Flight Data-Driven LSTM-Family Models for Resource-Aware Edge Deployment in Aerial Systems: A Review and Methodological Evaluation

Flight data-driven modeling has become an important approach for trajectory prediction, anomaly detection, and risk assessment in unmanned aerial vehicles and other aerial systems. Such data are usually high-dimensional, nonlinear, multirate, and non-stationary, especially during maneuvering flight, environmental disturbance, and mission-phase transitions. Traditional physics-based methods and shallow machine learning models often have limited adaptability in these conditions, while Long Short-Term Memory (LSTM) networks and their variants have shown strong potential for learning temporal dependencies from complex flight sequences. This paper reviews the development and application of LSTM-family models for flight data analysis, with attention to both methodological performance and resource-aware deployment. The reviewed models include basic LSTM, BiLSTM, CNN-LSTM, ConvLSTM, LSTM autoencoder, attention-enhanced LSTM, graph-based LSTM, and uncertainty-aware LSTM. Their applications are discussed in three main areas: flight trajectory prediction, anomaly detection, and risk assessment. Beyond prediction accuracy, this review also examines robustness to distribution shift, physical consistency, interpretability of anomalies, uncertainty estimation, and onboard implementation. A four-layer and ten-dimensional evaluation framework is presented across data characteristics, model performance, physical-mechanism consistency, and system-engineering constraints. The evidence shows that model size, runtime memory, computational cost, inference latency, energy consumption, and target hardware are often insufficiently reported, while direct quantitative onboard validation remains scarce. This gap highlights the need for standardized deployment reporting.