In this paper we extend hyperparameter-free sparse signal reconstruction approaches to permit the high-resolution time delay estimation of spread spectrum signals and demonstrate their feasibility in terms of both performance and computation complexity by applying them to the ISO/IEC 24730-2.1 real-time locating system (RTLS). Numerical examples show that the sparse asymptotic minimum variance (SAMV) approach outperforms other sparse algorithms and multiple signal classification (MUSIC) regardless of the signal correlation, especially in the case where the incoming signals are closely spaced within a Rayleigh resolution limit. The performance difference among the hyperparameter-free approaches decreases significantly as the signals become more widely separated. SAMV is sometimes strongly influenced by the noise correlation, but the degrading effect of the correlated noise can be mitigated through the noise-whitening process. The computation complexity of SAMV can be feasible for practical system use by setting the power update threshold and the grid size properly, and/or via parallel implementations.

Much attention has recently been paid to direction of arrival (DOA) estimation using compressed sensing (CS) techniques, which are sparse signal reconstruction methods. In our previous study, we developed a method for estimating the DOAs of multi-band signals that uses CS processing and that is based on the assumption that incident signals have the same complex amplitudes in all the bands. That method has a higher probability of correct estimation than a single-band DOA estimation method using CS. In this paper, we propose novel DOA estimation methods for multi-band signals with frequency characteristics using the Khatri-Rao product. First, we formulate a method that can estimate DOAs of multi-band signals whose phases alone have frequency dependence. Second, we extend the scheme in such a way that we can estimate DOAs of multi-band signals whose amplitudes and phases both depend on frequency. Finally, we evaluate the performance of the proposed methods through computer simulations and reveal the improvement in estimation performance.

We previously proposed a unified wireless system called “Flexible Wireless System”. Comprising of flexible access points and a flexible signal processing unit, it collectively receives a wideband spectrum that includes multiple signals from various wireless systems. In cases of simultaneous multiple signal reception, however, reception performance degrades due to the interference among multiple signals. To address this problem, we propose a new signal separation and reconstruction method for spectrally overlapped signals. The method analyzes spectral information obtained by the short-time Fourier transform to extract amplitude and phase values at each center frequency of overlapped signals at a flexible signal processing unit. Using these values enables signals from received radio wave data to be separated and reconstructed for simultaneous multi-system reception. In this paper, the BER performance of the proposed method is evaluated using computer simulations. Also, the performance of the interference suppression is evaluated by analyzing the probability density distribution of the amplitude of the overlapped interference on a symbol of the received signal. Simulation results confirmed the effectiveness of the proposed method.

This paper presents a new method for reconstruction of trigonometric polynomials, a specific class of bandlimited signals, from a number of integrated values of input signals. It is applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise rms measurements of periodic signal (or power and energy) based on the presented signal reconstruction. Based on the value of the integral of the original input (analogue) signal, with a known frequency spectrum but unknown amplitudes and phases, a reconstruction of its basic parameters is done by the means of derived analytical and summarized expressions. Subsequent calculation of all relevant indicators related to the monitoring and processing of ac voltage and current signals is provided in this manner. Computer simulation demonstrating the precision of these algorithms. We investigate the errors related to the signal reconstruction, and provide an error bound around the reconstructed time domain waveform.

Our work introduces a speech enhancement algorithm that modifies on-line the spectral representation of degraded speech to approximate the spectral coefficients of high quality speech. The proposed framework is based on the application of Discrete Fourier Transform (DFT) to a large ensemble of clean speech frames and the estimation of parametric, heavy-tail non-Gaussian probability distributions for the spectral magnitude. Each clean spectral band possesses a unique pdf. This is selected according to the smallest Kullback-Leibler divergence between each candidate heavy-tail pdf and the non-parametric pdf of the magnitude of each spectral band of the clean ensemble. The parameters of the distributions are derived by Maximum Likelihood Estimation (MLE). A maximum a-posteriori (MAP) formulation of the degraded spectral bands leads to soft threshold functions, optimally derived from the statistics of each spectral band and effectively reducing white and slowly varying coloured Gaussian noise. We evaluate the new algorithm on the task of improving the quality of speech perception as well as Automatic Speech Recognition (ASR) and demonstrate its robustness at SNRs as low as 0 dB.

An unwrapping of signal coefficients in transform domain is proposed for applications in which a lossy operation is performed on the coefficients between analysis and synthesis. It is shown that the unwrapping-based modification of signal-to-additive-signal ratio can employ the fact that an implementation of a biorthogonal decomposition is characterized by a mutually orthogonal eigenvectors. An example to illustrate the benefits of the presented approach in lossy image compression applications is shown.

In recent years, fractal processes have played important roles in various application fields. Since a 1/f process possesses the statistical self-similarity, it is considered sa a main part of fractal signal modeling. On the other hand, noise reduction is often needed in real-world signal processing. Hence, we propose an enhancement algorithm for 1/f signal disturbed by white noise. The algorithm is based on constrained minimization in a wavelet domain: the power of 1/f signal distortion in the wavelet domain is minimized under a constraint that the power of residual noise in the wavelet domain is smaller than a threshold level. We solve this constrained minimization problem using a Lagrangian equation. We also consider a setting method of the Lagrange multiplier in the proposed algorithm. In addition, we will confirm that the proposed algorithm with this Lagrange multiplier setting method obtains better enhancement results than the conventional algorithm through computer simulations.