In the cooperative penetration application scenario, there is an immediate need to optimize the communication topology and wing aircraft formation structure. A formation configuration optimization design method based on penetration thoroughfares is proposed to address the issues of reference benchmark selection and modeling of aircraft/interception force/battlefield relationships in formation configuration optimization. The leader aircraft’s role is obtained by the optimization of the communication topology. The formation configuration is referenced by the geometric centers of the leader aircraft and each team leader in turn. Explicit expressions for the formation configuration are designed to eliminate the dependence on obtaining prior information about the leader aircraft. A penetration thoroughfares model is established for winged aircraft to ensure their advantages in detection, anti-detection, and maneuver avoidance capabilities at various battlefield grids. To address the issue of balancing information sharing and network load on communication topology optimization, a communication topology is constructed under the constraints of formation configuration. In order to optimize both formation configuration and communication topology, a topology switching strategy depending on battlefield conditions is suggested after a communication topology optimization approach based on minimal spanning tree and optimal rigid graph is presented. Finally, the effectiveness of the designed optimization method is verified by taking the cooperative penetration against military threats for winged aircraft as an example.

The problem of distributed fusion and random observation loss for mobile sensor networks is investigated herein. In view of the fact that the measured values, sampling frequency and noise of various sensors are different, the observation model of a heterogeneous network is constructed. A binary random variable is introduced to describe the drop of observation component and the topology switching problem caused by complete observation loss is also considered. A cubature information filtering algorithm is adopted to design local filters for each observer to suppress the negative effects of measurement noise. To derive a consistent and accurate estimation result, a novel weighted average consensus-based filtering approach is put forward. For the sensor that suffers from observation loss, its local prediction information vector is fused with the information contribution vectors of the neighbors to obtain the local estimation. Then the consensus weight matrix is designed for consensus-based distributed collaborative information fusion. The boundness of the estimation errors is proved by employing the stochastic stability theory. In the end, two numerical examples are offered to assert the validity of the presented method.