Paolo Braca

Maritime Surveillance Using Multiple High-Frequency Surface-Wave Radars

In the last decades, great interest has been directed toward low-power high-frequency (HF) surface-wave radars as long-range early warning tools in maritime-situational-awareness applications. These sensors, developed for ocean remote sensing, provide an additional source of information for ship detection and tracking, by virtue of their over-the-horizon coverage capability and continuous-time mode of operation. Unfortunately, they exhibit many shortcomings that need to be taken into account, such as poor range and azimuth resolution, high nonlinearity, and significant presence of clutter. In this paper, radar detection, multitarget tracking, and data fusion (DF) techniques are applied to experimental data collected during an HF-radar experiment, which took place between May and December 2009 on the Ligurian coast of the Mediterranean Sea. The system performance is defined in terms of time on target (ToT), false alarm rate (FAR), track fragmentation, and accuracy. A full statistical characterization is provided using one month of data. The effectiveness of the tracking and DF procedures is shown in comparison to the radar detection algorithm. In particular, the detectors FAR is reduced by one order of magnitude. Improvements, using the DF of the two radars, are also reported in terms of ToT as well as accuracy.

Bayesian Tracking in Underwater Wireless Sensor Networks With Port-Starboard Ambiguity

Port-starboard ambiguity is an important issue in underwater tracking systems with anti-submarine warfare applications, especially for wireless sensor networks based upon autonomous underwater vehicles. In monostatic systems this ambiguity leads to a ghost track of the target symmetrically displaced with respect to the sensor. Removal of such artifacts is usually made by rough and heuristic approaches. In the context of Bayesian filtering approximated by means of particle filtering techniques, we show that optimal disambiguation can be pursued by deriving the full Bayesian posterior distribution of the target state. The analysis is corroborated by simulations that show the effectiveness of the particle-filtering tracking. A full validation of the approach relies upon real-world experiments conducted by the NATO Science and Technology Organization - Centre for Maritime Research and Experimentation during the sea trials Generic Littoral Interoperable Network Technology 2011 and Exercise Proud Manta 2012, results which are also reported.

Asymptotic Efficiency of the PHD in Multitarget/Multisensor Estimation

Tracking an unknown number of objects is challenging, and often requires looking beyond classical statistical tools. When many sensors are available the estimation accuracy can reasonably be expected to improve, but there is a concomitant rise in the complexity of the inference task. Nowadays, several practical algorithms are available for multitarget/multisensor estimation and tracking. In terms of current research activity one of the most popular is the probability hypothesis density, commonly referred to as the PHD, in which the goal is estimation of object locations (unlabeled estimation) without concern for object identity (which is which). While it is relatively well understood in terms of its implementation, little is known about its performance and ultimate limits. This paper is focused on the characterization of PHD estimation performance for the static multitarget case, in the limiting regime where the number of sensors goes to infinity. It is found that the PHD asymptotically behaves as a mixture of Gaussian components, whose number is the true number of targets, and whose peaks collapse in the neighborhood of the classical maximum likelihood estimates, with a spread ruled by the Fisher information. Similar findings are obtained with reference to a naïve, two-step algorithm which first detects the number of targets, and then estimates their positions.

Experimental Evaluation of the Range–Doppler Coupling on HF Surface Wave Radars

High-frequency surface wave radar (HFSWR) is used in oceanography to monitor surface wind waves and currents and, more recently, to detect ships in maritime surveillance. The radar accuracy is affected by range-Doppler coupling, which yields a displacement in the measured range proportional to the target radial velocity, i.e., the Doppler shift in the returned pulse. Although in oceanography this effect is usually not accounted for, its relevance grows in ship detection. In this letter, we present the results of two experimental data sets showing displacements in the HFSWR range measurements of up to 300 m and confirming the theoretical analysis. Furthermore, we show that the correction based on theoretical arguments, achieved by the statistical correlation between the range and Doppler measurements, provides remarkable improvement in the radar accuracy.

Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems

These last decades spawned a great interest toward low-power high-frequency (HF) surface-wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early warning tools in maritime situational awareness applications providing an additional source of information for target detection and tracking. Unfortunately, they also exhibit many shortcomings that need to be taken into account, and proper algorithms need to be exploited to overcome their limitations. In this paper, we develop a knowledge-based (KB) multitarget tracking methodology that takes advantage of a priori information on the ship traffic. This a priori information is given by the ship sea lanes and by their related motion models, which together constitute the basic building blocks of a variable structure interactive multiple model procedure. False alarms and missed detections are dealt with using a joint probabilistic data association rule and nonlinearities are handled by means of the unscented Kalman filter. The KB-tracking procedure is validated using real data acquired during an HF-radar experiment in the Ligurian Sea (Mediterranean Sea). Two HFSW radar systems were operated to develop and test target detection and tracking algorithms. The overall performance is defined in terms of time-on-target, false-alarm rate (FAR), track fragmentation (TF), and accuracy. A full statistical characterization is provided using one month of data. A significant improvement of the KB-tracking procedure, in terms of system performance, is demonstrated in comparison with a standard joint probabilistic data association tracker recently proposed in the literature to track HFSW radar data. The main improvement of our approach is the better capability of following targets without increasing the FAR. This increment is much more evident in the region of low FAR, where it can be over the 30% for both the HFSW radar systems. The KB-tracking exhibits on average a reduction of the TF of about the 20% and the 13% of the utilized HFSW-radar systems.

Single-Transmission Distributed Detection via Order Statistics

Consider a sensor network made of remote nodes connected to a common fusion center. In a recent work, Blum and Sadler proposed the idea of ordered transmissions-sensors with more informative measurements deliver their messages first-and they proved that optimal detection performance can be achieved using only a subset of the measurements available to the system. Taking to one extreme this approach, we show that using only one transmission the detection error can be made as small as desired, provided that the network size n is large enough. Indeed, we design a distributed detection scheme and prove its asymptotic consistency with respect to n, when the decision is made using just one-but the best-out of n collected samples.

Gamma Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking Using X-Band Marine Radar Data

X-band marine radar systems represent a flexible and low-cost tool for the tracking of multiple targets in a given region of interest. Although suffering several sources of interference, e.g., the sea clutter, these systems can provide high-resolution measurements, both in space and time. Such features offer the opportunity to get accurate information not only about the target position/motion but also about the targets size. Accordingly, in this paper, we exploit emergent extended target tracking (ETT) methodologies in which the target state, typically position/velocity/acceleration, is augmented with the target length and width. In this paper, we propose an ETT procedure based on the popular probability hypothesis density filter, and in particular, we describe the extended target state through the gamma Gaussian inverse Wishart model. The comparative simplicity of the used models allows us to meet the real-time processing constraint required for the practical surveillance purposes. Real-world data from an experimental and operational campaign, collected during the recovery operations of the Costa Concordia wreckage in October 2013, are used to assess the performance of the proposed target tracking methodology. The full signal processing chain is implemented, and considerations of the experimental results are provided. Important nonideal effects, common to every marine radar, are observed and discussed in relation to the assumptions made for the tracking procedure.

Distributed Information Fusion in Multistatic Sensor Networks for Underwater Surveillance

Surveillance in antisubmarine warfare has traditionally been carried out by means of submarines or frigates with towed arrays. These techniques are manpower intensive. Alternative approaches have recently been suggested using distributed stationary and mobile sensors, such as autonomous underwater vehicles (AUVs). In contrast with the use of standard assets, these small, low-power, and mobile devices have limited processing and wireless communication capabilities. However, when deployed in a spatially separated network, these sensors can form an intelligent network achieving high performance with significant features of scalability, robustness, and reliability. The distributed information FUSION (DIFFUSION) strategy, in which the local information is shared among sensors, is one of the key aspects of this intelligent network. In this paper, we propose two DIFFUSION schemes, in which the information shared among sensors consists of: 1) contacts, generated by the local detection stage and 2) tracks, generated by the local tracking stage. In the first DIFFUSION scheme, contacts are combined at each nodes using the optimal Bayesian tracking based on the random finite set formulation. In the second DIFFUSION scheme, tracks are combined using the track-to-track association/fusion procedure, then a sequential decision based on the association events is exploited. A full validation of the DIFFUSION schemes is conducted by the NATO Science and Technology Organization-Center for maritime research and experimentation during the sea trials Exercise Proud Manta 2012-2013 using real data. Performance metrics of DIFFUSION and of local tracking/detection strategies are also evaluated in terms of time-on-target (ToT) and false alarm rate (FAR). We demonstrate the benefit of using DIFFUSION against the local noncooperative strategies. In particular DIFFUSION improves the level of TOT (FAR) with respect to the local tracking/detection strategies. In particular, the TOT is increased over 90%-95% while the FAR is reduced of two order of magnitude. The problem of communication failures, data not available from the collaborative AUV during certain periods of time, is also investigated. The robustness of DIFFUSION with respect to these communication failures is demonstrated, and the related performance results are reported here. In particular, with 75% of communication failures the ToT is over 90%-95% with a relatively small increase of the FAR with respect to the case of perfect communication.

A linear complexity particle approach to the exact multi-sensor PHD

Recently it has been shown that the Multi-Sensor Probability Hypothesis Density (MS-PHD) has some optimality properties in the regime of large number of sensors [1, 2], achieving the same performance of the Bayes multi-sensor/multi-target posterior in the Random Finite Set (RFS) framework [3]. However, when the number of sensors N is relatively large, the traditional PHD filter loses its computational efficiency, the complexity being exponential in N. On the other hand, the complexity of the full Bayes posterior is only linear in N, and this paper suggests an idea for its computation using Sequential Monte Carlo (SMC) methods. The MS-PHD is then evaluated, and numerical examples show that it is possible to deal with a scenario where the number of sensors is very large while targets, appearing and disappearing, evolve in time.

Selective Measurement Transmission in Distributed Estimation With Data Association

In distributed multisensor estimation/tracking the problem of fusion is complicated by that of data association (i.e., with false alarms and missed detections): not only is it of concern to provide an estimation-efficient sensor level quantization of the “target-originated” measurement, but it is also unclear which among each sensors measurements this might be, if any at all. The former issue has been studied previously; in this paper we address only the latter concern. At first we assume that each sensor is tasked to communicate exactly one of its observations to a Fusion Center (FC) for a global estimate, and we work in one dimension. Via order statistics we show that, surprisingly, the nearest neighbor (NN) is not always the most appropriate measurement to share. We also expand our bandwidth to allow for transmission of multiple measurements, for example the nearest and third-nearest: it turns out that a single-measurement transmission is more bandwidth efficient than multiple. The analysis and results are further extended to two dimensions, but the moral-that sharing of the NNs is not always a good idea-remains.