Christophe Sintes

CPHD-DOA Tracking of Multiple Extended Sonar Targets in Impulsive Environments

In this paper, we propose a novel phased-array track before detect (TBD) filter for tracking multiple distributed (extended) targets from impulsive observations. Since the targets are angularly spread, we track the centroid Direction Of Arrival (DOA) of the target-generated (or backscattered) signal. The main challenge stems from the random target signals that, conditional to their respective states, constitute non-deterministic contributions to the system observation. The novelty of our approach is twofold. First, we develop a Cardinalized Probability Hypothesis Density (CPHD) filter for tracking multiple targets with non-deterministic contributions, more specifically, Spherically Invariant Random Vector (SIRV) processes. This is achieved by analytically integrating the SIRV and angularly distributed target signals in the update step. Thus, ensuring a more efficient implementation than existing solutions, that generally consider augmenting the target state with the target signal. Secondly, we provide an improved auxiliary particle CPHD filter and clustering methodology. The auxiliary step is carried out for persistent particles, while for newly birthed particles an adaptive importance distribution is given. This is in contrast with existing solutions that only consider the auxiliary step for birthed particles. Simulated data results showcase the improved performance of the proposed filter. Results on real sonar phased-array data are presented for underwater 3D image reconstruction applications.

Model-Based Adaptive 3D Sonar Reconstruction in Reverberating Environments

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

Sonar Data Simulation based on Tube Tracing

Simulating realistic sonar data is crucial for tuning detection and classification algorithms according to environment and acquisition characteristics. Moreover, robustness of performances estimation and prediction applications can be greatly enhanced as soon as such a simulation tool provides both a modular underwater world representation (multiple sensors, environments and acquisition conditions) and a selection of several computational engines (ray theory, parabolic equation ...). Therefore, we developed such a framework for simulators, allowing both scene design and computational engine choice. Within it, two engines (one for rays, one for tubes) has been successfully implemented and realistic simulations obtained, as shown in the presented simulated sonar images. A further step will be to also output the full received acoustic signal.

3-D bathymetric reconstruction in multi-path and reverberant underwater environments

In this paper a 3-D reconstruction method is proposed of sea bottom topography, i.e. bathymetry, for sonar data in highly reverberant and multi-path underwater environments. Recent publications showcase the negative impact of waves involving sea surface reflections on the sea bottom imaging process. The novelty of our proposal is twofold: firstly it relies on the use of Markovian-model based processors for bahymetric reconstruction and involves data association filters. Secondly, we propose a nearest neighbor version of the Integrated Probabilistic Data Association filter, capable of filtering several echo trajectories in the presence of clutter with a relatively reduced complexity compared to other existent methods.

Forward looking sonar data simulation through tube tracing

Simulating realistic sonar data is crucial for tuning detection and classification algorithms according to environment and acquisition characteristics. Moreover, robustness of performances estimation and prediction applications can be greatly enhanced as soon as such a simulation tool provides both a modular underwater world representation (multiple sensors, environments and acquisition conditions) and a selection of several computational engines (ray theory, parabolic equation, …). Therefore, we developed such a framework for simulators, allowing both scene design and computational engine choice. Within it, two engines (one for rays, one for tubes) has been successfully implemented and realistic simulations obtained, as shown in the presented simulated sonar images. This paper introduces the simulation of a new imaging sensor (DIDSON acoustic camera) showing the extensibility of the proposed framework while providing realistic and specific front-looking simulated images. Moreover, in order to bypass the scene resolution given by the facet sizes, some georeferenced perturbations are introduced in order to model sub-facet behaviors and produce more realistic responses from the scene with micro and macro textures on the output images.

Marked poisson point process PHD filter for DOA tracking

In this paper we propose a Track Before Detect (TBD) filter for Direction Of Arrival (DOA) tracking of multiple targets from phased-array observations. The phased-array model poses a new problem since each target emits a signal, called source signal. Existing methods consider the source signal as part of the system state. This is inefficient, especially for particle approximations of posteriors, where samples are drawn from the higher-dimensional posterior of the extended state. To address this problem, we propose a novel Marked Poisson Point Process (MPPP) model and derive the Probability Hypothesis Density (PHD) filter that adaptively estimates target DOAs. The PPP models variations of both the number and the location of points representing targets. The mark of a point represents the source signal, without the need of an extended state. Recursive formulas for the MPPP PHD filter are derived with simulations showcasing improved performance over state-of-the art methods.

Gaussian approximation of interferometric PDF for MLE derivation

The interferometry principle is usually implemented in sonar and radar systems to estimate the arrival angles of backscattered signals at time-sampling rate. This direction-finding method is based on phase-difference measurements between two close receivers. To quantify the associated bathymetric measurement quality, it is necessary to model the statistical properties of the interferometric-phase estimator. The classical interferometric estimator, i.e. arg{s1s2}, has a well known Gaussian-shaped probability density function (PDF). It is important to notice that the interferometric PDF is 2π periodic and is driven by two parameters: mean and correlation coefficient. Thanks to the relationship between these two parameters, the idea is to find a conversion technique between variance and correlation coefficient. A third-order polynomial function performs this easily while a simple affine function provides a rough but efficient approximation. The second step is to fit the PDF with finite support based on the normal distribution. This operation impacts the value of the PDF. From this it becomes possible to derive a convenient approximation of the maximum likelihood estimator with the Gaussian approximation which is well adapted for these calculations.

Sonar Data Simulation

1.1 What is the need for sonar data simulation ? At-sea daily costs for performing data collection are high and acquiring specific data to either validate hypotheses or try new algorithms is often too expensive to be achieved. Thus, due to operational constraints for underwater data acquisition, simulating realistic sonar data, like images and swath bathymetry profiles, is crucial for designing and tuning detection and classification algorithms according to sensors settings, sea-bottom nature and topography. To sum up, an accurate and realistic simulation tool is a very good asset to enhance system & algorithm performances, thus appearing as a complement to real data collections.