Unambiguous broadband direction of arrival estimation based on improved extended frequency-difference method

Wideband direction of arrival estimation using a backward nonnegative sparse method

In the field of direction-of-arrival (DOA) estimation for wideband signals, traditional methods often face a significant challenge as a result of spatial aliasing. These methods, which treat signals at different frequencies separately, may fail to detect weak signals due to the interference caused by spatial aliasing. To overcome this challenge, a wideband DOA estimation method, named the backward nonnegative sparse (BNNS) method, is introduced, which processes signals jointly across multiple frequencies. BNNS leverages a characteristic that the direction of the mainlobe remains constant across frequencies, and the grating lobe varies with frequency. By selectively retaining only those entries corresponding to the mainlobe in dictionaries, BNNS effectively reduces interference from grating lobes. Simulation results demonstrate the capability of BNNS to suppress grating lobes and detect weak signals under conditions of low signal-to-noise ratio and signal-to-interference ratio. Additionally, its ability to enhance peak-background contrast is demonstrated by the Swellex96 experimental data.

Wideband off-grid direction-of-arrival estimation based on the improved finite rate of innovation method

Off-grid direction-of-arrival estimation is a crucial research area in multi-sensor array signal processing to achieve accurate estimation in a finite sparse grid. However, current off-grid estimation methods primarily focus on narrowband processing, which may not be suitable for practical passive estimation scenarios where the targets of interest are wideband signals with various steering vectors and varying signal-to-noise ratios across frequency bins. First, we propose an improved weighting-based wideband joint finite rate of innovation algorithm to address this limitation. This algorithm extends the narrowband approach by approximating the wideband array data as multiple observations at the difference frequency using extended frequency difference weighting. Additionally, we propose an estimation method under non-ideal weighting conditions to mitigate bias caused by deviations in initial weight values through linear fitting of multiple estimation results obtained on a sparse grid. Simulation results demonstrate that our proposed algorithm outperforms existing methods by providing more accurate estimates and lower computational complexity for wideband off-grid multi-targets at low signal-to-noise ratio while unrestricted by grid limitations. Furthermore, experimental data collected from the South China Sea validate our proposed algorithm's effectiveness and superior performance for direction-of-arrival estimation of wideband off-grid targets.

High-resolution frequency-difference beamforming for a short linear array

Conventional beamforming (CBF) is a commonly employed approach for detecting and estimating the direction-of-arrival (DOA) of acoustic signals in underwater environments. However, CBF becomes ambiguous due to spatial aliasing when the received signal's half wavelength is smaller than the array spacing. Frequency-difference beamforming (FDB) allows for processing data in the lower frequency Δf without encountering spatial aliasing by utilizing the product of array data at frequency f with its complex conjugate at frequency f+Δf. However, lower frequency results in a wider mainlobe, which can lead to poorer DOA performance for short arrays. In this paper, a fourth-order cumulants FDB method and a conjugate augmented FDB method are proposed to extend an M-element uniform linear array to 2M-1 and 4M-3 elements. The proposed methods generate narrower beams and lower sidelobe levels than the original FDB for short arrays with large spacing. And by setting the signal subspace dimension reasonably, the proposed methods can improve the weak target detection ability under strong interference compared with FDB. Last, we verify the excellent performance of the proposed methods through simulations and experimental data.

Localization of a remote source in a noisy deep ocean sound channel using phase-only matched autoproduct processing

Long-range passive source localization is possible in the deep ocean using phase-only matched autoproduct processing (POMAP) [Geroski and Dowling (2021). J. Acoust. Soc. Am. 150, 171-182], an algorithm based on matched field processing that is more robust to environmental mismatch. This paper extends these prior POMAP results by analyzing the localization performance of this algorithm in the presence of environmental noise. The noise rejection performance of POMAP is assessed using both simulated and measured signal data, with noise data based on environmental noise measurements. Herein, signal and noise measurements are from the nominally one-year-long PhilSea10 ocean acoustic propagation experiment. All signals were recorded from a single moored source, placed near the ocean sound channel 129.4 km away from a nearly water-column-spanning distributed vertical line array. The source transmitted linear frequency modulated chirps with nominal bandwidth from 200 to 300 Hz. The noise measurements used in this study were collected in the months after this source stopped transmitting, and synthetic samples of noise are calculated based on the characteristics of this measured noise. The effect that noise rejection algorithms have on the source localization performance of POMAP is also evaluated, but only 1 dB of performance improvement is achieved using these.