Underwater Acoustic Localization Research Articles

Low cost underwater acoustic localization

Over the course of the last decade, the cost of marine robotic platforms has significantly decreased. In part this has lowered the barriers to entry of exploring and monitoring larger areas of the earth's oceans. However, these advances have been mostly focused on autonomous surface vehicles (ASVs) or shallow water autonomous underwater vehicles (AUVs). One of the main drivers for high cost in the deep water domain is the challenge of localizing such vehicles using acoustics. Here we propose a novel low cost underwater modem design to assist in localizing deep water submersibles. The system will consist of location aware anchor buoys at the surface and underwater nodes. We present a comparison of methods, simulation of the proposed algorithms as well as experimental results, together with details on the physical implementation to allow its integration into a novel deep sea AUV currently in development.

Blind channel estimation of time-varying underwater acoustic waveguide impulse responses

Some techniques for underwater passive acoustic localization make use of estimates for the direct and/or interface-reflected acoustic arrival times of a source at one or more underwater hydrophones. This estimation task can be difficult for non-impulsive sources (e.g., humpback whales) due to early arrivals masking later ones. In linear system analysis, the impulse response (IR) of a system is the system output when the input is an impulse (i.e., a short duration, large bandwidth signal), and expresses how a source signal interacts with the environment to yield the output waveform (e.g., that recorded by a hydrophone). Recordings of a relatively impulsive vocalization (e.g., a walrus knock), given that they approximate the IR between the walrus and hydrophone, may facilitate arrival time estimation. IR estimation for unknown, non-impulsive calls is more challenging, particularly for time-varying channels. Blind channel estimation is the process of estimating the set of IRs between a single (unknown) source and multiple receivers, and can potentially help estimate direct and interface-reflected arrival times for non-impulsive marine mammal vocalizations since the IRs can be analyzed rather than waveforms/spectra. In this paper, we use simulations to explore the importance of ocean surface gravity waves on blind estimation of acoustic IRs.

Compressive focused beamforming based on vector sensor array

With the rapid development of the theory and algorithms for sparse recovery in finite dimension, compressive sensing (CS) has become an exciting field that has attracted considerable attention in signal processing, such as sub-Nyquist sampling systems, sound imaging and reconstruction, wavelet denoising, compressive sensor networks, and so on. Moreover, the broad applicability of CS framework has already inspired some notable investigation in the context of array processing. The problem of acoustic source identification can be investigated from a limited number of measurements delivered by a microphone array as a basis pursuit problem, which has been developed in the context of compressive sensing, and the CS beamforming can be proved to be better than the conventional beamforming even in its near-field focusing version based on spherical waves. Focused beamforming is a typical method used to localize the position of acoustic sound sources in the near field of the measurement array, and can be a jointly reconstructed source powers and positions. Many super-resolution focused beamforming approaches have been developed to overcome the Rayleigh resolution limit of conventional focused beamforming. Especially, turning to the compressive sensing (CS) framework, we are able to exploit the inherent sparsity of the underlying signal in space domains to achieve super-resolution for the focused beamforming even in a noisy and coherent environment with few snapshots.Prior research has established CS as a valuable tool for array signal processing, but it is mainly from a theoretical point of view, and its application to underwater acoustic sources localization has been developed only for very limited scenarios. In this paper, we present an underwater noise sound source near-field localization method based on a sparse representation of vector sensor array measurements. By utilizing the sparsity approach, the new localization methods can jointly reconstruct source powers and positions, and enforce sparsity by imposing penalties, based on the l1-norm, to improve the integrated performance. By comparing with other source localization methods, such as the conventional focused beamforming, MVDR focused beamforming, and the maximum likelihood focused beamforming, the performance of compressive focused beamforming and the typical focused beamforming using pressure or vector sensor array is analyzed in detail, especially under noisy conditions, and coherent sources. Simulation and experimental results demonstrate that this new approach has a number of advantages over other source localization techniques, e.g. increased resolution, improved robustness to noise, limitations in data quantity and correlation of the sources, as well as lower levels of background interference. It is feasible to apply the proposed approach for effectively localizing and identifying underwater noise sound sources.

Collision Tolerant and Collision Free Packet Scheduling for Underwater Acoustic Localization

This article considers the joint problem of packet scheduling and self-localization in an underwater acoustic sensor network with randomly distributed nodes. In terms of packet scheduling, our goal is to minimize the localization time, and to do so we consider two packet transmission schemes, namely a collision-free scheme (CFS), and a collision-tolerant scheme (CTS). The required localization time is formulated for these schemes, and through analytical results and numerical examples their performances are shown to be dependent on the circumstances. When the packet duration is short (as is the case for a localization packet), the operating area is large (above 3 km in at least one dimension), and the average probability of packet-loss is not close to zero, the collision-tolerant scheme is found to require a shorter localization time. At the same time, its implementation complexity is lower than that of the collision-free scheme, because in CTS, the anchors work independently. CTS consumes slightly more energy to make up for packet collisions, but it is shown to provide a better localization accuracy. An iterative Gauss-Newton algorithm is employed by each sensor node for self-localization, and the Cramér Rao lower bound is evaluated as a benchmark.

A real-time array calibration method for underwater acoustic flexible sensor array

To solve the precise passive ranging problem of underwater acoustic localization systems, we design a large aperture flexible sensor array whose sensors are deployed without rigid connections. We propose a novel real-time array calibration method to estimate the 3-D coordinates of the sensors. Spherical interpolation estimator is used to estimate the range of the acoustic source. The impact of the estimation errors in time-delay and sensor coordinates on the spherical interpolation range estimator is analyzed via computer simulations. By comparing the passive acoustic ranging results from sea trials with the GPS measurement data, we show that the proposed method can calibrate the flexible array in real time more accurately and estimate the underwater acoustic source range precisely.

An improved underwater acoustic network localization algorithm

Underwater sensor network can achieve the unmanned environmental monitoring and military monitoring missions. Underwater acoustic sensor node cannot rely on the GPS to position itself, and the traditional indirect positioning methods used in Ad Hoc networks are not fully applicable to the localization of underwater acoustic sensor networks. In this paper, we introduce an improved underwater acoustic network localization algorithm. The algorithm processes the raw data before localization calculation to enhance the tolerance of random noise. We reduce the redundancy of the calculation results by using a more accurate basic algorithm and an adjusted calculation strategy. The improved algorithm is more suitable for the underwater acoustic sensor network positioning.

Investigation on Correlation Peaks Ambiguity and Ambiguity Elimination Algorithm in Underwater Acoustic Passive Localization

Investigation on Correlation Peaks Ambiguity and Ambiguity Elimination Algorithm in Underwater Acoustic Passive Localization

LOS and NLOS Classification for Underwater Acoustic Localization

The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS.

Underwater passive acoustic localization of Pacific walruses in the northeastern Chukchi Sea

This paper develops and applies a linearized Bayesian localization algorithm based on acoustic arrival times of marine mammal vocalizations at spatially-separated receivers which provides three-dimensional (3D) location estimates with rigorous uncertainty analysis. To properly account for uncertainty in receiver parameters (3D hydrophone locations and synchronization times) and environmental parameters (water depth and sound-speed correction), these quantities are treated as unknowns constrained by prior estimates and prior uncertainties. Unknown scaling factors on both the prior and arrival-time uncertainties are estimated by minimizing Akaike's Bayesian information criterion (a maximum entropy condition). Maximum a posteriori estimates for sound source locations and times, receiver parameters, and environmental parameters are calculated simultaneously using measurements of arrival times for direct and interface-reflected acoustic paths. Posterior uncertainties for all unknowns incorporate both arrival time and prior uncertainties. Monte Carlo simulation results demonstrate that, for the cases considered here, linearization errors are small and the lack of an accurate sound-speed profile does not cause significant biases in the estimated locations. A sequence of Pacific walrus vocalizations, recorded in the Chukchi Sea northwest of Alaska, is localized using this technique, yielding a track estimate and uncertainties with an estimated speed comparable to normal walrus swim speeds.

Underwater Localization with Time-Synchronization and Propagation Speed Uncertainties

Underwater acoustic localization (UWAL) is a key element in most underwater communication applications. The absence of GPS as well as the signal propagation environment makes UWAL similar to indoor localization. However, UWAL poses additional challenges. The propagation speed varies with depth, temperature, and salinity, anchor and unlocalized (UL) nodes cannot be assumed time-synchronized, and nodes are constantly moving due to ocean currents or self-motion. Taking these specific features of UWAL into account, in this paper, we describe a new sequential algorithm for joint time-synchronization and localization for underwater networks. The algorithm is based on packet exchanges between anchor and UL nodes, makes use of directional navigation systems employed in nodes to obtain accurate short-term motion estimates, and exploits the permanent motion of nodes. Our solution also allows self-evaluation of the localization accuracy. Using simulations, we compare our algorithm to two benchmark localization methods as well as to the Cramer-Rao bound (CBR). The results demonstrate that our algorithm achieves accurate localization using only two anchor nodes and outperforms the benchmark schemes when node synchronization and knowledge of propagation speed are not available. Moreover, we report results of a sea trial where we validated our algorithm in open sea.

Underwater acoustic localization for small submersibles

AbstractThis article discusses the complete processing chain of a high‐precision as well as robust underwater localization method. Range and azimuth as well as relative heading are determined for ranges up to 90 m. For ranges up to 10 m the measured precision is 0.05 m for range, 2 deg for direction, and 5 deg for heading of neighboring vehicles. The system is also designed to be deployable in swarms of vehicles as acoustic localization signals are separated by time slots and maximum length sequence (MLS) signatures. The proposed method is based on the consequent exploitation of MLS broadband signal characteristics and the exploration of several methods for each vehicle to estimate ranges, azimuths, and headings of neighboring vehicles. An existing radio‐based swarm communication system provides additional support and leads to enhanced measurement precision and interference robustness. The capabilities and limitations of the proposed systems are experimentally determined and discussed. © 2010 Wiley Periodicals, Inc.

Underwater acoustic localization by principal components analyses based probabilistic approach

In this paper, the underwater localization is given from wireless acoustic communication signals by probabilistic pattern recognition in eigenspace of PCA (principal components analyses). It should be emphasized that our underwater localization is from existing wireless acoustic communication signals, but not from additional localization systems. Our underwater localization scheme is based on fingerprinting and contains two stages, i.e., the off-line (i.e., training) and on-line (i.e., predicting) stages. In general, the received acoustic signals fluctuate seriously in underwater environments. To reduce the complexity and noise effects, all received signals are projected onto the eigenspace of PCA. Each projected feature is assumed to have Gaussian probabilistic distributions. Therefore, the location information can be easily obtained by probabilistic pattern recognition of projected features in PCA space. Note that our underwater localization scheme is not affected by reflected signals. To illustrate such a benefit, experiments were conducted in a bounded water pool where reflected signals exist near the walls. Experimental results show that the proposed underwater localization scheme is efficient and accurate. The proposed localization scheme is useful for underwater acoustic communication networks, and then in underwater technologies.

Stratification Effect Compensation for Improved Underwater Acoustic Ranging

Underwater acoustic localization usually relies on time-of-arrival (ToA) measurements, which are then converted into range estimates. However, the water medium is inhomogeneous and the sound speed varies depending on several parameters, e.g., the temperature, pressure and salinity. As a result, sound waves do not necessarily travel in straight lines. Ignoring this stratification effect could lead to considerable bias in the range estimates. We propose a depth-based approach to compensate the stratification effect for improved underwater ranging. We assume that the sound velocity profile (SVP) is only vertically stratified, the position of the sender is known, and the receiver has a noisy depth estimate via a depth sensor. We find a numerically simple range estimator, based on reconstructing the slanted path using Fermat's Principle and calculus of variations. This estimator removes the bias and is asymptotically efficient. We compare our solution to the simplistic linear estimator that assumes straight-line propagation in a shallow-water example where the sound speed decreases monotonically with depth. We find that the bias of the linear estimator increases with range and is non-negligible when the ToA measurements have a small variance, while our solution is bias-free and meets the Cramer-Rao lower bound (CRLB).

Localization and tracking techniques for navigation and guidance of underwater autonomous sensing systems

The key to the successful execution of autonomous underwater imaging and sensing is the localization and tracking of the data-acquisition units, which can be achieved by estimating the relative location with respect to a set of underwater base stations. In a three-dimensional underwater acoustic localization, the objective parameters are the range distance and a two-dimensional bearing angle. At the base stations, the transmitters send a sequence of guidance waveforms. One signal is a pair of polarity signals, with a 180° left–right phase offset, for the estimation of the bearing angle in the horizontal direction. Similarly, another one is with a top–bottom polarity for the estimation of the bearing angle in the vertical direction. The received signals represent spatial-temporal samples of the interferences produced by the polarities of the transmitted signals. The range distance can be estimated from the slope of the phase profiles of the spectra. And the bearing angle is directly related to the variation of the complex amplitude of the received polarity signals and can be estimated accordingly. This paper includes system design, signal analysis, development of processing algorithms, and results from full-scale experiments. [Research is supported by the UC MICRO program and Sonatech.]