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Real-time source localization test for the moving multiple quadcopter drones. Spatial scene analysis using an open-access audio dataset for machine learning. [Display omitted] • A novel visual representation method for multi-dimensional sound scene analysis. • Representation of the estimated localization result as RGB color channels. • Encoding with any localization algorithm in the preprocessing. • Possibility of adopting any localization method in the preprocessing for encoding. • Human-interpretable dataset capable of quantitative analysis with decoding. • Exported image file including metadata for decoding. A visual representation method, DoAgram, for multi-dimensional sound scene analysis is suggested. The visual representation of the sound source localization result gives intuitive information about the estimated one to the end user. Also, image-based deep learning is popularly used in the acoustic field nowadays, so such a visualized one can be used for data augmentation. To analyze the spatial sound scene for the moving source, the method displays the estimated azimuth angle and elevation angle of the source, and its corresponding time stamp and frequencies as RGB color channels and metadata by mapping the spatial coordinate to color space. Even though the suggested method is human-interpretable, decoding is needed for the quantitative analysis. Therefore, the time and frequency scanning method, and a histogram to estimate the DoA of the source are proposed. An experiment is conducted in an anechoic chamber to localize two quadcopter drones that have a mean angular velocity of 8°/s ± 9°/s (95 % CI) and 25°/s ± 31°/s (95 % CI), respectively, and the spatial sound scene analysis is implemented using the proposed methods. The test result shows that the trajectories with respect to the time of each source are well separated. Also, an additional test is conducted using an open-access audio dataset for machine learning. The cumulative source mapping method is adopted for the spatial sound scene analysis, and the decoded result shows that the DoAgram is feasible to adopt for machine learning applications. [ABSTRACT FROM AUTHOR]

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

The localization of targets has been presented in this article. DOA (Direction of Arrival) is an important parameter to be determined by radar. The MLE (Maximum Likelihood Estimator) has been widely used to accurately and efficiently estimate the DOAs of multiple targets. The targets at different ranges result in a variation in amplitude of the received signals, so an MLE estimator has to operate at all ranges. For accurate results of DOA, the complex amplitudes of multiple targets should not be much different and also the prior information of Doppler and number of targets is required. In this paper, an approach is proposed which uses the classical 2D algorithm to estimate range, Doppler and number of targets and then FDA (Frequency Diversity Array) is used to focus power in a particular range. As a result, the MLE can get data from a particular range cell where all targets have almost same amplitude and thus MLE can accurately estimate the DOAs of multiple targets. Simulations and results have confirmed the effectiveness of proposed approach.

We present an algorithm to estimate direction of arrival (DOA) of an incoming wave received at an array antenna in the scenario where the incoming wave is contaminated by the additive white Gaussian noise and scattered by arbitrary shaped 3D scatterer(s). We present different simulation examples to show the validity of the proposed method. It is observed that the proposed algorithm is capable of closely estimating the DOA of an incoming wave irrespective of the shape of the scatterer provided the decision is made over multiple iterations. Moreover, presence of noise affects the estimate especially in the case of low signal-to-noise ratio (SNR) that gives a relatively large estimation error. However, for larger SNR the DOA estimation is primarily dependent on the scatterer only.

The paper demonstrates the use of frequency reassignment for bearing estimation. For this task, signals derived from a linear equispaced passive array are used. The presented method makes use of Fourier transformation based spatial spectrum estimation. It is further developed through the application of two-dimensional reassignment, which leads to obtaining highly concentrated energy distributions in the joint frequency-angle domain and sharp graphical imaging. The introduced method can be used for analysing, a priori, unknown signals of broadband, nonstationary, and/or multicomponent type. For such signals, the direction of arrival is obtained based upon the marginal energy distribution in the angle domain, through searching for arguments of its maxima. In the paper, bearing estimation of three popular types of sonar pulses, including linear and hyperbolic frequency modulated pulses, as well as no frequency modulation at all, is considered. The results of numerical experiments performed in the presence of additive white Gaussian noise are presented and compared to conventional digital sum-delay beamforming performed in the time domain. The root-mean-square error and the peak-to-average power ratio, also known as the crest factor, are introduced in order to estimate, respectively, the accuracy of the methods and the sharpness of the obtained energy distributions in the angle domain.

A direction of arrival based (DoA-based) beamforming of massive MIMO was proposed for high-speed railway scenarios.In order to guarantee that the optimal system capacity can be obtained during traveling,an iterative multi-beams scheme was proposed.It aimed to design different beams for different mobile carriage terminals (MCT) respectively.The proposal adaptively selected the number of antennas for each beam,and adaptively designed the number of beams according to system capacity.Moreover,the scheme didn’t need channel state information (CSI) that can reduce system implementation complexity and cost.Numerical results indicate that the proposed scheme is suitable for high-speed scenarios,the system performance can be improved more efficiently compared to conventional methods.

Abstract A novel algorithm is presented based on sparse multiple measurement vector (MMV) model for direction of arrival (DOA) estimation of far-field narrowband sources. The algorithm exploits singular value decomposition denoising to enhance the reconstruction process. The proposed multiple nature of MMV model enables the simultaneous processing of several data snapshots to obtain greater accuracy in the DOA estimation. The DOA problem is addressed in both uniform linear array (ULA) and nonuniform linear array (NLA) scenarios. Superior performance is demonstrated in terms of root mean square error and running time of the proposed method when compared with conventional compressed sensing methods such as simultaneous orthogonal matching pursuit (S-OMP), l 2,1 minimization, and root-MUISC.

This study focuses on the problem of power spectra separation and localisation of multiple sources using distributed arrays. First, the array structure and signal model are discussed. By cross-correlating the multi-channel received signals in time-domain, a third tensor is constructed. Then, utilising the multi-dimensional characteristic, the tensor is decomposed to separate the array manifold matrix and the power spectra matrix through alternating least square (ALS) method. Finally, the sources are located using the relative x-y plane relationship between the distributed arrays and the direction of arrival (DOA), which can be estimated by spectrum analysis of each column of the array manifold matrix. The effectiveness and superiority of the proposed method is demonstrated by simulation results.

Underwater gliders travel through the ocean by buoyancy control, which makes their motion silent and involves low energy consumption. Due to those advantages, numerous studies on underwater acoustics have been carried out using gliders and different acoustic payloads have been developed. This paper aims to illustrate the use of gliders in underwater acoustic observation and target detection through experimental data from two sea trials. Firstly, the self-noise of the glider is analyzed to illustrate its feasibility as an underwater acoustic sensing platform. Then, the ambient noises collected by the glider from different depths are presented. By estimating the transmission loss, the signal receiving ability of the glider is assessed, and a simulation of target detection probability is performed to show the advantages of the glider over other underwater vehicles. Moreover, an adaptive line enhancement is presented to further reduce the influence of self-noise. Meanwhile, two hydrophones are mounted at both ends of the glider to form a simple array with a large aperture and low energy consumption. Thus, the target azimuth estimation is verified using broadband signals, and a simple scheme to distinguish the true angle from the port‒starboard ambiguity is presented. The results indicate that the glider does have advantages in long-term and large-scale underwater passive sensing. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of power spectra separation and localisation of multiple sources using distributed arrays. First, the array structure and signal model are discussed. By cross-correlating the multi-channel received signals in time-domain, a third tensor is constructed. Then, utilising the multi-dimensional characteristic, the tensor is decomposed to separate the array manifold matrix and the power spectra matrix through alternating least square (ALS) method. Finally, the sources are located using the relative x-y plane relationship between the distributed arrays and the direction of arrival (DOA), which can be estimated by spectrum analysis of each column of the array manifold matrix. The effectiveness and superiority of the proposed method is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

Response and recovery are the two most crucial aspects associated with post disaster management. Both these operations need real-time data and location at which the event occurs. These operations require event-based collection of data at critical times. The events may occur at any place and the data may be needed from either or all of the events for strategic planning and event handling. It is a challenging task to process the events whose locations are uncertain. In this view, there is a need to know the real-time location of events occurring in the surrounding. Moreover, there is a possibility of huge amount of signal processing among the devices deployed in the terrain. In this context, an event localization algorithm is proposed based on the Direction of Arrival estimation technique in the Internet of Things environment. It estimates the location of events by mapping the deployed devices using concentric circular array in the region. Further, the Cramer–Rao bound for the proposed algorithm is derived and compared with the existing schemes for efficacy. The algorithm is implemented on real test bed and presented with comparative evaluation to validate the work. [ABSTRACT FROM AUTHOR]

In this paper, an idea from the quasi-maximum-likelihood (QML) algorithm has been applied to estimation of the direction of arrival (DOA) and parameters of the polynomial phase signals (PPS) impinging on the uniform linear array. The algorithm uses a search over a set of possible DOA values. For considered value, the DOA is compensated and resulted signal parameters are estimated using the original QML approach. In this way, several sets of potential estimates are obtained. Optimal DOA and PPS parameters belong to set that maximizes the proposed cost function. In the proposed approach, parameters are estimated using 2-D search over DOA and window widths used for calculation of the short Fourier transform in the QML that is an important advantage with respect to existing strategies like, for example, the polynomial beamformer requiring multi-dimensional or metaheuristic search strategies. Numerical validation of the proposed approach for both mono- and multi-component signals is provided in the paper. Both far- and near-field scenarios are considered. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of joint two-dimensional direction of arrival (2-D DOA) and frequency estimation via reduced-dimensional propagator method (RD-PM) with L-shaped array. The proposed algorithm has no need for eigenvalue decomposition of the sample covariance matrix and simplifies three-dimensional global spectral search within the three-dimensional propagator method (3-D PM) to one-dimensional local search, which greatly reduces computational complexity. Furthermore, the proposed algorithm can work under both uniform and non-uniform L-shaped array and can achieve paired 2-D DOA and frequency estimates automatically. In addition, the 2-D DOA and frequency estimation performance for the proposed method is approximate 3-D PM algorithm and parallel factor (PARAFAC) method but exceeds the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and improved PM algorithm. The detailed derivation of Cram´er-Rao bound (CRB) is provided and the simulation results demonstrate the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Parameter estimation of plane waves emitted by sources lying in Fraunhofer zone is one of the active areas of research for last few decades. In this study, Fractional Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm is designed for direction of arrival and amplitude estimation of plane waves impinging on uniform linear array representing the scenario of monostatic Multiple Input and Multiple Output (MIMO) radar. Approximation theory in the mean square error sense is exploited to develop a fitness function of the problem. Design parameters of the system model are optimized by utilizing the strength of the FO-DPSO algorithm in case of various numbers of non-coherent sources, and analysis is performed in terms of fitness, mean square error, Nash–Sutcliffe efficiency, and computational complexity operators. Worth of the proposed FO-DPSO based optimization mechanism is established by consistently achieving the near to optimal values of performance metrics in all three scenarios for monostatic MIMO radar systems. [ABSTRACT FROM AUTHOR]

We propose Weighted SBL (WSBL) for sparse signal recovery, inspired by the Sparse Bayesian Learning (SBL) method. Unlike SBL, where all hyperparameter priors follow Gamma distributions with identical parameters, in WSBL, the hyperparameters are Gamma distributed with distinct parameters. These parameters, guided by some known weights, give more importance to some hyperparameters over others, thus introducing more degrees of freedom to the problem and leading to better recovery performance. The weights can be determined based on a low-resolution estimate of the sparse vector, for example, an estimate obtained via a method that does not encourage sparsity. The choice of the MUSIC estimate as weight is analyzed. Unlike in SBL, the WSBL hyperparameters are upper bounded; this makes it easy to select a threshold to separate zero from non-zero elements in the recovered sparse vector, which makes the iterative recovery process converge faster. Theoretical analysis based on variational approximation theory and also simulation results demonstrate that WSBL results in substantial improvement in terms of probability of detection and probability of false alarm, especially in the low signal to noise ratio regime, as compared to existing approaches, such as SBL, Sparse Bayesian Support knowledge (BSN), and Multiple response Sparse Bayesian Learning (MSBL). The performance of WSBL is evaluated for Direction of Arrival (DOA) estimation in colocated Multiple Input Multiple Output (MIMO) radar. [ABSTRACT FROM AUTHOR]

The paper presents and discusses a method of azimuth determination of ultrasonic echo arrival in air. The basis of the presented approach is the assumption that the received signal is a narrowband one. In this way, the direction of the signal arrival can be determined based on its phase shift using two receivers. When the distance between the receivers exceeds half of the wavelength of the received signal, a problem of ambiguity in determining the angle of arrival arises. To solve this, a method using multiple pairs of receivers was used. Its robustness and temperature dependence is analysed. The most important advantages of the presented approach are simplified computations and low hardware requirements. Experimental data made it possible to show that for strong echoes, the accuracy is higher than 0.5X. In the case of weak echos, it is reduced to about 2X. Because the method is based on phase shift measurement, the ultrasonic sonar that uses this method can be compact in size. Moreover, owing to the theoretical analysis, certain properties of the mutual location of the receivers were found and formally proved. They are crucial for determining proper receivers' inter-distances. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a Taylor expansion multiple signal classification (TE MUSIC) method for joint direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic multiple-input multiple-output (MIMO) array. First, using a Taylor expansion of the steering vector, a two-dimensional (2D) search in the conventional MUSIC method for MIMO arrays is reduced to a two-step one-dimensional (ID) search in the proposed TE MUSIC method. Second, DOAs of the targets can be achieved via Lagrange multiplier by a ID search. Finally, substituting the DOA estimates into the 2D MUSIC spectrum function, DODs of the targets are obtained by another ID search. Thus, the DOD and DOA estimates can be automatically paired. The performance of the proposed method is better than that of the MIMO ESPRIT method, and is similar to that of the 2D MUSIC method. Furthermore, due to the ID search, the TE MUSIC method avoids the high computational complexity of the 2D MUSIC method. Simulation results are presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, the authors address the problem of passive mixed near‐field and far‐field sources localisation using a uniform linear array (ULA) in which the signals received by the array may come from mixed sources. This study presents a new two stage cumulant‐based multiple signal classification (MUSIC) algorithm for passive source localisation using fourth‐order cumulants of a ULA data. The significant characteristic of the proposed algorithm is that it constructs a new special cumulant matrix to acquire more information of signals received by a ULA. Consequently, the proposed algorithm gives high direction of arrival (DOA) and range estimation accuracy, and alleviates the array aperture loss. Monte Carlo simulations are established to verify the effectiveness of the proposed method in increasing direction of arrival and range estimation accuracies. [ABSTRACT FROM AUTHOR]

In order to solve the angle estimation problem of coherent sources under the non-stationary random noise background, an algorithm based on correlation vector Teoplitz reconstruction and oblique projection is proposed. First, transform non-stationary noise into Gauss white noise by correlation vector Toeplitz reconstruction. Then the oblique projection method is used to separate independent and correlated sources. Finally, the conventional algorithm is adopted to estimate the DOA and DOD of bistatic MIMO radar. The algorithm has no loss of array aperture and better angle estimation performance in the case of low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

This paper presents a 2.4 GHz electronically steerable antenna array (ESAA) using a phase shifter based on the 4x4 Butler matrix which is suitable for the wireless indoor positioning system (WIPS). The low cost and simplicity are advantages of the proposed ESAA. The main beam of the antenna can be steered at four positions in the elevation plane, from -37°, -12°, 12° to+36° with the narrow beamwidth in order to increase localization accuracy and wide-angle coverage of 240° in the azimuth plane. The ESAA is demonstrated as a base station antenna in a single-anchor indoor localization system experimentation. Two indoor localization algorithms, including fingerprinting and direction-of-arrive (DoA), are implemented and discussed in this paper. The indoor measurement setups and localization results using the proposed ESAA are proven in this paper to have better results than other similar approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance of the existing direction of arrival (DOA) estimation algorithms for source localization in the wireless sensor networks (WSN) degrades when the sources are correlated. The degradation is due to the rank deficiency of the source covariance matrix which is reflected in the high cramer-Rao lower bound (CRLB) for correlated sources. Unlike subspace based techniques, maximum likelihood (ML) based technique does not require any preprocessing technique for DOA estimation of correlated signals. Hence ML technique can be directly applied for WSN with arbitrary array geometry. The DOA estimation accuracy for correlated signals is improved by employing distributed ML approach in this paper. The subarray formed at a particular node which is experiencing highest CRLB can improve its estimation accuracy by receiving better DOA estimates from its neighbors. The corresponding distributed CRLB is derived and found a substantial improvement in the distributed scenario. The distributed CRLB lies between that of local CRLB for the subarray formed at the node and global CRLB for the array formed by all sensors in the WSN. Diffusion quantum particle swarm optimization is used to optimize ML estimator for fully correlated signals as it has only a single parameter for tuning. Simulation results show that the estimation accuracy improves at a node even for correlated signals yielding highest CRLB using distributed approach. The root mean square error at a specific node using distributed algorithm approaches to the derived distributed CRLB. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose an effective classification and localization algorithm of mixed far-feld and near-field sources using a uniform circular array under an unknown mutual coupling. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. The effects of unknown mutual coupling would degrade the performance of most high resolution algorithms. Firstly, according to rank reduction type method, the direction of arrival of far-field sources is estimated directly without mutual coupling elimination. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through MUSIC search after mutual coupling effects and far-field components elimination. The proposed algorithm only requires the second order cumulant and any three dimensional spectrum search is circumvented. Some simulation results would prove that the proposed algorithm can reduce more than eighty percent estimating error of mixed sources localization compared to those algorithms without mutual coupling compensation. [ABSTRACT FROM AUTHOR]

Highlights • The elements squared of array covariance are exploited for non-coherent DOA estimation. • The nonlinear model is approximately cast as a linear model under a high-power reference source. • A simple but effective strategy is designed to avoid ambiguities. • The nested CADiS is adopted to enhance the number of DOFs greatly. • The algorithm performs independent of phase errors and can provide improved performance. Abstract This fast communication presents a non-coherent direction of arrival (DOA) estimation algorithm utilizing the elements squared of the array covariance. Very different from previous non-coherent DOA estimation algorithms, the nonlinear model produced by the squared operation is approximately cast as a linear model, under a high-power reference source. And then, the DOA estimation is efficiently obtained via the mature ℓ 1 -norm minimization. The proposed algorithm can be applied to arbitrary arrays. Focused on the estimation accuracy, the nested coprime array with displaced subarrays (CADiS) is exploited. To resolve the ambiguity problems in the scenario that the sources impinging on the array from the range [ − 90 ∘ , 90 ∘ ] , a simple but effective strategy is introduced. The proposed algorithm performs independent of the phase errors and can provide improved DOA estimation accuracy with reduced computational complexity in large snapshot case. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simultaneously determining the 3-dimensional (3-D) position and orientation of a rigid object is termed rigid body localization (RBL). The RBL has potential applications in the systems of virtual reality, spacecraft docking, and so on. In this letter, a new RBL scheme based on Direction of Arrival (DoA) measurements is proposed, which needs only a single base station (BS) and has no requirement for time synchronization between the target of interest and BS. For determining the position and orientation, the rigid object of interest has wireless sensors mounted on its surface, which are distributed with known topology. We first build a geometrical model fusing the measured DoAs from the wireless sensors and their known topology to determine the 3-D coordinates of these sensors. Then, using the obtained coordinate information, we achieve the RBL via rigid body transform methodologies. The constrained Cramer–Rao bound is derived to evaluate the performance of the developed method with respect to the DoA noise level and rigid body size. [ABSTRACT FROM AUTHOR]

Localizing audio sources is challenging in real reverberant environments, especially when several sources are active. We propose to use a neural network built from stacked convolutional and recurrent layers in order to estimate the directions of arrival of multiple sources from a first-order Ambisonics recording. It returns the directions of arrival over a discrete grid of a known number of sources. We propose to use features derived from the acoustic intensity vector as inputs. We analyze the behavior of the neural network by means of a visualization technique called layerwise relevance propagation. This analysis highlights which parts of the input signal are relevant in a given situation. We also conduct experiments to evaluate the performance of our system in various environments, from simulated rooms to real recordings, with one or two speech sources. The results show that the proposed features significantly improve performances with respect to raw Ambisonics inputs. [ABSTRACT FROM AUTHOR]

Abstract This study developed an 8-channel microphone array to track the location of a sound source in real time. In addition, a system that implements the GCC–PHAT function as the main algorithm was developed to measure the direction of arrival (DoA) in four spaces with different space volumes and reverberation times (RTs). The developed system exhibited usability in indoor environments with high background noises as it extracted DoA with errors lower than 3° when the signal-to-noise ratio (SNR) was higher than 5 dB for white noise. In addition, as is generally known, the accuracy of a localization system decreases as the space volume and RT increase. However, a higher DoA accuracy was observed in large spaces such as concert halls, in which the RT is high but early reflective surfaces are located far away, compared with that in spaces such as lecture rooms, in which the RT is relatively low and early reflections from nearby furniture and walls. Speech intelligibility was the highest when strong early reflections within 20 dB (compared with direct sound) were formed in the error range of ±3.2°. Furthermore, it was verified that the system was most effective when the frame length, which is a real-time sound source analysis parameter, was 5.8 ms to extract a small amount of operation and the amount of DoA information within the same time period through the evaluation results in the four spaces. Highlights • Investigated impacts of building environment on a DoA estimation. • Developed 8-channel linear microphone array and GCC-PHAT-based system. • Evaluated DOA characteristics according to space volume and reverberation. • Optimal frame length for the real-time system was selected as 5.3 ms • Strong early reflections within 20 dB have large impacts on speech intelligibility. [ABSTRACT FROM AUTHOR]

Abstract A procedure of calibration of a custom 3D acoustic vector sensor (AVS) for the purpose of direction of arrival (DoA) estimation, is presented and validated in the paper. AVS devices working on a p-p principle may be constructed from standard pressure sensors and a signal processing system. However, in order to ensure accurate DoA estimation, each sensor needs to be calibrated. The proposed algorithm divides the calibration process into two stages. First, amplitude calibration is performed in order to compensate for amplitude differences between pairs of microphones situated on each axis. After the pressure and velocity signals are computed from the corrected microphone signals, the second stage is performed in order to correct phase differences between the pressure and velocity signals, which then allows for computing the intensity signals for each axis. In order to validate the calibration method, a reference AVS was constructed from low cost components, namely MEMS microphones and a DSP board. The method was engineered with an assumption that it will be applicable to any AVS working on the same principle. A set of experiments was performed in order to validate the calibration method and to compare the accuracy of the calibrated sensor with a commercial AVS. It was found in the experiments that DoA accuracy of the proposed 3D AVS calibrated with the proposed procedure matches that of a commercial, high cost, factory-calibrated sensor. Therefore, the proposed calibration method fulfills the requirements of accurate DoA estimation, and it is applicable to calibration of custom built, low cost AVS devices, that may be implemented in practical applications for determining the direction of sound sources, such as environmental monitoring, traffic monitoring and public security systems. [ABSTRACT FROM AUTHOR]

In order to estimate the frequency and DOA of an incident signal with sub-Nyquist spatio-temporal sampling,A novel joint estimation algorithm based on spectrum correction and Chinese remainder theorem(CRT)was proposed.Firstly,the incident signal was parallelly sampled at multiple sensors non-uniformly located in a sparse linear array.Then,AM spectrum corrector was employed to extract the rema of frequency and phase-difference from a small quantity of snapshots.Finally,implementing the close-form CRT on these remainders directly yields the estimates of frequency and DOA.In the proposed scheme,the spectrum corrector was shared by both the frequency estimator and the DOA es-timator,which contributed to a high utilization of the snapshots.Furthermore,compared to the existing CRT based esti-mators,the proposed estimator needs not to incorporate multiple times of temporal undersampling on a single sensor,which saves considerable time consumption and thus obtain high ability of tracking time-variant objects.Simulations verify the feasibility and the high-accuracy of the proposed joint estimator and further study shows that high reconstruc-tion probability can be realized even in low SNR cases,which present the vast potential for future development.