Multireference TDOA-based source localization

This paper presents an automatic sound source localization approach based on a combination of the basic time delay estimation sub-methods namely, Time Difference of Arrival (TDOA), and Steered Response Power (SRP) methods. The TDOA method is a fast but vulnerable approach for finding the sound source location in long distances and reverberant environments and is so sensitive in noisy situations. On the other hand, the conventional SRP method is time consuming, but a successful approach to accurately find sound source location in noisy and reverberant environments. Also, another SRP-based method, SRP Phase Transform (SRP-PHAT), has been suggested for the better noise robustness and more accuracy of sound source localization. In this paper, based on the combination of TDOA and SRP based methods, two approaches were proposed for sound source localization. In the first proposed approach called Classical TDOA-SRP, the TDOA method is used to find the approximate sound source direction and then SRP based methods were used to find the accurate location of sound source in the Field of View (FOV) which is obtained by the TDOA method. In the second proposed approach which called Optimal TDOA-SRP, for more reduction of computational processing time of SRP-based methods and better noise robustness, a new criterion has been proposed for finding the effective FOV which is obtained through the TDOA method. Experiments were carried out under different conditions confirming the validity of the purposed approaches.

Accurate localization of sound sources is indispensable for high‐quality sound capture. The difference of distance from a sound source to two microphones causes a time delay of arrival (TDOA). The cross‐power spectrum phase analysis (CSP) method has been proposed to estimate the TDOA based on correlation in signals. The sound source can be localized theoretically as a crossing point of sound directions estimated using two pairs of microphones. However, in a real environment, microphones receive sounds that have originated through reverberation and reflection. This situation makes TDOA estimation difficult, and accurate sound localization is difficult when using only two pairs of microphones. The authors propose a new method of sound localization based on multiple microphones that are widely distributed and placed under the ceiling of a room. A number of pairs of microphones give a series of CSP correlation coefficients as a function of the time delay. The CSP coefficients for hypothetical sound sources are accumulated over all the microphone pairs. The number of microphones is 16. The sound source is localized as a hypothesis that maximizes the accumulated CSP coefficient. Experiment results obtained in a real room show that the proposed method improves the localization accuracy.

In this paper, we propose a cooperative localization method for moving source using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. Research on cooperative localization of moving source is an important topic to solve the passive source localization problem in different fields, such as radar, sonar, navigation, wireless communications and so forth. We investigate the improvement in source estimation accuracy. In the cooperative localization structure, a cooperative localization method based on quasi-Newton (QN) approach and two-step weighted least-squares (TSWLS) method is designed. The research results prove that the source localization accuracy has been improved effectively by using cooperative localization method based on QN-TSWLS method.

Receiver location uncertainty plays an important role in passive source localization since it can significantly deteriorate the accuracy of the source location. In this paper, we first derive a joint position estimation model for source location and receiver positions with erroneous receiver positions, and then, based on this model, an efficient algorithm for estimation the location of source and the positions of receivers using Time Difference of Arrival (TDOA) measurements is proposed. The proposed method employs modified Genetic Algorithm (MGA) and jointly estimates both source and receiver positions simultaneously. Simulation results show that when receiver position or TDOA measurement errors are large, the proposed method can achieve better performance than the modified Taylor method and the original Taylor method. It is more suitable for adverse measurement conditions.

Source localisation using time difference of arrival (TDOA) measurements has drawn considerable attention in the past few decades. The presence of outliers in TDOAs could deteriorate the localisation performance significantly. Under the reasonable assumption that outliers are sparse and they do not dominate in the raw measurement set, a computationally efficient outlier‐robust TDOA localisation method is proposed in this study. It integrates the half‐quadratic minimisation and reweighted least absolute shrinkage and selection operator to iteratively identify the outliers and find the source location estimate using the TDOA inliers only. Both additive and multiplicative forms of the proposed method are established. Simulation results demonstrate the computational efficiency and effectiveness of the developed algorithms.

Uncertainty in sensor (receiver) locations can seriously deteriorate the source localization accuracy. A localization algorithm needs to take the sensor location errors into account in order to improve source location estimate. This paper considers the localization for the position and velocity of a moving source using the time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a signal received at a number of sensors that have random location errors. The proposed method is based on the Taylor-series method that jointly estimates both the source and sensor locations simultaneously. Simulation results show that with a reasonable initial guess close to the true solution to begin with, the proposed method converges to the true solution and achieves the CRLB accuracy for both near-field and far-field sources.

This paper considers the problem of moving source localization by introducing an additional Doppler frequency shift (DFS) measurement into the previous location system using time differences of arrival (TDOAs) and frequency differences of arrival (FDOAs). Because of the separation of associated data channels and the difference in the estimation techniques, the DFS is independent of TDOA and FDOA measurements. The Cramer-Rao lower bound (CRLB) analysis shows that introducing DFS can improve source localization accuracy comparing with that of the TDOA and FDOA localization system, especially for the velocity location accuracy. An improved two-step weighted least-squares (I-TSWLS) source localization method utilizing DFS as well as TDOA and FDOA is developed. The solution is shown analytically to achieve the CRLB over small noise region, and have a better performance than that of the TSWLS method, especially when the sensor station error noises become large. Simulation results demonstrate the good performance of the proposed method.

Various microphone array geometries (e.g., linear, circular, square, cubic, spherical, etc.) have been used to improve the positioning accuracy of sound source localization. However, whether these array structures are optimal for various specific localization scenarios is still a subject of debate. This paper addresses a microphone array optimization method for sound source localization based on TDOA (time difference of arrival). The geometric structure of the microphone array is established in parametric form. A triangulation method with TDOA was used to build the spatial sound source location model, which consists of a group of nonlinear multivariate equations. Through reasonable transformation, the nonlinear multivariate equations can be converted to a group of linear equations that can be approximately solved by the weighted least square method. Then, an optimization model based on particle swarm optimization (PSO) algorithm was constructed to optimize the geometric parameters of the microphone array under different localization scenarios combined with the spatial sound source localization model. In the optimization model, a reasonable fitness evaluation function is established which can comprehensively consider the positioning accuracy and robustness of the microphone array. In order to verify the array optimization method, two specific localization scenarios and two array optimization strategies for each localization scenario were constructed. The optimal array structure parameters were obtained through numerical iteration simulation. The localization performance of the optimal array structures obtained by the method proposed in this paper was compared with the optimal structures proposed in the literature as well as with random array structures. The simulation results show that the optimized array structure gave better positioning accuracy and robustness under both specific localization scenarios. The optimization model proposed could solve the problem of array geometric structure design based on TDOA and could achieve the customization of microphone array structures under different specific localization scenarios.

In this paper, we introduce an integration method of Global Positioning System (GPS) pseudorange and time difference of arrival (TDOA) measurements from a pilot signal of a wireless communication network to solve the navigation problem in the case that the number of visible GPS satellites is not sufficient. TDOA measurements are known to have poor accuracy due to technical limitations of a communication system and propagation environments. The major error source of TDOA is the non-line-of-sight (NLOS). To estimate the TDOA error in a cellular network, we propose a method using a Kalman filter for each TDOA channel. In addition, a method to measure TDOA errors is proposed. Some simulations and real tests are conducted to evaluate the performance of the proposed methods. Real experiments are conducted near the city center of Seoul. Using a commercial cellular phone and a GPS receiver, pseudorange of GPS and TDOAs are measured. Test results show that the proposed methods continuously provide subscribers a reasonably accurate location in the case that GPS data are occasionally insufficient.

We investigate the problem of sensor and source joint localization using time-difference of arrivals (TDOAs) of an ad-hoc array. A major challenge is that the TDOAs contain unknown time offsets between asynchronous sensors. To address this problem, we propose a low-rank approximation method that does not need any prior knowledge of sensor and source locations or timing information. At first, we construct a pseudo time of arrival (TOA) matrix by introducing two sets of unknown timing parameters (source onset times and device capture times) into the current TDOA matrix. Then we propose a Gauss-Newton low-rank approximation algorithm to jointly identify the two sets of unknown timing parameters, exploiting the low-rank property embedded in the pseudo TOA matrix. We derive the boundaries of the timing parameters to reduce the initialization space and employ a multi-initialization scheme. Finally, we use the estimated timing parameters to correct the pseudo TOA matrix, which is further applied to sensor and source localization. Experimental results show that the proposed approach outperforms state-of-the-art algorithms.

Accuracy of unknown sound source localization and lifetime of sensor nodes is one of the important and critical issue and challenge in wireless sensor network. Localization plays major role when there is an uncertainty of the exact source location of some fixed or mobile devices. The main problem is source localization in an environment where they try to localize and focus on a source of unknown sound and noisy distance. This paper presents a new approach on the indoor localization of an acoustic sensor source. In this localization system, the environment will be scattered with a swarm of acoustic motes. The motes used in this localization system are the LOTUS motes from MEMSIC. In order to get the system working, We utilize a modular sensor board to plug on the mote. This sensor board captures acoustic signals and then digitalizes them. This localization algorithm uses the incoming Time Difference of Arrival (TDOA) values of a swarm ZigBee modules. The location of the source of the acoustic signal is determined by the multilateration technique. A sensor network is used in order to collect the signals. The evident advantage of the algorithm is that it requires synchronization between the motes mutually and not between the motes and acoustic source. The algorithm for these methods is written in MATLAB. The motes communicate in a ZigBee mesh network.

Acoustic source localization has been an hot research topic with widespread applications in many fields. In the noisy environment or when the reverberation is considerable, the source localization problem becomes challenging and many existing algorithms deteriorate. The paper proposes a robust algorithm which combines the RANdom SAmple Consensus (RANSAC) algorithm, and the Generalized Cross-Correlation (GCC) based Time Difference of Arrival (TDOA). Experiments in real world data show that the proposed algorithm has significantly better performance than the traditional algorithm.

In this paper, we focus on the localization of a passive source from time difference of arrival (TDOA) measurements. TDOA values are computed with respect to pairs of fixed sensors that are required to be accurately time-synchronized. This constitutes a weakness as all synchronization techniques are vulnerable to delay injections. Attackers are able either to spoof the signal or to inject asymmetric delays in the communication channel. By nature, TDOA measurements are highly sensitive to time-synchronization offsets between sensors. Our first contribution is to show that timing attacks can severely affect the localization process. With a delay of a few microseconds injected on one sensor, the resulting estimate might be several kilometers away from the true location of the unknown source. We also show that residual analysis does not enable the detection and identification of timing attacks. Our second contribution is to propose a two-step TDOA-localization technique that is robust against timing attacks. It uses a known source to define a weight for each pair of sensors, reflecting the confidence in their time synchronization. Our solution then uses the weighted least-squares estimator with the newly created weights and the TDOA measurements received from the unknown source. As a result, our method either identifies the network as being too corrupt to localize, or gives a corrected estimate of the unknown position along with a confidence metric. Numerical results illustrate the performance of our technique.

This paper develops an efficient bi-iterative source location and propagation speed estimation method utilizing time difference of arrival (TDOA) measurements. The source location and propagation speed estimation is a nonlinear problem due to the nonlinearity in the TDOA measurement equations. The newly developed bi-iterative method computes the source location and propagation speed alternately. The asymptotic convergence of the new bi-iterative method is theoretically analyzed. First-order perturbation analysis is applied to the newly developed solution to derive its bias and variance. The first-order analytical results show that the proposed method provides approximately unbiased source position and propagation speed estimates for low noise levels and the accuracy of these estimates approaches the Cramer-Rao lower bound (CRLB). The extension of the new bi-iterative method to the more general situation where the sensor locations are subject to random errors is also presented. Simulation studies are given to show the good performance of the proposed method.

Acoustic source localization based on time difference of arrival (TDOA) measurements from spatially separated sensors is an important problem in wireless sensor networks (WSNs). While extensive research works have been performed on algorithm development, limited attention has been paid in how to form the sensor pairs. In the literature, most of the works adopt a centralized sensor pairing strategy, where only one common sensor node is chosen as the reference. However, due to the multi-hop nature of WSNs, it is well known that this kind of centralized signal processing method is power consuming since raw measurement data is involved in the transmissions. To reduce the requirements for both network bandwidth and power consumptions, we propose an in-network sensor pairing method to collect the TDOA measurements while guaranteeing the quality of source localization. The solution involves finding a minimal sized dominating set (MSDS) for a graph of the muti-hop network. It has been proved that in-network sensor pairing can result in the same Cramer-Rao-Bound (CRB) as the centralized one but at a far more less communication cost. Furthermore, the structure of the proposed in-network sensor pairing coincides with the decentralized source localization, which is an important application of our method.