Design of Real-time Airborne Airdrop State Recognition System Based on CNN-LSTM
In many application scenarios of airborne airdrops,devices are required to automatically recognize the status and react accordingly in a timely manner.However,there may be limitations in the performance of traditional meth-ods that make it difficult to respond flexibly to various situations.To solve this problem,this paper proposes a neural network model based on the combination of convolutional neural network and long short-term memory network(CNN-LSTM)and deploys it on the RV1106 chip to build a real-time airborne airdrop state recognition system.First,the raw data of airborne drop process and MATLAB simulation data are collected as training samples,then the network model is trained by the improved CNN-LSTM algorithm,and finally the network model is deployed to the terminal sys-tem.Through multi-dimensional feature fusion,this algorithm can determine the current state more accurately.The ex-perimental results show that the method proposed in this paper improves the recognition rate by 3.1％and the utility rate by 61％over the LSTM algorithm alone,which can meet the requirements of flight and parachute state recognition.
LSTMreal-timemultidimensional feature fusionRV1106 chipAI model deployment
万方数据
维普
