Detection of Direction-Of-Arrival in Time Domain Using Compressive Time Delay Estimation with Single and Multiple Measurements

Abstract

The compressive time delay estimation (TDE) is combined with delay-and-sum beamforming to obtain direction-of-arrival (DOA) estimates in the time domain. Generally, the matched filter that detects the arrivals at the hydrophone is used with beamforming. However, when the ocean noise smears the arrivals, ambiguities appear in the beamforming results, degrading the DOA estimation. In this work, compressive sensing (CS) is applied to accurately evaluate the arrivals by suppressing the noise, which enables the correct detection of arrivals. For this purpose, CS is used in two steps. First, the candidate time delays for the actual arrivals are calculated in the continuous time domain using a grid-free CS. Then, the dominant arrivals constituting the received signal are selected by a conventional CS using the time delays in the discrete time domain. Basically, the compressive TDE is used with a single measurement. To further reduce the noise, common arrivals over multiple measurements, which are obtained using the extended compressive TDE, are exploited. The delay-and-sum beamforming technique using refined arrival estimates provides more pronounced DOAs. The proposed scheme is applied to shallow-water acoustic variability experiment 15 (SAVEX15) measurement data to demonstrate its validity.

Estimation of Acoustic Channel Impulse Response at Low Frequencies Using Sparse Bayesian Learning for Nonuniform Noise Power

“…When underwater sound that is emitted from a source is transmitted through the ocean, the signal that arrives at the receiver contains multiple signals that pass through different paths that arise from the waveguide from the interfaces (sea bottom and surface) and the scatterers (submerged objects and fish schools). An impulse response of the acoustic channel (acoustic CIR) is approximated by assuming an insignificant dispersion of acoustic waves during sound propagation (particularly, the Doppler effect is ignored because our attention is limited to the low frequency region), as follows [2], [6], [12], [16], [18], [19], [30]:…”

“…In underwater acoustics, the CS has been predominantly applied to estimate the direction of arrivals (DOAs) from underwater targets [9]- [15] and was introduced in the underwater acoustic channel parameter estimation by several researchers [6], [16]- [18], whereas the MF-based CIR estimation is a conventional approach that was employed for active sonar systems [1], [2]. The sparse solutions in the CS literature help estimate the parameters with super-resolution [12], [17], [19]. However, manually determined hyperparameters in the schemes that are associated with measurement noise are sensitive to sparsity.…”

“…The CS has been previously used to estimate acoustic CIRs by utilizing the sparse arrivals in acoustic channels [6], [12], [16], [17], [19]. Meanwhile, the CS-based DOA estimations were recently advanced by the SBL, which can find the DOAs into sensor array without additional post-processing [21]- [27].…”

Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

“…The architecture of the neural networks is described in Figure 2. In Figure 2, the nodes of the input layer represent the posterior probabilities of the possible PN sequence usages in the signal present case, given the 1st through the (m − 1)th measurements, i.e., P b (l|H 1 , ℵ m ) (1, 2, ..., L) in Equation (11). The neural network is fully connected and real valued.…”

“…In last decade, the compressive sensing (CS) theorem was rendered [8,9], which provided perspectives on sufficient sampling on image and communication signal processing techniques [10][11][12]. Motivated by the CS theorem, many compressive signal detection methods were proposed, such as sparse signal reconstruction-based methods.…”