Intelligent anti-jam communication is a new generation of anti-interference technology combined with artificial intelligence, and the identification of interference signals is the basis of the technology. It is required to achieve better identification results with lower computational complexity in engineering applications. However, previous research has shown that they cannot balance these two sides. Here, we report an interference signal identification algorithm based on Extreme Learning Machine (ELM). Five typical oppressive interference signals were recognized based on ELM which is based on feature extraction. The overall correct identification rate is more than 96% under the condition of 40 neurons in a single hidden layer, and it has certain generalization ability. This study objectively promotes the engineering application of this technology.
To reduce the measurement error in the vibration scenario, this paper proposes a cooperative post-processing method based on the measured overload information and Kalman filter. In order to make the equation of motion more accurately, the target measures and shares its overload information to the source. The source then calculates the overall overload information and perform Kalman filter to the real-time measured data. The performance evaluation is carried out through a physical measurement test and a post-processing simulation. The real-time measuring data is updated at 200Hz while the vibration frequency is about 1Hz. The simulation results show that the proposed method can reduce the measurement error by about 9.9 times and is insensitive to the common time synchronization error and overload measurement error.
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