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Under the influence of the radome of the active and passive radar, the uniform circular array (UCA) becomes non-uniform in the electromagnetic performance, so the accuracy of direction of arrive (DOA) estimation is not high. This paper presents a universal look-up table algorithm for 5-element circular array to solve the problem. Firstly, we traverse the view field of the radar to obtain the long...
The direction-of-arrival (DOA) estimation for the long-distance underwater acoustic sources usually suffers from low levels of signal-to-noise ratio (SNR) and insufficient snapshot data. Therefore, a novel signal self-nulling based DOA estimation algorithm is proposed. First, assume that the angular sector, where the desired signal is located, is known. Then, the beampattern of the minimum variance...
A passive synthetic array (PSA) using a single moving sensor is formed to estimate direction of arrival (DOA). And the signal model with velocity errors is constructed. By using MUSIC algorithm, the angle estimation performance is analyzed through numerical simulations under different velocity errors. With the increase of velocity error, resolution and accuracy of multi-target angle estimation decline...
The LMS-based indirect frequency estimation algorithm (IFE) is reformulated using weighted least-square error criterion. Theoretical analyses for steady state bias and mean square error (MSE) are addressed. It has been shown that the proposed algorithm outperforms the conventional LMS-based algorithms in terms of convergence speed at the same value of MSE.
Direct Sequence Spread Spectrum (DSSS) signal has been widely used because of its low signal-to-noise ratio, strong anti-interference, low interception rate and multi-path effect. It is gradually replacing the traditional communications, and widely used in modern military and commercial communications systems. Therefore, the corresponding direct-communication communication reconnaissance technology...
The fundamental frequency is one of the prosodic parameters, and many algorithms have been developed for estimating the fundamental frequency of speech signals. Most of them provide good results on good quality speech signals, but their performance degrades when dealing with noisy signals. Moreover, although some provide a probability for the voicing decision, none of them indicate how reliable the...
A cognitive radar adapts the transmit waveform in response to changes in the radar and target environment. In this work, we analyze the recently proposed sub-Nyquist cognitive radar wherein the total transmit power in a multi-band cognitive waveform remains the same as its full-band conventional counterpart. For such a system, we derive lower bounds on the mean-squared-error (MSE) of a single-target...
We propose a TDOA-based algorithm for source localization on rigid surfaces. This allows the conversion of readily available large surfaces into touch interfaces using surface-mounted vibration sensors. To achieve this, we characterize the arrival of each sensor-received signal by the arrival times of its frequency components. To estimate the arrival time of each frequency component, we first model...
Reverberation and noise are known to be the two most important culprits for poor performance in far-field speech applications, such as automatic speech recognition. Recent research has suggested that reverberation-aware speech enhancement (or speech technologies, in general) could be used to improve performance. However, recent results also show existing blind room acoustics characterization algorithms...
Classic approaches to multi-channel signal enhancement rely on model assumptions regarding speech source relative transfer functions and noise covariance matrix, or on estimates thereof obtained in, e.g., speech pauses. To alleviate these constraints, we here investigate an approach to adaptive estimation of the speech (target) source and noise related acoustic parameters based on localized speech...
As compared to the FFT, the recently introduced Sparse Fourier Transform (SFT) achieves substantial reduction in the complexity of detecting frequencies in signals that are sparse in the frequency domain. However, the SFT requires the significant frequencies to be on the grid and the exact sparsity of the signal to be known. In this paper, we propose a framework that overcomes these issues. Our method...
This paper presents a blind algorithm for the automatic detection of isolated astrophysical pulses. The detection algorithm is applied to spectrograms (also known as “filter bank data” or “the (t,f) plane”). The detection algorithm comprises a sequence of three steps: (1) a Radon transform is applied to the spectrogram, (2) a Fourier transform is applied to each projection parametrized by an angle,...
In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity...
The Apparent Diffusion Coefficient (ADC) is a quantitative measure derived from MRI that is able to asses the amount of diffusion within living tissues. It is employed to characterize different diseases and to evaluate response to therapy. For ADC estimation, the diffusion signal needs to be sampled using a small number of values, presenting distortions due to the aliasing and windowing effect. In...
Speech recognition performance deteriorates in face of unknown noise. Speech enhancement offers a solution by reducing the noise in speech at runtime. However, it also introduces artificial distortions to the speech signals. In this paper, we aim at reducing the artifacts that has adverse effects on speech recognition. With this motivation, we propose a modification scheme including smoothing adaptation...
Given that a sequence x(n) is periodic with period P belonging to a known integer set {P1, P2, … PL}, what is the minimum number of samples of x(n) required to find the period? For the special case where the samples of x(n) are constrained to be contiguous in time, this problem has recently been solved. More generally, when the samples are allowed to be non-contiguous, the problem is quite difficult...
In the era of deep learning, although beam-forming multi-channel signal processing is still very helpful, it was reported that single-channel robust front-ends usually cannot benefit deep learning models because the layer-by-layer structure of deep learning models provides a feature extraction strategy that automatically derives powerful noise-resistant features from primitive raw data for senone...
The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many...
In this paper, the estimation of a narrowband time-varying channel under the practical assumptions of finite block length and finite transmission bandwidth is investigated. It is shown that the signal after passing through a time-varying narrowband channel, under these assumptions, reveals a particular low-rank structure. The rank in this structure is governed by the number of dominant paths in the...
An effective method is proposed to estimate the desired-signal (S) subspace by the intersection between the signal-plus-interference (SI) subspace and a reference space covering the angular region where the desired signal is located. The estimated S subspace is robust to steering vector mismatch and overestimation of the SI subspace, capable of detecting the relative strength of the desired signal...
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