Alessio Balleri

Maximum likelihood estimation for compound-gaussian clutter with inverse gamma texture

The inverse gamma distributed texture is important for modeling compound-Gaussian clutter (e.g. for sea reflections), due to the simplicity of estimating its parameters. We develop maximum-likelihood (ML) and method of fractional moments (MoFM) estimates to find the parameters of this distribution. We compute the Cramer-Rao bounds (CRBs) on the estimate variances and present numerical examples. We also show examples demonstrating the applicability of our methods to real lake-clutter data. Our results illustrate that, as expected, the ML estimates are asymptotically efficient, and also that the real lake-clutter data can be very well modeled by the inverse gamma distributed texture compound-Gaussian model.

Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar

Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.

Frequency-agile non-coherent ultrasound radar for collection of micro-Doppler signatures

Classification of targets by micro-Doppler signatures has attracted a growing interest in recent years. The main bulk translation of a target and additional target motions, such as vibrations and rotations, generate Doppler modulations in the echo that contain unique target features and thus can be used to perform target recognition. Although, target classification by micro-Doppler signatures has been exploited in the RF regime for radar systems, the frequency spectrum is becoming increasingly congested and expensive to use, so that it is desirable to identify and exploit other types which have similar capabilities. In this paper a frequency-agile non-coherent ultrasound radar developed to gather micro-Doppler signatures is presented. This was used in an experimental trial to gather micro-Doppler signatures of personnel targets whilst undertaking various types of motion. Classification performance by these same micro-Doppler signatures is then assessed and results discussed.

Measurements of bistatic radar sea clutter

Bistatic radar is a technique of considerable potential importance and interest. Despite this, current understanding of the properties of bistatic clutter, and in particular, bistatic sea clutter, is limited at best. We present some results of a recent trials campaign to gather bistatic sea clutter data, and to analyse the data in order to develop models to represent bistatic radar sea clutter. The results indicate that the shape parameter of the compound K distribution model fitted to the data tends to be higher for the bistatic clutter than for the equivalent monostatic clutter. This suggests that a bistatic geometry may allow a lower detection threshold for a given probability of false alarm, and hence give improved detection performance of weak targets against a clutter background compared to the conventional monostatic geometry.

Flower classification by bats: Radar comparisons

In recent years, with the development of high-range resolution radars, the desire to identify targets under all weather and clutter conditions has become of great importance. This is an activity carried out with great success by echo-locating mammals such as nectar feeding bats that are able to detect and select flowers of bat-pollinated plants, even in a dense clutter environment. Herein, data consisting of acoustically-generated high-range resolution profiles of four bat-pollinated flower heads are analysed. Multi-perspective classification performance is assessed and compared with the radar case. There are close parallels that suggest lessons can be learnt from nature.

Biomimetic Echolocation With Application to Radar and Sonar Sensing

Nature provides a number of examples where acoustic echolocation is the primary sensing modality, the most well known of these being the bat, whale, and dolphin. All demonstrate a remarkable ability to “see with sound.” Using echolocation, they navigate, locate, and capture prey. As species, they have not only survived but have thrived in all their individual environments, often solely reliant on echolocation. All of these creatures are inherently cognitive. They all maintain a perception of their environment through the nervous system that allows them to take actions. In this paper, we focus on the bat as an example of a cognitive system exploiting a memory-driven perception-action cycle, enabling it to navigate and interact with its environment. The key conceptual components of cognition and how it could be applied to man-made echoic sensors is introduced. This is followed by a description of how echoic flow fields, a bio-inspired technique that bats have been shown to use, fit guidance, and control problems. We then go on to explain how bats are able to reliably distinguish between different targets. A combination of the theory and examples is used to demonstrate the vast potential for advancing the capability of made in man-made systems by adopting aspects of natural echolocating cognitive dynamic systems.

Impact of flight trajectory on the detection and selection of flowers by nectar-feeding bats

Echolocating mammals, such as bats, have evolved an excellent ability to detect, select and identify the targets they depend on for their survival by echolocation even in the most challenging environments. They simultaneously adapt their trajectory and diversify transmission parameters such as waveform, bandwidth and time duration, to obtain high level detection and classification performance that is far better than that obtained by modern radar and sonar systems. This paper exploits the information available to the bat along its in-flight trajectory aiming at assessing the impact of choosing the right trajectory on detection and classification performance. A set of experimental data consisting of HRRPs of one inflorescence of Rhytidophyllum auriculatum is analysed and results are discussed and related to the case of radar and sonar systems.

NetRAD multistatic sea clutter database

This paper describes a multistatic, sea clutter and vessel database, assembled using the three node, S Band radar, known as NetRAD (for, “Netted Radar”). This data was collected in the United Kingdom and South Africa during 2009-201. It is the intention of the investigators and sponsors of this work to make the raw data available to individuals and organisations interested in processing this multistatic data in support of the development of the understanding of sea clutter and vessel properties in the multistatic configuration.

Classification of flowers by bats: comparison with the radar case

In recent years, with the development of high range resolution radars, the desire to be able to identify targets under all weather and clutter conditions has become of great importance. This is an activity carried out with great success by echolocating mammals such as nectar feeding bats that are able to detect and select flowers of bat-pollinated plants even in a dense clutter environment. In this paper data consisting of acoustically generated high range resolution profiles of four bat pollinated flower heads are analysed. Multi perspective classification performance is assessed and compared with the radar case. There are close parallels that suggest lessons can be learnt from nature.

Bat-inspired ultrasound tomography in air

Classification of targets is of great interest to the radar and sonar community. The development of modern systems has resulted in a drive to improve automatic target recognition in order to be able to identify targets under all weather and clutter conditions. Echolocating mammals, such as bats, have an imperative to identify the targets they depend on for their survival and have evolved an excellent ability to detect, select, and attack prey even in the most challenging environments. They acquire multi perspective information as to construct an image of the target in the way a typical tomographic image is developed. In this paper ultrasound high range resolution profiles of a real scaled target are processed to exploit tomographic images at ultrasound frequencies in air. Results are discussed and related to the radar case.