Sunmaster: An SEP cargo vehicle for Mars missions

Options are examined for an unmanned solar powered electric propulsion cargo vehicle for Mars missions. The 6 prime areas of study include: trajectory, propulsion system, power system, supporting structure, control system, and launch consideration. Optimization of the low thrust trajectory resulted in a total round trip mission time just under 4 years. The argon propelled electrostatic ion thruster system consists of seventeen 5 N engines and uses a specific impulse of 10,300 secs. At Earth, the system uses 13 engines to produce 60 N of thrust; at Mars, five engines are used, producing 25 N thrust. The thrust of the craft is varied between 60 N at Earth and 24 N at Mars due to reduced solar power available. Solar power is collected by a Fresnel lens concentrator system using a multistacked cell. This system provides 3.5 MW to the propulsion system after losses. Control and positioning to the craft are provided by a system of three double gimballed control moment gyros. Four shuttle 'C' launches will be used to transport the unassembled vehicle in modular units to low Earth orbit where it will be assembled using the Mobile Transporter of the Space Station Freedom

PyMieDAP: a Python--Fortran tool to compute fluxes and polarization signals of (exo)planets

PyMieDAP (the Python Mie Doubling-Adding Programme) is a Python--based tool for computing the total, linearly, and circularly polarized fluxes of incident unpolarized sun- or starlight that is reflected by, respectively, Solar System planets or moons, or exoplanets at a range of wavelengths. The radiative transfer computations are based on an adding--doubling Fortran algorithm and fully include polarization for all orders of scattering. The model (exo)planets are described by a model atmosphere composed of a stack of homogeneous layers containing gas and/or aerosol and/or cloud particles bounded below by an isotropically, depolarizing surface (that is optionally black). The reflected light can be computed spatially--resolved and/or disk--integrated. Spatially--resolved signals are mostly representative for observations of Solar System planets (or moons), while disk--integrated signals are mostly representative for exoplanet observations. PyMieDAP is modular and flexible, and allows users to adapt and optimize the code according to their needs. PyMieDAP keeps options open for connections with external programs and for future additions and extensions. In this paper, we describe the radiative transfer algorithm that PyMieDAP is based on and the code's principal functionalities. And we provide benchmark results of PyMieDAP that can be used for testing its installation and for comparison with other codes. PyMieDAP is available online under the GNU GPL license at http://gitlab.com/loic.cg.rossi/pymiedapComment: 15 pages, 7 figures, 4 tables. Accepted for publication in Astronomy and Astrophysic

Unit circle MVDR beamformer

The array polynomial is the z-transform of the array weights for a narrowband planewave beamformer using a uniform linear array (ULA). Evaluating the array polynomial on the unit circle in the complex plane yields the beampattern. The locations of the polynomial zeros on the unit circle indicate the nulls of the beampattern. For planewave signals measured with a ULA, the locations of the ensemble MVDR polynomial zeros are constrained on the unit circle. However, sample matrix inversion (SMI) MVDR polynomial zeros generally do not fall on the unit circle. The proposed unit circle MVDR (UC MVDR) projects the zeros of the SMI MVDR polynomial radially on the unit circle. This satisfies the constraint on the zeros of ensemble MVDR polynomial. Numerical simulations show that the UC MVDR beamformer suppresses interferers better than the SMI MVDR and the diagonal loaded MVDR beamformer and also improves the white noise gain (WNG).Comment: Accepted to ICASSP 201

Passive radar on moving platforms exploiting DVB-T transmitters of opportunity

The work, effort, and research put into passive radar for stationary receivers have shown significant developments and progress in recent years. The next challenge is mounting a passive radar on moving platforms for the purpose of target detection and ground imaging, e.g. for covert border control. A passive radar on a moving platform has many advantages and offers many benefits, however there is also a considerable drawback that has limited its application so far. Due to the movement the clutter returns are spread in Doppler and may overlap moving targets, which are then difficult to detect. While this problem is common for an active radar as well, with a passive radar a further problem arises: It is impossible to control the exploited time-varying waveform emitted from a telecommunication transmitter. A conventional processing approach is ineffective as the time-varying waveform leads to residuals all over the processed data. Therefore a dedicated clutter cancellation method, e.g. the displaced phase centre antenna (DPCA) approach, does not have the ability to completely remove the clutter, so that target detection is considerably limited. The aim must be therefore to overcome this limitation by exploiting a processing technique, which is able to remove these residuals in order to cope with the clutter returns thus making target detection feasible. The findings of this research and thesis show that a reciprocal filtering based stage is able to provide a time-invariant impulse response similar to the transmissions of an active radar. Due to this benefit it is possible to achieve an overall complete clutter removal together with a dedicated DPCA stage, so that moving target detection is considerably improved, making it possible in the first place. Based on mathematical analysis and on simulations it is proven, that by exploiting this processing in principle an infinite clutter cancellation can be achieved. This result shows that the reciprocal filter is an essential processing stage. Applications on real data acquired from two different measurement campaigns prove these results. By the proposed approach, the limiting factor (i.e. the time-varying waveform) for target detection is negotiated, and in principle any clutter cancellation technique known from active radar can be applied. Therefore this analysis and the results provide a substantial contribution to the passive radar research community and enables it to address the next questions

Airborne Radar Interference Suppression Using Adaptive Three-Dimensional Techniques

This research advances adaptive interference suppression techniques for airborne radar, addressing the problem of target detection within severe interference environments characterized by high ground clutter levels, levels, noise jammer infiltration, and strong discrete interferers. Two-dimensional (2D) Space-Time Adaptive Processing (STAP) concepts are extended into three-dimensions (3D) by casting each major 2D STAP research area into a 3D framework. The work first develops an appropriate 3D data model with provisions for range ambiguous clutter returns. Adaptive 3D development begins with two factored approaches, 3D Factored Time-Space (3D-FTS) and Elevation-Joint Domain Localized (Elev-JDL). The 3D adaptive development continues with optimal techniques, i.e., joint domain methods. First, the 3D matched Filter (3D-MF) is derived followed by a 3D Adaptive Matched Filter (3D-AMF) discussion focusing on well-established practical limitations consistent with the 2D case. Finally, a 3D-JDL method is introduced. Proposed 3D Hybrid methods extend current state-of-the-art 2D hybrid methods. The initial 3D hybrid, a functional extension of the 2D technique, exhibits distinct performance advantages in heterogeneous clutter. The final 3D hybrid method is virtually impervious to discrete interference