Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

Passive MIMO Radar Detection

Passive multiple-input multiple-output (MIMO) radar is a sensor network comprised of multiple distributed receivers that detects and localizes targets using the emissions from multiple non-cooperative radio frequency transmitters. This dissertation advances the theory of centralized passive MIMO radar (PMR) detection by proposing two novel generalized likelihood ratio test (GLRT) detectors. The first addresses detection in PMR networks without direct-path signals. The second addresses detection in PMR networks with direct-path signals. The probability distributions of both test statistics are investigated using recent results from random matrix theory. Equivalence is established between PMR networks without direct-path signals and passive source localization (PSL) networks. Comparison of both detectors with a centralized GLRT for active MIMO radar (AMR) detection reveals that PMR may be interpreted as the link between AMR and PSL sensor networks. In particular, under high direct-path-to-noise ratio (DNR) conditions, PMR sensitivity and ambiguity approaches that of AMR. Under low-DNR conditions, PMR sensitivity and ambiguity approaches that of PSL. At intermediate DNRs, PMR sensitivity and ambiguity smoothly varies between that of AMR and PSL. In this way, PMR unifies PSL and AMR within a common theoretical framework. This result provides insight into the fundamental natures of active and passive distributed sensing

Passive detection of rank-one signals with a multiantenna reference channel

In this work we consider a two-channel passive detection problem, in which there is a surveillance array where the presence/absence of a target signal is to be detected, and a reference array that provides a noise-contaminated version of the target signal. We assume that the transmitted signal is an unknown rank-one signal, and that the noises are uncorrelated between the two channels, but each one having an unknown and arbitrary spatial covariance matrix. We show that the generalized likelihood ratio test (GLRT) for this problem rejects the null hypothesis when the largest canonical correlation of the sample coherence matrix between the surveillance and the reference channels exceeds a threshold. Further, based on recent results from random matrix theory, we provide an approximation for the null distribution of the test statistic.The work of I. Santamaría was supported by the Spanish Government through grants PRX14/0028 (Estancias de Movilidad de Profesores, Ministerio de Educación) and by project RACHEL (TEC2013-47141-C4-3-R) funded by the Ministerio de Economía y Competitividad (MINECO). The work of L. Scharf and D. Cochran was supported in part by a sub-contract with Matrix Research for research sponsored by the Air Force Research Laboratory under contract FA8650-14-D-1722

Laser space rendezvous and docking tradeoff

A spaceborne laser radar (LADAR) was configured to meet the requirements for rendezvous and docking with a cooperative object in synchronous orbit. The LADAR, configurated using existing pulsed CO2 laser technology and a 1980 system technology baseline, is well suited for the envisioned space tug missions. The performance of a family of candidate LADARS was analyzed. Tradeoff studies as a function of size, weight, and power consumption were carried out for maximum ranges of 50, 100, 200, and 300 nautical miles. The investigation supports the original contention that a rendezvous and docking LADAR can be constructed to offer a cost effective and reliable solution to the envisioned space missions. In fact, the CO2 ladar system offers distinct advantages over other candidate systems

First-Order Statistical Framework for Multi-Channel Passive Detection

In this paper we establish a general first-order statistical framework for the detection of a common signal impinging on spatially distributed receivers. We consider three types of channel models: 1) the propagation channel is completely known, 2) the propagation is known but channel gains are unknown, and 3) the propagation channel is unknown. For each problem, we address the cases of a) known noise variances, b) common but unknown noise variances, and c) different and unknown noise variances. For all 9 cases, we establish generalized-likelihood-ratio (GLR) detectors, and show that each one can be decomposed into two terms. The first term is a weighted combination of the GLR detectors that arise from considering each channel separately. This result is then modified by a fusion or cross-validation term, which expresses the level of confidence that the single-channel detectors have detected a common source. Of particular note are the constant false-alarm rate (CFAR) detectors that allow for scale-invariant detection in multiple channels with different noise powers.Comment: 26 pages, 1 tabl

Development of advanced digital techniques for data acquisition processing and communication Interim scientific report

Image correlation and computerized simulation applied to data acquisition and imaging technique

Passive detection of correlated subspace signals in two MIMO channels

In this paper, we consider a two-channel multiple-input multiple-output passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension, but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and a subspace signal of the same dimension, but unknown basis, which is correlated with the subspace signal in the reference channel. We consider a second-order detection problem where these subspace signals are structured by an unknown, but common, p-dimensional random vector of symbols transmitted from sources of opportunity, and then received through unknown M × p matrices at each of the M-element arrays. The noises in each channel have spatial correlation models ranging from arbitrarily correlated to independent with identical variances. We provide a unified framework to derive the generalized likelihood ratio test for these different noise models. In the most general case of arbitrary noise covariance matrices, the test statistic is a monotone function of canonical correlations between the reference and surveillance channels.I. Santamaría and J. Vía have received funding from Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U. under projects TEC2013-47141-C4-3-R (RACHEL), TEC2016-75067-C4-4-R (CARMEN) and TEC2016-81900-REDT (KERMES). The research of Haonan Wang was partially supported by NSF grant DMS-1521746

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure