By constructing an external circulating cavity to provide sufficient delay that equals a multiple of pulse repetition time, a method with a fixed experimental configuration is proposed to measure the coherence length of both single-frequency and microwave-modulated optical pulses. This method can accurately determine the number of coherent pulses as well as distinguish the coherence states: complete coherence, partial coherence and complete incoherence. In addition, all desired coherence phenomena are obtained by one-time measurement, avoiding other operations like frequent fiber-cutting or devices-reconnection in previous methods. Simulation results show that the coherence length of the dual-frequency laser is periodically extended by the reciprocal of the frequency difference, and the random jitter of pulse propagation time would result in obvious measurement errors via perturbing coherence status.
Aircraft wake is a pair of strong counter rotating vortices formed behind the aircraft in flight. Its rapid identification is the basis of wake characteristic parameter inversion, which has important application requirements in the field of aviation safety. In this paper, convolution neural network based on improved alexnet structure is used to deeply learn the characteristics of aircraft wake. Firstly, the alexnet network model is improved based on the aircraft wake characteristics and recognition requirements, and then the model is trained based on a large number of lidar wake observation data. The results show that the accuracy of the model in the test set reaches 97%, and it has good generalization ability, and can quickly and accurately identify the aircraft wake under complex background wind field.
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