Multiobject Tracking for Thresholded Cell Measurements

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 method that relies on thresholded cell measurements compared to the conventional multiobject tracking based on point measurements with and without AM.

A Probabilistic Focalization Approach for Single Receiver Underwater Localization

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.

Learning Dynamics from Multicellular Graphs with Deep Neural Networks

The inference of multicellular self-assembly is the central quest of understanding morphogenesis, including embryos, organoids, tumors, and many others. However, it has been tremendously difficult to identify structural features that can indicate multicellular dynamics. Here we propose to harness the predictive power of graph-based deep neural networks (GNN) to discover important graph features that can predict dynamics. To demonstrate, we apply a physically informed GNN (piGNN) to predict the motility of multicellular collectives from a snapshot of their positions both in experiments and simulations. We demonstrate that piGNN is capable of navigating through complex graph features of multicellular living systems, which otherwise can not be achieved by classical mechanistic models. With increasing amounts of multicellular data, we propose that collaborative efforts can be made to create a multicellular data bank (MDB) from which it is possible to construct a large multicellular graph model (LMGM) for general-purposed predictions of multicellular organization.

A Belief Propagation Approach for Direct Multipath-Based SLAM

In this work, we develop a multipath-based simultaneous localization and mapping (SLAM) method that can directly be applied to received radio signals. In existing multipath-based SLAM approaches, a channel estimator is used as a preprocessing stage that reduces data flow and computational complexity by extracting features related to multipath components (MPCs). We aim to avoid any preprocessing stage that may lead to a loss of relevant information. The presented method relies on a new statistical model for the data generation process of the received radio signal that can be represented by a factor graph. This factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that directly uses received radio signals as measurements. Simulation results in a realistic scenario with a single-input single-output (SISO) channel demonstrate that the proposed direct method for radio-based SLAM outperforms state-of-the-art methods that rely on a channel estimator.

Graph-based Simultaneous Localization and Bias Tracking

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent's position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate the proposed algorithm to significantly outperform state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramer-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.

To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.

A BP Method for Track-Before-Detect

Tracking an unknown number of low-observable objects is notoriously challenging. This letter proposes a sequential Bayesian estimation method based on the track-before-detect (TBD) approach. In TBD, raw sensor measurements are directly used by the tracking algorithm without any preprocessing. Our proposed method is based on a new statistical model that introduces a new object hypothesis for each data cell of the raw sensor measurements. It allows objects to interact and contribute to more than one data cell. Based on the factor graph representing our statistical model, we derive the message passing equations of the proposed belief propagation (BP) method for TBD. Approximations are applied to certain BP messages to reduce computational complexity and improve scalability. In a simulation experiment, our proposed BP-based TBD method outperforms two other state-of-the-art TBD methods.

Toward Terrain-based Navigation Using Side-scan Sonar

This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using side-scan 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.

Navigation in shallow water using passive acoustic ranging

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.

Message Passing-Based 9-D Cooperative Localization and Navigation with Embedded Particle Flow

Cooperative localization (CL) is an important technology for innovative services such as location-aware communication networks, modern convenience, and public safety. We consider wireless networks with mobile agents that aim to localize themselves by performing pairwise measurements amongst agents and exchanging their location information. Belief propagation (BP) is a state-of-the-art Bayesian method for CL. In CL, particle-based implementations of BP often are employed that can cope with non-linear measurement models and state dynamics. However, particle-based BP algorithms are known to suffer from particle degeneracy in large and dense networks of mobile agents with high-dimensional states. This paper derives the messages of BP for CL by means of particle flow, leading to the development of a distributed particle-based message-passing algorithm which avoids particle degeneracy. Our combined particle flow-based BP approach allows the calculation of highly accurate proposal distributions for agent states with a minimal number of particles. It outperforms conventional particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we compare the proposed method to a centralized particle flow-based implementation, known as the exact Daum-Huang filter, and to sigma point BP in terms of position accuracy, runtime, and memory requirement versus the network size. We further contrast all methods to the theoretical performance limit provided by the posterior Cram\'er-Rao lower bound (PCRLB). Based on three different scenarios, we demonstrate the superiority of the proposed method.