Passive acoustic monitoring (PAM) is a powerful and non-intrusive tool for studying marine mammals, particularly rare and deep-diving species such as beaked whales. However, the post-processing and manual analysis of large data sets is time-intensive. Multi-target tracking (MTT) methods have significant potential in automating this process and reducing human workload, but face challenges due to the potential absence or high directionality of beaked whale vocalizations. These two effects cause high variability in target detection probability, which can cause standard MTT trajectories to fragment. In this paper, we address this issue by introducing a multi-step estimation method that combines belief propagation-based MTT with track smoothing and stitching. Using real recordings of echolocation clicks from Ziphius cavirostris (Cuvier's beaked whales), we demonstrate that track stitching can generate continuous tracks in the presence of a significant number of consecutive missed detections.

Accurate navigation of autonomous underwater vehicles (AUVs) is a key task for data collection at sea with high resolution in time and space. Sidescan sonar (SSS), originally developed for imaging the seafloor, has high potential for establishing landmark-aided navigation or simultaneous localization and mapping (SLAM) capabilities on small-scale AUVs. A key task to establish these capabilities is to determine the height of the AUV from the seafloor, also referred to as “flight height.” This paper combines image processing techniques with probabilistic data association to detect and filter AUV flight height from SSS data. The proposed method first aims to detect the edge between the water column and the seabed using image processing techniques. (The pixel index of this edge is proportional to the flight height in meters.) Subsequently, a multisensor probabilistic data association filter (PDAF) fuses the resulting flight height detections computed from images provided by port and star-board SSS transducers to effectively mitigate missed detections and false positives. To facilitate deployments, we evaluate the performance of the proposed approach using real data collected by surface vehicles with SSS and demonstrate accurate flight height estimation from noisy SSS images.

In many multiobject tracking applications, including radar and sonar tracking, after prefiltering the received signal, measurement data is typically structured in cells. The cells, e.g., represent different range and bearing values. However, conventional multiobject tracking methods use so-called point measurements. Point measurements are provided by a preprocessing stage that applies a threshold or detector and breaks up the cell’s structure by converting cell indexes into, e.g., range and bearing measurements. We here propose a Bayesian multiobject tracking method that processes measurements that have been thresholded but are still cell-structured. We first derive a likelihood function that systematically incorporates an adjustable detection threshold which makes it possible to control the number of cell measurements. We then propose a Poisson Multi-Bernoulli (PMB) filter based on the likelihood function for cell measurements. Furthermore, we establish a link to the conventional point measurement model by deriving the likelihood function for point measurements with amplitude information (AM) and discuss the PMB filter that uses point measurements with AM. Our numerical results demonstrate the advantages of the proposed PMB filter for thresholded cell measurements compared to the conventional PMB filter for point measurements with and without AM.

We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOAs predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.

Multiobject tracking (MOT) is an important task in robotics, autonomous driving, and maritime surveillance. Traditional work on MOT is model-based and aims to establish algorithms in the framework of sequential Bayesian estimation. More recent methods are fully data-driven and rely on the training of neural networks. The two approaches have demonstrated advantages in certain scenarios. In particular, in problems where plenty of labeled data for the training of neural networks is available, data-driven MOT tends to have advantages compared to traditional methods. A natural thought is whether a general and efficient framework can integrate the two approaches. This paper advances a recently introduced hybrid model-based and data-driven method called neural-enhanced belief propagation (NEBP). Compared to existing work on NEBP for MOT, it introduces a novel neural architecture that can improve data association and new object initialization, two critical aspects of MOT. The proposed tracking method is leading the nuScenes LiDAR-only tracking challenge at the time of submission of this paper.

Localization using time-difference of arrival (TDOA) has myriad applications, e.g., in passive surveillance systems and marine mammal research. In this paper, we present a Bayesian estimation method that can localize an unknown number of static sources in 3-D based on TDOA measurements. The proposed localization algorithm based on particle flow (PFL) can overcome the challenges related to the highly nonlinear TDOA measurement model, the data association (DA) uncertainty, and the uncertainty in the number of sources to be localized. Different PFL strategies are compared within a unified belief propagation (BP) framework in a challenging multisensor source localization problem. In particular, we consider PFL-based approximation of beliefs based on one or multiple Gaussian kernels with parameters computed using deterministic and stochastic flow processes. Our numerical results demonstrate that the proposed method can correctly determine the number of sources and provide accurate location estimates. The stochastic flow demonstrates greater accuracy compared to the deterministic flow when using the same number of particles.

This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using sidescan sonar (SSS) data. The presented approach relies on slant range measurements extracted from the received ping of a SSS. In particular, incorporating slant range measurements to landmarks for navigation constrains the location and altitude error of an autonomous platform in GPS-denied environments. The proposed navigation filter consists of a prediction step based on the unscented transform and an update step that relies on particle filtering. The SSS measurement model aims to capture the highly nonlinear nature of SSS data while maintaining reasonable computational requirements in the particle-based update step. For our numerical results, we assume a scenario with a surface vehicle that performs SSS and compass measurements. The simulated scenario is consistent with our current hardware platform. We also discuss how the proposed method can be extended to autonomous underwater vehicles (AUVs) in a straightforward way and why the combination of SSS sensor and compass is particularly suitable for small autonomous platforms.

Passive acoustics can provide a variety of capabilities with applications in oceanographic research and maritime situational awareness. In this paper, we develop a method for the navigation of autonomous underwater vehicles (AUVs) in shallow water. Our approach relies on passively recorded signals from acoustic sources of opportunities (SOOs). By making use of the waveguide invariant, a measurement of the range to the SOO is extracted from the spectrogram of a single hydrophone. Range extraction requires knowledge of the range rate, i.e., the radial velocity between the SOO and AUV, computed from the pressure fields at different time intervals. A particle-based navigation filter fuses the range measurements with the AUV’s internal velocity and heading measurements. As a result, the position error, which would otherwise increase over time, can be bounded. The ability to compute the range rate and range measurements from the pressure field measured using a single hydrophone is demonstrated on real data from the SWellEx-96 experiment. The capability of the developed navigation filter is shown based on synthetic data generated by the normal mode program KRAKEN.

Tracking targets based on measurements provided by radar, sonar, or lidar sensors is essential to obtain situational awareness in important applications, including autonomous navigation and applied ocean sciences. A key challenge in multitarget tracking is the unknown association between the available measurements and the targets to be tracked. In particular, robust data association for closely spaced targets requires advanced methods that explicitly model unresolved measurements. Due to limited sensor resolution, the sensor produces a single measurement for two or more actual targets. If not explicitly modeled in the multitarget tracking method, unresolved measurements lead to track losses and thus, to significant tracking errors. In this paper, we propose a scalable data association method for the tracking of multiple potentially unresolved targets. A loopy belief propagation method is presented that efficiently approximates the marginal association probabilities given a set of potentially unresolved measurements. This method scales quadratically in the number of targets and linearly in the number of measurements. Our numerical results demonstrate that the computed approximate marginal association probabilities are close in $L_{1}$ distance to the true marginal association probabilities, which can only be calculated for very small tracking scenarios.

We present a Bayesian method for sequential direction finding based on variational line spectral estimation (VALSE). The proposed method promotes sparse solutions by means of a Bernoulli-Gaussian amplitude model, is grid-less, and provides marginal posterior distributions from which DOA estimates and their uncertainties can be extracted. Simulation results demonstrate performance improvements in the considered scenario. We also evaluate the proposed method using acoustic data from an underwater source localization experiment.

Location aware networks will introduce new applications and services for modern convenience, the military, and public safety. In this paper, we introduce a Bayesian method for network localization and navigation in the presence of measurement-origin uncertainty (MOU). In the envisioned cooperative scenario, the agents in a dynamic network aim to better localize themselves by performing pairwise observations with other agents in their environment and sharing their location information. Since pairwise observations suffer from MOU, a data association problem has to be solved before an agent can update its location information. In our approach, joint inference is performed through a factor graph formulation of the entire, network-wide estimation problem. Performing the loopy sum-product algorithm on the derived factor graph results in a distributed and scalable inference algorithm. Simulation results demonstrate that cooperation among agents can significantly improve the localization accuracy even in the presence of MOU.

The Bernoulli filter (BF) is a Bayes-optimal method for target tracking when the target can be present or absent in unknown time intervals and the measurements are affected by clutter and missed detections. We propose a distributed particle-based multisensor BF algorithm that approximates the centralized multisensor BF for arbitrary nonlinear and non-Gaussian system models. Our distributed algorithm uses a new extension of the likelihood consensus (LC) scheme that accounts for both target presence and absence and includes an adaptive pruning of the LC expansion coefficients. Simulation results for a heterogeneous sensor network with significant noise and clutter show that the performance of our algorithm is close to that of the centralized multisensor BF.