We consider the problem of estimating the trajectory of a submerged source emitting acoustic signals without using any anchor nodes or receiving array. This approach is required for several applications, including the localization of acoustic sources such as marine mammals or underwater vehicles, for which the cost of covering a broad area with multiple receivers would be excessively high. Since multi-lateration is impossible in this scenario, we perform localization by incorporating bathymetry information. Specifically, we assume that the receiver retains knowledge of the environmental parameters that affect the signal propagation, and that the bathymetry of the area is sufficiently diverse to induce distinguishable channel impulse responses for different source locations. Our method compares the channel estimates obtained from the received acoustic signals against a database of channel responses, pre-computed through an acoustic ray tracing model. The set of possible node locations that result are then organized in trellis form to obtain a final estimate of the source's trajectory via a path tracking method similar to the Viterbi algorithm. Our results show that the proposed approach can estimate node locations and paths with very small error, provided that the receiver has sufficiently accurate and up-todate environmental information.
Underwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to estimate the direction of arrival of a desired signal. When the available equipment does not provide the required level of accuracy, it may be convenient to merge multiple transceivers into a larger acoustic array, in order to achieve better processing performance. In this paper, we name such a structure an “array of opportunity” to signify the often inevitable sub-optimality of the resulting array design, e.g., a distance between nearest array elements larger than half the shortest acoustic wavelength that the array would receive. The most immediate consequence is that arrays of opportunity may be affected by spatial ambiguity, and may require additional processing to avoid large errors in wideband direction of arrival (DoA) estimation, especially as opposed to narrowband processing. We consider the design of practical algorithms to achieve accurate detections, DoA estimates, and position estimates using wideband arrays of opportunity. For this purpose, we rely jointly on DoA and rough multilateration estimates to eliminate spatial ambiguities arising from the array layout. By means of emulations that realistically reproduce underwater noise and acoustic clutter, we show that our algorithm yields accurate DoA and location estimates, and in some cases it allows arrays of opportunity to outperform properly designed arrays. For example, at a signal-to-noise ratio of –20 dB, a 15-element array of opportunity achieves lower average and median localization error (27 m and 12 m, respectively) than a 30-element array with proper λ / 2 element spacing (33 m and 15 m, respectively). We confirm the good accuracy of our approach via emulation results, and through a proof-of-concept lake experiment, where our algorithm applied to a 10-element array of opportunity achieves a 90th-percentile DoA estimation error of 4 ∘ and a 90th-percentile total location error of 5 m when applied to a real 10-element array of opportunity.
We consider a range estimation algorithm based on the time-of-arrival differences between multipath replicas of a signal transmitted by an anchor node of known location to a silent node that needs to localize itself. We consider the geometry of the problem in the simplified case of a constant sound speed profile (SSP), and argue that the more realistic case of a nonconstant SSP can be still tackled as if the SSP were constant, provided that an "effective" sound speed value is computed based on the geometry of the signal propagation. We finally evaluate the accuracy of the latter approach in the presence of errors affecting different quantities required for the range estimation process, including the TDoA values and the bottom depth. Our results show that our approach offers improved accuracy with respect to a baseline LSE estimator.
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