An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method.

For the airborne pod strapdown inertial navigation system, it is necessary to use the host aircraft's inertial navigation system for the transfer alignment as quickly and accurately as possible in the flight process of the aircraft. The purpose of this paper is to propose an adaptive transfer alignment method based on the observability analysis for the strapdown inertial navigation system, which is able to meet the practical need of maintaining the navigation accuracy of the airborne pod. The observability of each state variable is obtained by observability analysis of system state variables. According to the weight of the observability, a transfer alignment filter algorithm based on adaptive adjustment factor is constructed to reduce the influence of weak observability state variables on the whole filter, which can improve the estimation accuracy of transfer alignment. Simulations and experiment tests of the airborne pod and the master strapdown inertial navigation systems show that the adaptive transfer alignment method based on the observability analysis can overcome the shortage of the weak observability state variables, so as to improve the alignment and the navigation performance in practical applications, thus improving the adaptability of the airborne pod.

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