In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

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To solve the combat task assignment of reconnaissance unmanned aerial vehicle (RUAV)/unmanned combat aerial vehicle(UCAV), this paper proposed an efficient task assignment method that takes into account the expected destruction probability of target. This method improves the utility function and constraint of the model that based on the goal of destroying the total sum of the target value. The adjustment factor is added to the model to achieve a balanced distribution of RUAVs/UCAVs resources; the expected destruction probability of target is introduced as a constraint to prevent the excessive distribution of RUAVs/UCAVs resources. Subsequently, a greedy algorithm based on maximizing marginal-return is designed to solve the proposed model. The simulation results show that the improved algorithm not only meets the combat effectiveness but also improves the economic performance on the basis of real-time task allocation.