In search and rescue (SAR) operation, the potential of Unmanned Aerial Vehicles (UAVs) gathers great attention. Existing studies have made various experiments to find victims by a UAVs with a single sensor, e.g., one of an optical camera, a thermal infrared camera, and a radio wave signal. However, the experimental environments are limited to show the performance of the sensor. Since there are various SAR missions, it is difficult to choose the best sensor for all environments. Then, to enhance the UAV performance, we need to consider multiple sensors to find a victim efficiently. In the paper, we investigate optical camera and thermal infrared camera for finding a victim helpfully. In the investigation, we observed the differences between their images by distance and brightness to find a human.

In this paper, we propose a novel estimation technique1of the power spectral density (PSD) of phase noise based on complementary autocorrelation (CAC). Utilizing the fact that the CAC of phase noise is nonzero, this technique offers a distinct advantage that the PSD of phase noise is estimated while canceling other circular-symmetric noises. This property is most useful when estimating phase noise directly from digitally-modulated signals because they typically are accompanied by various kinds of circular noises that easily mask the phase noise of interest. A practical method to compute the phase noise PSD is given. In addition, an expression for a noise suppression factor of the method is given. Numerical simulations demonstrate the effectiveness of the proposed method, and verifies the noise suppression factor.

We propose an application of variational Bayesian inference to the problem of blind estimation of single-input multiple-output (SIMO), finite-impulse-response (FIR) channels. The blind channel estimation problem has been extensively studied well over a decade. Nevertheless, there are some types of channel that cannot be successfully estimated by existing blind methods. One example of such a channel type is frequency-selective, exponentially-weighted Rayleigh fading channel. Our blind estimation approach to it is to incorporate prior knowledge on channel weighting in the form of channel prior distribution, then obtain the approximation to the posterior distribution of the channel using the variational Bayesian framework. Despite the error due to the mean field approximation involved in the variational framework, the derived algorithm has been verified by simulation to provide much improvements over the existing ones in a statistical sense.

We propose an application of the Expectation-Maximization (EM) algorithm to the problem of blind estimation of single-input multiple-output (SIMO), finite-impulse-response (FIR) channels. We first assume Gaussian input to formulate an EM-based estimation of the signal subspace of the output covariance matrix. This Gaussian assumption allows us to utilize knowledge from EM-based probabilistic principle component analysis (P-PCA). Next, we show that the equilibrium point of the EM iteration equations is reached without the Gaussian assumption, which suggests usage of non-Gaussian communication input signals. The estimated signal subspace is then utilized to identify the channels. In principle, the proposed method yields the same channel estimates as the widely-known subspace method, but is computationally more efficient. In addition, unlike typical EM applications, the proposed scheme is free from cumbersome parameter initialization issue, which greatly increases flexibility of the proposed scheme.