According to the principle of complete chaos synchronization and the theory of Hilbert phase transformation, we propose a novel real-time multi-target ranging scheme by using chaotic polarization laser radar in the drive-response vertical-cavity surface-emitting lasers (VCSELs). In the scheme, to ensure each polarization component (PC) of the master VCSEL (MVCSEL) to be synchronized steadily with that of the slave VCSEL, the output x-PC and y-PC from the MVCSEL in the drive system and those in the response system are modulated by the linear electro-optic effect simultaneously. Under this condition, by simulating the influences of some key parameters of the system on the synchronization quality and the relative errors of the two-target ranging, related operating parameters can be optimized. The x-PC and the y-PC, as two chaotic radar sources, are used to implement the real-time ranging for two targets. It is found that the measured distances of the two targets at arbitrary position exhibit strong real-time stability and only slight jitter. Their resolutions are up to millimeters, and their relative errors are very small and less than 2.7%.
We experimentally investigate the performances of an optical reservoir computing (RC) system based on two parallel time-delay reservoirs composed of two semiconductor lasers (SLs) subject to optical feedback. In such a system, the information being processed is split into two parts to send into two reservoirs through directly modulating the pump currents of two SLs, and the temporal output of the two SLs are sampled and taken as the virtual node states for training and testing. Via Santa Fe time series prediction task and multi-waveform recognition task, the performances of the proposed RC system are investigated and compared with those of the system based on one reservoir. The results show that the system based on two parallel reservoirs behaves better performance and stronger parameter robustness than that based on one reservoir. Moreover, through analyzing the dependence of the system performances on the number of virtual node states actually used for readout, the potential data processing rate (DPR) of the system is evaluated. For processing a prediction task under guaranteeing the normalized mean square error below 0.1 and a recognition task under guaranteeing the signal error rate below 0.005, the potential DPR of the proposed RC system can achieve 200 MSa/s, which is twice the DPR of the system with only one reservoir.
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