Stephane Blouin

Intermission-based adaptive structure estimation of wireless underwater networks

Performing distributed computation over underwater wireless sensor networks is challenging because no particular node may know the exact network size and structure. Recent techniques for estimating network structure or size are practically deficient for accomodating underwater networks made of a combination of fixed and mobile sensors. A solution is proposed to estimate dynamic network structures based on node-to-node intermissions. The current approach is more general than existing techniques and it enables distributed computing over underwater networks. In particular, broadcasts are considered and an adaptable network-structure estimation technique is derived. Simulation results support the proposed solution.

A Distributed Particle Filter for Bearings-Only Tracking on Spherical Surfaces

We present a distributed particle filter for bearings-only tracking of a target moving on the surface of a sphere, such as Earth. The proposed filter accounts for the curvature of the surface in the measurement model for more robust performance. In addition, a linearization of the likelihood function significantly reduces the communication overhead. Simulations demonstrate that the proposed distributed approach maintains accuracy comparable to that of a centralized filter with access to all measurements even when the sensors and target are spread over a large region, where a planar approximation would fail.

General solution and approximate implementation of the multisensor multitarget CPHD filter

Random finite set (RFS) based filters such as the cardinalized probability hypothesis density (CPHD) filter have been successfully applied to the problem of single sensor multitarget tracking. Various multisensor extensions of these filters have been proposed in the literature, but exact update equations for the multisensor CPHD filter have not been identified. In this paper, we provide the update equations and propose an approximate implementation. The exact implementation of the multisensor CPHD filter is infeasible even for very simple scenarios. We develop an algorithm that greedily searches for the most likely groups of measurement subsets. This enables a computationally tractable implementation. Numerical simulations are performed to compare the proposed filter implementation with other random finite set based filters.

Distributed consensus control of unicycle agents in the presence of external disturbances

Abstract In this paper, a distributed consensus control strategy is presented for a team of unicycle agents subject to external disturbances. Bounded disturbances with unknown dynamics on both translational and angular velocities are applied to the system. The key idea is to design the control inputs of each agent in such a way that, after a finite time, agents move with an acute angle with respect to a reference vector typically used for the consensus control of disturbance-free single-integrator agents. Convergence to consensus is then proved using Lyapunov theory. Simulation results confirm the efficacy of the proposed controller.

Multisensor CPHD filter

The single-sensor probability hypothesis density (PHD) and cardinalized probability hypothesis density (CPHD) filters have been developed in the literature using the random finite set framework. The existing multisensor extensions of these filters have limitations such as sensor-order dependence, numerical instability, or high computational requirements. In this paper, we derive update equations for the multisensor CPHD filter. The multisensor PHD filter is derived as a special case. Exact implementation of the multisensor CPHD involves sums over all partitions of the measurements from different sensors and is thus intractable. We propose a computationally tractable approximation that combines a greedy measurement partitioning algorithm with the Gaussian mixture representation of the PHD. Our greedy approximation method allows the user to control the trade-off between computational overhead and approximation accuracy.

Simulation of underwater communications with a colored noise approximation and mobility

We study the software simulation of physical properties of underwater communications, namely, underwater acoustic waves and (de)modulation of underwater acoustic digital data signals. We take into account the mobility of nodes, present in underwater sensor networks. We also consider the integration with protocol layers above the physical layer, i.e., the link and network layers. In this context, mobility is relevant because there are underwater vehicles and environmental conditions causing displacements of sensors. Attenuation is sensitive to transmitter-receiver separation distance. Because of mobility, this separation distances is variable. Our simulation approach is based on the work of Borrowski (2010). The physical layer is modeled as MATLAB functions. As a function of distance and frequency, the model takes into account attenuation, noise and their effects on a phase-shift keying signal. We use OMNeT++ to model link and network layer protocols. The MATLAB functions and OMNet++ models are linked together. While MATLAB does particularly well with signal processing, OMNeT++ addresses better the protocols placed in the link layer and above.

Global network connectivity assessment via local data exchange for underwater acoustic sensor networks

This paper studies the problem of distributed connectivity assessment for a network of underwater sensors in a data aggregation mission. Motivated by a sufficient condition for asymptotic almost sure consensus in a network defined over a random digraph, vertex connectivity of the expected communication graph is used as a measure for the connectivity of the underwater sensor network. A distributed update scheme is proposed in which the sensors update their perception of the expected communication graph. The expected communication graph can be characterized by its associated probability matrix. A learning algorithm is employed by each sensor to update its belief on the probabilities using the broadcast messages it receives. Each sensor uses a polynomial-time algorithm to estimate the degree of vertex connectivity of the expected graph based on its perception of the network graph. The proposed algorithms can also handle changes in the topology of the network such as node addition, node deletion, and time-varying probabilities. The performance of the proposed algorithms is validated in simulation.

Distributed underwater acoustic source localization and tracking

We consider the problem of localizing and tracking an acoustic noise source under water using bearing measurements taken from a small collection of acoustic sensors. Nodes must cooperate in order to improve their estimates and overcome significant noise levels and spurious measurements from clutter. However the underwater communication channel is highly unreliable, which makes coordination challenging. We evaluate the performance of distributed particle filtering methods in the setting where nodes communicate over unreliable links. Our results are validated using data from an experiment conducted at sea.

A consensus control strategy for unicycles in the presence of disturbances

In this paper, a distributed consensus control strategy for a team of unicycle agents subject to disturbance is presented. Disturbances on both the translational and angular velocities are considered. It is assumed that disturbance dynamics are known a priori while their initial conditions are unknown. The norm and the angle of a typical control vector for the consensus of disturbance-free single-integrator agents is used to design the proposed controllers. The control input for each agent consists of two parts. One part which leads to consensus in disturbance-free case, and a second term which compensates for the disturbances. The simulation results confirm the efficacy of the proposed controllers.

Location-free link state routing for underwater acoustic sensor networks

We propose a location-free link state routing protocol for Underwater Acoustic Sensor Networks (UASNs). Additionally, we present the mathematical background for the theoretical capacity and transmission power metrics of an underwater acoustic channel. UASNs are formed by devices enabled with acoustic communication capabilities that are deployed underwater to perform collaborative monitoring tasks. Information is collected by a sink at the surface also equipped with a radio. The underwater communication channel is characterized by a limited bandwidth and high propagation delay. The network topology constantly changes due to mobility of the nodes. In our routing protocol, every node ranks the quality of the path that it offers toward the sink. Packet forwarding is performed hop-by-hop considering one or several routing metrics, e.g., hop count or pressure. To avoid communication void problems, every node selects a one-hop neighbor within an area that guarantees progress toward a sink. Our strategy is loop-free. It includes a recovery mode handling network topology changes. Our routing protocol was implemented in NS-3 to conduct experiments.