In this article, a direction of arrival (DOA) based estimation method is proposed for the localization of acoustic gas leakage sources indoors, with the use of a microphone array. Since the narrowband signal assumption for the uniform circular array (UCA)-MUSIC and ESPRIT methods are not applicable to the case of gas leakage sources, different approaches including the incoherent, coherent, and frequency-domain frequency-invariant beamformer ones are leveraged and optimized for the signal shape considered. The proposed method is evaluated with real-world experimental results obtained with a gas pistol resembling a leakage. The evaluation using experimental data demonstrates that coherent subspace (CSS)-MUSIC is ahead of other methods in the error metrics.

This paper proposes two efficient and easy-to-use error mitigation solutions to the problem of three-dimensional (3-D) angle-of-arrival (AOA) source localization in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) indoor environments. A weighted linear least squares estimator is derived first for the LOS AOA components in terms of the direction vectors of arrival, albeit in a sub-optimal manner. Next, data selection exploiting the sum of squared residuals is carried out to discard the error-prone NLOS connections. In so doing, the first approach is constituted and more accurate closed-form location estimates can be obtained. The second method applies a simulated annealing stochastic framework to realize the robust ℓ1-minimization criterion, which therefore falls into the methodology of statistical robustification. Computer simulations and ultrasonic onsite experiments are conducted to evaluate the performance of the two proposed methods, demonstrating their outstanding positioning results in the respective scenarios.

The reliability of an indoor localization system is often limited by the capability of the systems of distinguishing the line-of-sight signals in environments with multipath. Besides, estimating the height of the target can be challenging due to dilution of precision (DOP) and non line of sight (NLOS) situations. However, the reflections can be used to infer information about the scenario. We present a novel approach which exploits the ground reflections to reduce the error in three dimensional localization and detect reliably the line-of-sight signals. The presented approach uses the geometry of the reflections as the model of the RANSAC method. In this model, ground reflections and line of sight signals can be found just solving a quadratic equation, which reduces the computational power required compared to other approaches. The positions are estimated using local optimization algorithms. In real experiments, the median error in the height estimation was reduced from 25 cm to 4.2 cm.

In this paper we present a novel indoor localization approach based on measuring the time of flight (TOF) of ultrasound signal reflections in human bodies and walls. This is a stand off detection approach, it does not require the user to carry any wireless device. Only a static co-located speaker and microphone pair is used. The speaker sends a chirp, which is reflected to the environment. Then, the microphone receives the chirp and the reflections, measuring the reception times. Subsequently, the position of the target is estimated by the line of sight signal and the second reflections from 2 walls. Fast normalized correlation is used to estimate the timestamps of the received signals. The human being reflections are detected by subtracting the correlated signal in consecutive intervals, exploiting the fact that even a static human being moves slightly due to his breathing. Besides, we use a polynomial approximation of the reflections to increase the accuracy of this process. The choice among the remaining peaks is done by minimizing the error of the geometrical equations involved. When the target is moving continuously, an unscented Kalman filter is used to predict the most reliable peaks and estimate the position of the target. Real measurements show the capability of the proposed approach of localizing a person standing still with a median error of 0.15m and a moving human being with a median error of 0.08m and a standard deviation of 0.13 m.

Indoor localization based on time difference of arrival (TDOA) has been recently a promising field of study. We consider the previously unsolved problem of locating a moving target receiver by using unsynchronized stationary beacons without requirement of manual calibration. Thus, the received signals and their time of arrival (TOA) have to be assigned to a beacon. Besides, in order to automatically calibrate the system it is required to estimate the time offsets between the senders, their positions and the initial receiver position. We present an approach to estimate all the variables of the scenario using the gradient descent and the Gauss-Newton method, two local optimization algorithms which use the derivative of a system of hyperbolic error equations. Besides, we present an ultrasound transmission system approach which fulfils the requirements of this scenario, being robust against multipath and estimating the reception time with high accuracy. In order to avoid interference by echoes the packet size is reduced by using two frequencies in Orthogonal Frequency Division Multiplex (OFDM). Further, the transmission system enables distinction of the beacons, as the ultrasound signals are used both for localization and for information transmission. The simulations show the local optimization algorithms are capable of estimating the positions of the beacons, receivers and offsets. They require only a rough knowledge of the sender positions. Further, real experiments show that the timestamps are measured with a standard deviation of only 1.19 μs for a SNR of 10 dB, which corresponds to standard deviation of about 0.4 mm for the distance measurement.

Localization based on the time difference of arrival (TDoA) has turned out to be a promising approach for indoor environments, especially in combination with innovative self-calibrating TDoA algorithms that eliminate the need to measure the positions of reference receivers. We consider the previously unsolved problem of locating a moving target receiver by discrete signals from stationary beacons at unknown locations. We assume that the beacons are small and inexpensive and they require no further communication, i.e. they are unsynchronized. They only emit short discrete signals at regular intervals, of which we assume that they can be distinguished. The moving target travels on an unknown trajectory, receiving signals from the beacons and calculating the TDoA of the signals. First, we discuss adaptions of two TDoA algorithms by which the senders can be located from unknown signals. Second, we propose two novel approaches based on probabilistic state estimation to enable robust localization of the mobile receiver using the discrete arrival times, once the senders have been located. The probabilistic algorithms use the particle filter and the unscented Kalman filter to estimate the position and velocity of the target, as well as the unknown synchronization offsets of the senders. We provide a motion model and a sensor model for which we take into account that the signals of the beacons are received as singles, each at a different time. We verify the feasibility and robustness of our approach in extensive simulations, where we analyze the reliability of localization and compare both algorithms.

In this paper, we present a novel smartphone indoor localization system. The smartphone user is localized with small effort, affordable equipment and with high accuracy in indoor areas. The system uses commercially available smartphones generating high pitched acoustic chirp signals beyond the audible range. The chirp signals are received by sound receivers which identify the specific sound produced from each smartphone. The receivers are connected to a WiFi network, such that they synchronize their clocks and exchange the time differences of arrival (TDoA) of the received chirps. In this way, using an iterative multilateration algorithm, the location of the smartphones can be calculated and the receiver positions are calibrated automatically. For generating the specific sound signals from the smartphone and for user navigation an Android software application was developed. The user interface is simple and is invoked by starting the software application, which automatically connects to a server and receives an ID using the internet connection of the smartphone. Furthermore, the user is assigned specific parameters, such that several devices can be distinguished by the appearance of the chirps. The position of the user is displayed on the smartphone in context of the environment, with a map and surrounding items. In the presented work we have verified our system in a real-world scenario. We compared our trajectory of a pedestrian carrying smartphone to the reference positions. We could locate the smartphones with error margin of 30 cm. a centimeter margin of error.

Localization based on time differences of arrival (TDOA) has turned out to be a promising approach when neither receiver positions nor the positions of signal origins are known a priori. In this paper, we consider calibration-free tracking of a mobile beacon using TDOA, i.e., the positions of the receivers are not given. We propose a probabilistic formulation using a particle filter to simultaneously localize the signal beacon and the receivers. Our method is robust against measurement outliers and incorrect initialization. This is achieved through a probabilistic sensor model for TDOA data which explicitly considers the measurement uncertainty and takes into account disproportional errors caused by measurement outliers. For the reliable initialization of the particle filter, we apply an iterative optimization approach to multiple subsets of TDOA data, where the best solution is implicitly selected by appropriate weighing of the sensor model. We verify the robustness of our approach in extensive experiments in a spacious indoor environment by an ultrasound beacon moving on various trajectories. We demonstrate that our approach ensures a proper initialization of the particle filter and provides accurate position estimates for the signal beacon and the receivers even in case of measurement outliers. Compared to position references of an optical motion capture system we achieve mean position errors below 5 centimeters.

We present an approach for the localization of passive receiver nodes in a communication network. In our settings the positions of the nodes are unknown. The only source of information is the time when environmental sound or ultrasound signals are received. The discrete signals occur at unknown positions and times, but they can be distinguished. The clocks of the receivers are synchronized, so the time differences of arrival (TDOA) of the signals can be computed. The goal is to determine the relative positions of all receiver nodes and implicitly the positions and times of the environmental signals. Our novel approach, the Iterative Cone Alignment algorithm, solves iteratively a non-linear optimization problem of time differences of arrival (TDOA) by a physical spring-mass simulation. Here, our algorithm shows a smaller tendency to get stuck in local minima than a non-linear least-squares approach. The approach is tested in numerous simulations and in a real-world setting where we demonstrate and evaluate a tracking system for a moving ultrasound beacon without the need to initially calibrate the positions of the receivers. Using our approach we estimate the trajectory of a moving model train with a precision in the range of centimeters.

We consider an anchor-free, relative localization and synchronization problem where a set of n receiver nodes and m wireless signal sources are independently, uniformly, and randomly distributed in a disk in the plane. The signals can be distinguished and their capture times can be measured. At the beginning neither the positions of the signal sources and receivers are known nor the sending moments of the signals. Now each receiver captures each signal after its constant speed journey over the unknown distance between signal source and receiver position. Given these nm capture times the task is to compute the relative distances between all synchronized receivers. In a more generalized setting the receiver nodes have no synchronized clocks and need to be synchronized from the capture times of the stolen signals.

We present a smartphone application to localize a group of networking devices in a mobile environment without the need of any further infrastructure. Ambient sound signals are the only information source. Time marks are assigned to the recorded audio stream for distinctive audio events, out of which we evaluate the time differences of arrival (TDOA) between the devices. In contrast to common multilateration approaches we do not need any positional anchor points - neither any predefined smartphone positions nor the positions of the environmental sounds. As an application scenario we can localize arbitrary devices using only the random environmental noise peaks, e.g. in crowded areas like market places or concerts with the usual soundscape, or for thunderstorm tracking. Especially, our solution becomes useful when established positioning systems (e.g. GPS) are too imprecise or fail, as during indoor self-localization. We use a Wi-Fi connection to synchronize the clocks of the devices and to exchange time marks. In our experiments we evaluated the audio information and synchronized the devices up to an order of 0.1 ms. This results in a positioning precision in the order of 10 cm.