Source localization for the nonlinear measurement model based on time difference of arrival (TDOA) measurements remains a vital research area and has been intensively studied for the past few decades. However, the localization accuracy decreases significantly as the random measurement noise becomes large. In addition, when sensors are mounted on moving platforms like vehicles or aircrafts, inevitable sensor position errors might pose more severe challenges on source localization accuracy. This paper proposes to construct a pseudo-linear measurement model that introduces both the TDOA measurement noise and the sensor position error firstly. Next, the constrained total least squares (CTLS) formulation is presented, and the iterative alternating direction method of multipliers (ADMM) is employed to solve the resulting optimization model. Simulation results show that the proposed method can approach Cramer Rao lower bound (CRLB) better and outperforms several existing methods when considering sensor position uncertainties and large TDOA measurement errors.

Time difference of arrival (TDOA) measurements, which are contaminated by large values of error, known as outliers, would have a significant impact on the accuracy of sound source localization (SSL) in wireless acoustic sensor networks (WASNs). Few techniques are reported in the literature to tackle SSL in WASNs by taking TDOA outliers into consideration. To mitigate the effect of outliers on the accuracy of SSL, we propose outlier-resistant robust sound source localization (RSSL) algorithms based on sparse regularization using an unsynchronized network of microphone arrays. The TDOA errors are divided into two components: a) energy-bounded inliers and b) outliers. Assuming that outliers are sparse in the measurement set, we formulate the RSSL problem as that of minimizing the number of outliers, mathematically, a$\ell _{0}$(pseudo)-norm optimization problem with non-convex constraints. Five sub-optimal RSSL solvers are derived, among which the first two solvers are applicable to the scenario involving only outliers while the last three solvers concentrate on the scenario incorporating both outliers and inliers. In common, these solvers exploit a convex approximation technique called Concave Convex Procedure to dispose of the non-convex constraints. Differently, the first solver approximates the original$\ell _{0}$(pseudo)-norm cost function with the$\ell _{1}$norm while a concave surrogate function is adopted in the second solver to yield a tighter approximation to the$\ell _{0}$(pseudo)-norm. Apart from the application of these two approximation techniques, the third and fourth solvers relax the non-convex$\ell _{2}$norm constraint with the$\ell _{\infty }$norm. The fifth solver is dedicated to the$\ell _{1}$norm regularization problem with the Lasso formulation, which is equivalent to the M-estimator of Huber’s function solved via the iteratively reweighted least squares paradigm. Experimental results validate the effectiveness and robustness of the proposed algorithms.

In this paper, we address the multiple sound source localization problem by associating and fusing the direction of arrival (DOA) estimates from multiple microphone arrays. For multi-source scenarios especially in indoor environments, a critical issue is to tell the correspondence among DOA estimates across different arrays, which is known as the data association problem. We propose a multi-dimensional assignment-based data association approach to find the optimal associations of DOA estimates from the same source. First, in the sense of maximum likelihood, the data association problem is formulated by finding the most likely partition of the measurement set into the source-originated and false alarm-originated subsets. Next, by defining the association costs appropriately, the problem of finding the most likely measurement partition is transformed into a generalized multi-dimensional assignment problem which can be solved efficiently by a Lagrangian relaxation algorithm. After the optimal associations of DOA estimates across different arrays are obtained, the locations of sources can be estimated by fusing the same source-originated DOA estimates. In the presence of missed detections, false alarms and the unknown number of sources, the proposed method achieves high accuracy in data association and localization, and outperforms the competing method in reverberant and noisy environments. In addition, since our method does not require additional features and uses DOA estimates only, it is more computationally efficient than the competing method.

In this paper, we address the multiple sound source localization problem using time differences of arrival (TDOAs) of sound sources to a microphone array. Typically, TDOAs are estimated based on the peak extraction of the generalized crosscorrelation function. In multi-source cases, for any given microphone pair, it is hard to tell the correspondence between the sound sources and the extracted peaks. In this work, we develop a novel localization approach based on data association which combines multiple TDOAs from the same source across different microphone pairs. Firstly, the generalized cross correlation-phase transform (GCC-PHAT) function is evaluated and multiple peaks of the GCC function indicating candidate TDOAs are extracted for each pair of microphones. Next, we employ the multi-dimensional assignment algorithm to associate multiple TDOAs from the same source. Finally, multiple sound source localization is carried out based on the obtained TDOA associations across different microphone pairs. Experimental results show the proposed method achieves superior performance for multiple sound source localization compared to the competing algorithm, especially in noisy environments.

In this paper, we address the problem of sound source localization in reverberant environments. Time-delay estimation (TDE) methods are widely employed to locate sound sources based on the time differences of arrival (TDOAs) of signals received at different microphone pairs. In strong reverberations, the highest peak of the localization function is not necessarily from the true source resulting from the multi-path effect. Our previously proposed method based on the optimal peak association (OPA) aims to extract multiple peaks from the localization function for each microphone pair and find out the optimal association of TDOAs corresponding to the same sound source. However, due to the limitation of geometric configuration of microphones and possible missed detections, some microphone pairs fail to provide high-quality TDOA measurements. An improved OPA method is developed in this work based on the robust least squares which can determine the weights adaptively in terms of their respective observation accuracy. Experimental results demonstrate the superiority of the proposed method compared with the original OPA method in reverberant environments.

In this paper, we address the problem of distributed joint detection and estimation, in which numbers of sensor nodes are employed to detect signal-presence or absence, and estimate the unknown parameter associated with the decided hypothesis. Due to the limited bandwidth, each local sensor quantizes its original measurement into one bit of information, and the final global decision is then made based on the quantized data set at the fusion center (FC). Firstly, the multi-sensor joint likelihood function under either hypothesis is evaluated by assuming the data transmission channel between the FC and local sensors are perfect or imperfect, respectively. The expected utility is then introduced to assess the joint performance for distributed detection and estimation tasks. Finally, an optimal estimation receiver operating curve (EROC-opt) decision scheme is employed to accomplish the distributed joint detection and estimation. Performance comparisons with the centralized scheme without quantization and the generalized likelihood ratio test (GLRT) are made to show the superiority of the proposed approach.

In this paper, we consider the source localization problem in which several microphones collaborate to locate an active sound source in a reverberant environment. Sound source localization (SSL) based on the Generalized Cross Correlation (GCC) function is widely studied for the past few decades. However, in a reverberant environment, the maximal peak of the GCC function does not necessarily correspond to the true source location due to the multipath effect. In this case, the traditional GCC-based method performs poorly. In this paper, by combining the information from all the available microphone pairs, we aim to seek a set of source-originated peaks rather than the maximal peaks. To achieve this, for each pair of microphones, multiple peaks of the GCC function indicating candidate TDOAs are extracted firstly. A graphic model is then constructed based on the extracted TDOAs from multiple microphone pairs, and the optimal association of peaks corresponding to true time delays can be obtained by optimizing the association cost function for the given set of peaks. Finally, the source location is estimated in the least square sense. Simulation results show the superior performance of the proposed approach compared with the traditional GCC-based localization algorithm.

This paper addresses the problem of joint detection and estimation fusion when sensor quantized data are correlated in the distributed system. The traditional methods to handle this joint problem tend to treat the detection and estimation tasks separately, which put more emphasis on the detection part but treat the estimation part sub-optimally. In this work, the joint detection and estimation fusion model is described based on the idea of multi-objective optimization, providing attainable flexibility between the detection and estimation performance. There into, the critical joint likelihood function for multi-sensor correlated data is evaluated based on the Copula theory. Simulation results show that the copula-based joint model outperforms the dependency-ignoring model. Further, the proposed approach is superior to the comparative GLRT (Generalized likelihood ratio test) and NP (Neyman-Pearson) in term of the average estimation cost.

This paper deals with sound source localization and number estimation in indoor environments using a circular microphone array. Multiple sound source localization is achieved by performing single source localization at each selected time-frequency (TF) point of received signals after short-time Fourier transform. A TF point selection method is proposed to reduce the computational time, which depends on a trained SVM with power and power ratio of TF points as its features. Nonparametric Bayesian clustering is applied on the obtained DOA estimates to identify the number of active sources. The algorithm is shown to outperform others through simulations.

A computationally effective approach is developed in this study to deal with the problem of track fusion in the presence of sensor biases. Aiming at the case that sensor biases are implicitly included in the local estimates, a pseudo‐measurement equation is derived based on the Taylor series expansion firstly, which reveals the relationship explicitly between local estimates and the sensor biases; and then, the bias estimates can be obtained in the rule of recursive least squares; finally, based on the derived pseudo‐measurement equation, the sensor biases can be removed from the original local estimates and track fusion can be carried out directly and easily. Monte Carlo simulations demonstrate the efficiency and effectiveness of the proposed approach compared with the competing algorithms.

Due to the astonishing development in memory and processing capabilities of hand held devices such as smart phones, it is not a dream anymore to enable mobile devices not only as conventional web service requesters but even as providers. The willingness and enthusiasm of service providers place abundant services at the disposal. But this abundance makes the efficiency of service discovery a critical issue. Centralized registries have severe drawbacks in such a scenario due to the dynamic and spontaneous nature of mobile peers. In the quest for a more appropriate approach for mobile web service discovery, we observed P2P to share very similar characteristics with behaviors of peers in mobile network. Hence we tried to find alternate mobile web service discovery mechanisms by using the features of the P2P networks like JXTA modules. The scalability analysis of the approach proves that the discovery can scale to the needs of large cellular networks.

In the condition of sensor network (SN), to associate local tracks front multiple sensors is a complex task, due to the combination explosion caused by the increasing number of sensors and targets. A new graphical models-based technique for track association is proposed in this paper to deal with the problem. Firstly, by means of the sparse structure inherent in multisensor multitarget tracking scenario, the graphical structure for track association is established; Secondly, the compatibility function of nodes and edges in graph is properly defined to describe the objective function with constrains about track association problem; finally, max-product message passing scheme is employed to obtain the optimal association result. Simulation results demonstrate the efficiency of the presented method.

Data association is one of the key techniques on bearing-only tracking with infrared search and track (IRST) system. A new data association algorithm based on information fusion is proposed in this paper. Firstly, by considering the special feature of IRST system, the new method constructs several kinds of evidences that are based on the multi-type information, such as angular measurement, intensity level and so on. And then all results from different aspects are fused by utilizing Dempster combination rule. Finally, the association decision is obtained by maximizing the final mass function. The simulation results show that the proposed approach has better performance than the traditional ones. As observation conditions deteriorate, the advantage of new method becomes obvious. For uncertain data, this new data association algorithm based on evidence theory is excellent by utilizing reasonably the measurements