515 research outputs found
A structure-aware DOA estimation method for sources with short known waveforms
For direction of arrival (DOA) estimation with known waveform information, with a larger number of snapshots and a longer duration of the known waveforms, the required storage space for hardware implementation will increase. To save storage resources and also reduce the response time of the array system, the DOA estimation problem with short known waveforms in snapshot size is studied in this paper. We first establish the connection between DOA estimation for known waveform sources and the structured least squares (SLS) technique utilizing a potential rotation invariant property. Next, a constraint is formulated and then transformed into its real and imaginary parts to satisfy the requirements of SLS, and the resultant SLS optimization problem is solved iteratively. Simulation results show that the proposed method has a better performance than the existing DEML method for small numbers of snapshots
DOA estimation with known waveforms in the presence of unknown time delays and Doppler shifts
A novel DOA estimation method for known waveform sources with different unknown time delays and Doppler shifts is proposed. Based on the idea of maximum likelihood and the matrix projection theory, a decoupled cost function is first constructed and then the problem of estimating time delay and Doppler shift is transformed into a nonlinear least squares (NLS) problem. To solve the NLS problem efficiently without multidimensional search, a Toeplitz dominant rule is established to perform initial estimates with a reduced dimension. Finally, with the aid of time delay and Doppler shift estimates, DOAs and complex amplitudes of the incoming signals are obtained. Simulation results show that the proposed method can achieve a performance close to CRB at high SNR and with a large number of snapshots
The Analysis of Sophisticated Direction of Arrival Estimation Methods in Passive Coherent Locators
In passive coherent locators (PCL) systems, noise and the precision of direction of arrival (DOA) estimation are key issues. This thesis addresses the implementation of sophisticated DOA estimation methods, in particular the multiple signal classification (MUSIC) algorithm, the conventional beam forming (CBF) algorithm, and the algebraic constant modulus algorithm (ACMA). The goal is to compare the ACMA to the MUSIC, and CBF algorithms for application to PCL. The results and analysis presented here support the use of constant modulus information, where available, as an important addition to DOA estimation. The ACMA offers many simple solutions to noise and separation related problems; at low SNR levels, it provides much more accurate estimates and yields reasonable separation performance even in the presence of challenging signals. Differential ACMA, which allows the simple digital removal of the direct signal component from the output of a sensor array, is also introduced
Towards Unified All-Neural Beamforming for Time and Frequency Domain Speech Separation
Recently, frequency domain all-neural beamforming methods have achieved
remarkable progress for multichannel speech separation. In parallel, the
integration of time domain network structure and beamforming also gains
significant attention. This study proposes a novel all-neural beamforming
method in time domain and makes an attempt to unify the all-neural beamforming
pipelines for time domain and frequency domain multichannel speech separation.
The proposed model consists of two modules: separation and beamforming. Both
modules perform temporal-spectral-spatial modeling and are trained from
end-to-end using a joint loss function. The novelty of this study lies in two
folds. Firstly, a time domain directional feature conditioned on the direction
of the target speaker is proposed, which can be jointly optimized within the
time domain architecture to enhance target signal estimation. Secondly, an
all-neural beamforming network in time domain is designed to refine the
pre-separated results. This module features with parametric time-variant
beamforming coefficient estimation, without explicitly following the derivation
of optimal filters that may lead to an upper bound. The proposed method is
evaluated on simulated reverberant overlapped speech data derived from the
AISHELL-1 corpus. Experimental results demonstrate significant performance
improvements over frequency domain state-of-the-arts, ideal magnitude masks and
existing time domain neural beamforming methods
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey
Integrated sensing and communication (ISAC) has the advantages of efficient
spectrum utilization and low hardware cost. It is promising to be implemented
in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile
communication systems, having the potential to be applied in intelligent
applications requiring both communication and high-accurate sensing
capabilities. As the fundamental technology of ISAC, ISAC signal directly
impacts the performance of sensing and communication. This article
systematically reviews the literature on ISAC signals from the perspective of
mobile communication systems, including ISAC signal design, ISAC signal
processing algorithms and ISAC signal optimization. We first review the ISAC
signal design based on 5G, 5G-A and 6G mobile communication systems. Then,
radar signal processing methods are reviewed for ISAC signals, mainly including
the channel information matrix method, spectrum lines estimator method and
super resolution method. In terms of signal optimization, we summarize
peak-to-average power ratio (PAPR) optimization, interference management, and
adaptive signal optimization for ISAC signals. This article may provide the
guidelines for the research of ISAC signals in 5G-A and 6G mobile communication
systems.Comment: 25 pages, 13 figures, 8 tables. IEEE Internet of Things Journal, 202
The University Defence Research Collaboration In Signal Processing
This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations.
The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour
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Structured Sub-Nyquist Sampling with Applications in Compressive Toeplitz Covariance Estimation, Super-Resolution and Phase Retrieval
Sub-Nyquist sampling has received a huge amount of interest in the past decade. In classical compressed sensing theory, if the measurement procedure satisfies a particular condition known as Restricted Isometry Property (RIP), we can achieve stable recovery of signals of low-dimensional intrinsic structures with an order-wise optimal sample size. Such low-dimensional structures include sparse and low rank for both vector and matrix cases. The main drawback of conventional compressed sensing theory is that random measurements are required to ensure the RIP property. However, in many applications such as imaging and array signal processing, applying independent random measurements may not be practical as the systems are deterministic. Moreover, random measurements based compressed sensing always exploits convex programs for signal recovery even in the noiseless case, and solving those programs is computationally intensive if the ambient dimension is large, especially in the matrix case. The main contribution of this dissertation is that we propose a deterministic sub-Nyquist sampling framework for compressing the structured signal and come up with computationally efficient algorithms. Besides widely studied sparse and low-rank structures, we particularly focus on the cases that the signals of interest are stationary or the measurements are of Fourier type. The key difference between our work from classical compressed sensing theory is that we explicitly exploit the second-order statistics of the signals, and study the equivalent quadratic measurement model in the correlation domain. The essential observation made in this dissertation is that a difference/sum coarray structure will arise from the quadratic model if the measurements are of Fourier type. With these observations, we are able to achieve a better compression rate for covariance estimation, identify more sources in array signal processing or recover the signals of larger sparsity. In this dissertation, we will first study the problem of Toeplitz covariance estimation. In particular, we will show how to achieve an order-wise optimal compression rate using the idea of sparse arrays in both general and low-rank cases. Then, an analysis framework of super-resolution with positivity constraint is established. We will present fundamental robustness guarantees, efficient algorithms and applications in practices. Next, we will study the problem of phase-retrieval for which we successfully apply the sparse array ideas by fully exploiting the quadratic measurement model. We achieve near-optimal sample complexity for both sparse and general cases with practical Fourier measurements and provide efficient and deterministic recovery algorithms. In the end, we will further elaborate on the essential role of non-negative constraint in underdetermined inverse problems. In particular, we will analyze the nonlinear co-array interpolation problem and develop a universal upper bound of the interpolation error. Bilinear problem with non-negative constraint will be considered next and the exact characterization of the ambiguous solutions will be established for the first time in literature. At last, we will show how to apply the nested array idea to solve real problems such as Kriging. Using spatial correlation information, we are able to have a stable estimate of the field of interest with fewer sensors than classic methodologies. Extensive numerical experiments are implemented to demonstrate our theoretical claims
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