Logarithmic detectors (LogDs) have been used in coherent Brillouin optical time-domain analysis (BOTDA) sensors to reduce the effect of phase fluctuation, demodulation complexities, and measurement time. However, because of the inherent properties of LogDs, a DC component at the level of hundreds of millivolts that prohibits high-gain signal amplification (SA) could be generated, resulting in unacceptable data acquisition (DAQ) inaccuracies and decoding errors in the process of prototype integration. By generating a reference light at a level similar to the probe light, differential detection can be applied to remove the DC component automatically using a differential amplifier before the DAQ process. Therefore, high-gain SA can be employed to reduce quantization errors. The signal-to-noise ratio of the weak Brillouin gain signal is improved from ∼11.5 to ∼21.8 dB. A BOTDA prototype is implemented based on the proposed scheme. The experimental results show that the measurement accuracy of the Brillouin frequency shift (BFS) is improved from ±1.9 to ±0.8 MHz at the end of a 40-km sensing fiber.
We demonstrate a temperature sensor with ~ 9 times sensitivity enhancement that consists of two cascaded sagnac interferometers and works analogously to a Vernier scale. Experimental results show that the temperature sensitivity is increased from -1.46 nm/°C based on a single sagnac configuration to -13.36nm/°C.
Camera calibration is an important step for vision-based measurement applications. A well-known flexible camera calibration method is analyzed that uses the checkerboard pattern plane and in which the camera can be moved freely. When using a perspective projection camera model, characteristics of both the objective plane and the image plane are utilized and accurate results can be obtained. However, the method's results may fail when the rotation angles of the planar pattern are small, and the distortion coefficients obtained under the perspective projection model can not be used for a real-time vision application. We solve the ill-conditioned equations using the genetic algorithm, and the correct camera parameters are always obtained. We compute the distortion coefficients of the inverse projection model, which can be used for general vision applications. The influence of the corner detection precision is taken into consideration. Simulation shows that the best results may be obtained when the planar pattern is placed in a close range and its rotation angle is small. Simulations and real-world experiments illustrate that the improved calibration algorithm can always obtain robust and accurate results.
In a real-time vision navigation system, an accurate and fast convergent pose estimation algorithm is required for the video guidance sensor. The orthogonal iteration (OI) algorithm is fast and globally convergent, but its results have a large translation error at a close range, and sometimes it fails to give a correct rotation matrix when the data are severely corrupted, when using the 3-D feature points. When the rotation matrix solution in the OI algorithm has been refined, an efficient pose estimation algorithm is derived. Simulation of the improved algorithm shows that the rotation matrix is always proper, which in turn improves the accuracy of the translation vector. The noise resistance and the outlier tolerance are enhanced by using the improved algorithm. The two algorithms are applied to our experimental system for an unmanned vehicle rendezvous and docking simulation separately. The comparison experiments show that the relative distance error is less than 0.28% from 1.5 to 5 m, and the rotation angle error is within ±0.7 deg in 5 m using the improved algorithm. These are better than the results using the OI algorithm.