Cooperative pattern recognition method for air-ground networked swarms

The air-ground networked swarms have excellent application potential in national economics and production, such as smart cities, smart agriculture and forestry, and intelligent transportation. It also shows an exceptional value of application in the military fields, such as battlefield situational awareness and air-ground cooperative strikes. This paper addresses the need for accurate perception and recognition of complex environmental targets in air-ground networked swarms. We present a distributed learning and adaptive information fusion approach for air-ground network clustering, and we develop a model that minimizes the global likelihood function based on the probability of pattern categorization. This algorithm includes two main steps: information diffusion based on the gradient descent method and information fusion based on adaptive weighting calculation, and forms an air-ground cooperative pattern recognition method. Furthermore, the average error recursive equation for the cooperative pattern recognition algorithm of the air-ground networked clusters is derived, and the error convergence of the algorithm is theoretically proven. The results of the simulation demonstrate that the distributed fusion algorithm is accurate and that it can converge to the theoretical optimal level of the system in terms of both average mean square deviation and system error of information estimation. The simulation results show that the distributed fusion algorithm has good accuracy, and the algorithm's average mean square deviation and system error of information estimation can converge to the system's theoretical optimal level. When the number of nodes increases from 10 to 40, the mean square deviation of the distributed fusion algorithm for air-ground networked swarms decreases from −48.70 dB to −53.96 dB, and the system error reduces from −27.42 dB to −30.22 dB, which is close to the theoretical value of the error. Comparative experiments show that the algorithm proposed in this paper has good accuracy compared to traditional methods and can effectively support the perception and recognition of complex environmental targets for air-ground networked swarms.