An area-based position and attitude estimation for unmanned aerial vehicle navigation

The paper aims to challenge non-GPS navigation problems by using visual sensors and geo-referenced images. An area-based method is proposed to estimate full navigation parameters(FNPs), including attitude, altitude and horizontal position, for unmanned aerial vehicle(UAV) navigation. Our method is composed of three main modules: geometric transfer function, local normalized sobel energy image(LNSEI) based objective function and simplex-simulated annealing(SSA) based optimization algorithm. The adoption of relatively rich scene information and LNSEI, makes it possible to yield a solution robustly even in the presence of very noisy cases, such as multi-modal and/or multi-temporal images that differ in the type of visual sensor, season, illumination, weather, and so on, and also to handle the sparsely textured regions where features are barely detected or matched. Simulation experiments using many synthetic images clearly support noise resistance and estimation accuracy, and experimental results using 2367 real images show the maximum estimation error of 5.16(meter) for horizontal position, 9.72(meter) for altitude and 0.82(degree) for attitude.

Three-dimensional sound source localization using distributed microphone arrays

To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.