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Operation of a degenerate dual-pump phase sensitive amplifier (PSA) is thoroughly numerically investigated using a multi-wave model, taking into account high-order waves associated with undesired four-wave mixing (FWM) processes. More accurate phase-sensitive signal gain characteristics are obtained compared to the conventional 3-wave model, leading to precise optimization of the pump configuration in a degenerate dual-pump PSA. The signal gain for different pump configurations, as well as the phase sensitivity, is obtained and interpreted by investigating the dominant FWM processes in terms of the corresponding phase matching. Moreover, the relation between dispersion slope and the width of the signal gain curve versus the pump-pump wavelength separation is revealed, permitting the application-oriented arbitrary tailoring of the signal gains by manipulating the dispersion profile and pump wavelength allocation.

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We have developed a radio-frequency local oscillator remote distribution system, which transfers a phase-stabilized 10.03 GHz signal over 100 km optical fiber. The phase noise of the remote signal caused by temperature and mechanical stress variations on the fiber is compensated by a high-precision phase-correction system, which is achieved using a single sideband modulator to transfer the phase correction from intermediate frequency to radio frequency, thus enabling accurate phase control of the 10 GHz signal. The residual phase noise of the remote 10.03 GHz signal is measured to be -70  dBc/Hz at 1 Hz offset, and long-term stability of less than 1×10⁻¹⁶ at 10,000 s averaging time is achieved. Phase error is less than ±0.03π.

ABSTRACT In this article, we investigate the horizontal trajectory tracking problem for an underactuated stratospheric airship subject to nonvanishing external disturbances and model uncertainties. By transforming the tracking errors into new virtual error variables, we can specify the transient and steady-state tracking performance of the resulting nonlinear system quantitatively, which means that under the proposed control scheme, the tracking errors will converge to prescribed residual sets around the origin before a preselected finite time with decay rates no less than a preassignable value. To address unknown items, minimal learning parameter (MLP) techniques for neural networks (NNs) approximation are employed, which efficaciously relax the computational burden, enhance the robustness against dynamics uncertainties and provide an improved property for disturbances rejection. A finite-time convergent observer (FTCO) is incorporated into the control framework to realise output-feedback control, ensuring that estimation errors are bounded during operation and approach zero within a finite time. Stability analysis proves that all the closed-loop signals are uniformly bounded. The effectiveness and advantages of the proposed control strategy are verified by simulation results.