Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

Multiple source localization using spherical microphone arrays

Direction-of-Arrival (DOA) estimation is a fundamental task in acoustic signal processing and is used in source separation, localization, tracking, environment mapping, speech enhancement and dereverberation. In applications such as hearing aids, robot audition, teleconferencing and meeting diarization, the presence of multiple simultaneously active sources often occurs. Therefore DOA estimation which is robust to Multi-Source (MS) scenarios is of particular importance. In the past decade, interest in Spherical Microphone Arrays (SMAs) has been rapidly grown due to its ability to analyse the sound field with equal resolution in all directions. Such symmetry makes SMAs suitable for applications in robot audition where potential variety of heights and positions of the talkers are expected. Acoustic signal processing for SMAs is often formulated in the Spherical Harmonic Domain (SHD) which describes the sound field in a form that is independent of the geometry of the SMA. DOA estimation methods for the real-world scenarios address one or more performance degrading factors such as noise, reverberation, multi-source activity or tackled problems such as source counting or reducing computational complexity. This thesis addresses various problems in MS DOA estimation for speech sources each of which focuses on one or more performance degrading factor(s). Firstly a narrowband DOA estimator is proposed utilizing high order spatial information in two computationally efficient ways. Secondly, an autonomous source counting technique is proposed which uses density-based clustering in an evolutionary framework. Thirdly, a confidence metric for validity of Single Source (SS) assumption in a Time-Frequency (TF) bin is proposed. It is based on MS assumption in a short time interval where the number and the TF bin of active sources are adaptively estimated. Finally two analytical narrowband MS DOA estimators are proposed based on MS assumption in a TF bin. The proposed methods are evaluated using simulations and real recordings. Each proposed technique outperforms comparative baseline methods and performs at least as accurately as the state-of-the-art.Open Acces

DOA Estimation of a Wideband Signal Using a 2-D Array Antenna with Spatial Processing Capability

This paper describes investigations into Direction–Of–Arrival (DOA) estimation of a wideband signal by a two–dimensional array antenna, which employs only spatial signal processing for beam forming. The elements of this array are arranged in a horizontal rectangular lattice to steer a beam in azimuth over a wide frequency band. By applying the concept of interpolated array, a composite covariance matrix is produced. This composite covariance matrix is a simple addition of covariance matrices of narrowband virtual arrays, being stretched or compressed versions of a nominal array, all featuring the same radiation pattern. DOA is estimated by eigen–decomposition of the composite covariance matrix using the narrowband MUSIC algorithm. The performance of the proposed DOA estimation method is demonstrated by computer simulations. The obtained results indicate that the two–dimensional array provides better estimation of DOA than the one–dimensional one when the interpolated array technique in conjunction with the MUSIC algorithm is applie

Underdetermined DOA Estimation Under the Compressive Sensing Framework: A Review

Direction of arrival (DOA) estimation from the perspective of sparse signal representation has attracted tremendous attention in past years, where the underlying spatial sparsity reconstruction problem is linked to the compressive sensing (CS) framework. Although this is an area with ongoing intensive research and new methods and results are reported regularly, it is time to have a review about the basic approaches and methods for CS-based DOA estimation, in particular for the underdetermined case. We start from the basic time-domain CSbased formulation for narrowband arrays and then move to the case for recently developed methods for sparse arrays based on the co-array concept. After introducing two specifically designed structures (the two-level nested array and the co-prime array) for optimizing the virtual sensors corresponding to the difference coarray, this CS-based DOA estimation approach is extended to the wideband case by employing the group sparsity concept, where a much larger physical aperture can be achieved by allowing a larger unit inter-element spacing and therefore leading to further improved performance. Finally, a specifically designed ULA structure with associated CS-based underdetermined DOA estimation is presented to exploit the difference co-array concept in the spatio-spectral domain, leading to a significant increase in DOFs. Representative simulation results for typical narrowband and wideband scenarios are provided to demonstrate their performance

Comparative study between Direction of arrival for wide band & narrow band Signal using Music Algorithm

Direction of arrival is a key parameter in array signal processing. It is one of the important problem in field such as sonar, radar and wireless communication. Traditional DOA estimation algorithm consists of large no of snapshot and are not reliable in application such as underwater array processing. There are many sources such as seismic wave ,acoustic signals, speech and signal processing which is wide band signal and estimation parameters such as snapshot ,side lobes, resolution is an important task. In the recent advancement of technology wide band signal are more favoured over narrow band signals. Wide band signal are able to estimate  DoAs efficiently with less side lobes and snapshots. In this paper a comparative analysis of direction of arrival for wide band and narrow band by analysing angular spectrum of MUSIC algorithm. We will   estimate the position of spectral with different scanning grid size. We will search the spectral peak position and estimates final DOA Therefore it become important to study and analyzed wide band signal specially application such as 5G m-MIMO systems

Comparative study between Direction of arrival for wide band & narrow band Signal using Music Algorithm

Direction of arrival is a key parameter in array signal processing. It is one of the important problem in field such as sonar, radar and wireless communication. Traditional DOA estimation algorithm consists of large no of snapshot and are not reliable in application such as underwater array processing. There are many sources such as seismic wave ,acoustic signals, speech and signal processing which is wide band signal and estimation parameters such as snapshot ,side lobes, resolution is an important task. In the recent advancement of technology wide band signal are more favoured over narrow band signals. Wide band signal are able to estimate  DoAs efficiently with less side lobes and snapshots. In this paper a comparative analysis of direction of arrival for wide band and narrow band by analysing angular spectrum of MUSIC algorithm. We will   estimate the position of spectral with different scanning grid size. We will search the spectral peak position and estimates final DOA Therefore it become important to study and analyzed wide band signal specially application such as 5G m-MIMO systems