fed back by a phase-locked loop, a carrier from an intermediate-frequency signal input into a tracking loop; constructing, on the basis of the autocorrelation characteristics of a ranging code, a quadratic cost function related to a measurement deviation of the ranging code, the cost function being not affected by a multipath signal; and finally, designing a new tracking loop of the ranging code according to the quadratic cost function and the principle of the steepest descent method, such that the loop has a multipath suppression function without increasing the computational burden. Compared with a narrow-distance correlation method, the current method reduces computing resources by ?, the design and adjustment of parameters are simple and feasible, a multipath suppression effect is superior, and a high engineering application value is obtained.

Abstract: Disclosed is an online dynamic mutual-observation modeling method for unmanned aerial vehicle (UAV) swarm collaborative navigation, which includes: first performing first-level screening for members according to the number of usable satellites received by a satellite navigation receiver of each member, to determine the role of each member in collaborative navigation at the current time, and then establishing a moving coordinate system with each object member to be assisted as the origin, and calculating coordinates of each candidate reference node; and on this basis, performing second-level screening for the candidate reference nodes according to whether mutual distance measurement can be performed with each object member, to obtain a usable reference member set, and preliminarily establishing a dynamic mutual-observation model; and finally, optimizing the model by means of iterative correction, and conducting a new round of dynamic mutual-observation modeling according to an observation relationship in the U