The invention discloses a comprehensive capability evaluation method for a
monitoring system for space target monitoring, and relates to the technical field of space target monitoring, and the method combines a
physical model and a long-
short term memory (LSTM) network
deep learning model, captures the nonlinear change of an
orbit, effectively deals with the rapid change and abrupt change of the
orbit, and improves the comprehensive capability of the
monitoring system. High-precision prediction is still kept in a complex dynamic
orbit environment, LSTM and extended Kalman filtering EKF are introduced, prediction can be performed based on historical orbit data, real-time
observation data can be dynamically corrected, the response of the
system to target orbit change is more timely, the self-adaptability of the
system in the face of orbit change is further optimized through
reinforcement learning, and the prediction accuracy of the
system is improved. Compared with the prior art, the method has the advantages of reducing the
response time, improving the real-time performance, dynamically adjusting the tracking strategy through
reinforcement learning, and adopting an interference
processing mechanism, effectively solving the problem of target trajectory overlapping or dynamic interference, and ensuring that the target is not lost or mistakenly tracked even in a high-density and complex-interference environment.