Murat Uney

This paper describes a sea trial conducted by CMRE in the coastal waters of La Spezia, Italy. It presents a selection of the most significant results achieved using the field data collected with an underwater surveillance network of fixed and mobile platforms equipped with advanced passive acoustics sensors. The surveillance network consists of autonomous, long-endurance, low-power, smart sensor nodes capable of automatically generating sonar contacts from acoustic data in real time and of sharing these contacts with the other nodes of the network through acoustic modems. The sonar contacts bring information on time stamp, azimuth, elevation and frequency of each detected noise source (target). The paper focuses on real-time passive detection, localization & tracking of artificial targets and vessels of opportunity as well on cooperative autonomy among the mobile assets of the network.

We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example.

We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example.

This paper develops optimal procedures for point estimation with Bernoulli filters. These filters are of interest to radar and sonar surveillance because they are designed for stochastic targets that can enter and exit the surveillance region at random instances. Because of this property they are not served by the minimum mean square estimator, which is the most widely used approach to optimal point estimation. Instead of the squared error loss, this paper proposes an application-oriented loss function that is compatible with Bernoulli filters, and it develops two significant practical estimators: the minimum probability of error estimate (which is based on the rule of ideal observer), and the minimum mean operational loss estimate (which models a simple defence scenario).

In this work, the authors consider joint micro‐Doppler signature estimation and track‐before‐detect for detecting and classifying manoeuvring and small rotary‐wing aircraft with a phased array radar receiver. Such aircraft induce low signal‐to‐noise ratio reflections which complicates their detection. These difficulties are exacerbated by the high manoeuvrability of such aircraft which results with resolution cell migrations in conventional detectors. The authors propose a novel track‐before‐detect algorithm which jointly estimates the micro‐Doppler signature, dominant reflectivity and kinematic trajectory of an object before the decision on its existence is made. These quantities are used in a Neyman‐Pearson test against a constant false alarm rate threshold which is equivalent to evaluating a coherent long time integration value. The authors demonstrate the efficacy of the proposed scheme in a real data experiment and compare it with conventional techniques.

In this work, we consider the estimation of micro-Doppler signatures of small rotary-wing aircraft such as drones using a pulsed radar with a uniform planar array (UPA) receiver in order to identify/classify them within the processing performed for their detection. Such objects induce low signal to noise ratio (SNR) reflections which complicate their detection and Doppler processing. We propose a novel track-before-detect algorithm which simultaneously estimates complex reflection coefficients, micro-Doppler features in the form of additional frequency shifts generated by rotating blades and the motion trajectory before the decision on object existence is made. We demonstrate the efficacy of our estimation scheme in a simulation example and compare with conventional techniques.

In this work, we consider maximum likelihood estimation of parameters in a stochastic trajectory model. The velocity paths are generated from an Ornstein-Uhlenbeck process and thus revert to a latent expected value. In addition to this expected velocity, parameters that specify the reversion characteristics and the process noise covariance determine the behaviour of typical trajectories of the model. Estimation of these parameters from trajectory samples facilitates learning of patterns and training of predictive models using trajectory data, e.g., automatic identification system (AIS) messages transmitted by vessels. We propose a six-degrees-of-freedom parameterisation and investigate the identifiability of these parameters using the Cramér-Rao bound matrix which we estimate using Monte Carlo methods. We demonstrate that some parameter configurations of interest are identifiable and their maximum likelihood estimate can be found using iterative optimisation algorithms. We demonstrate the efficacy of this approach on both simulated and real data.