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Numerous applications, not only Earth-based, but also space-based, have strengthened the interest of the international scientific community in using Global Navigation Satellite Systems (GNSSs) as navigation systems for space missions that require good accuracy and low operating costs. Indeed, already successfully used in Low Earth Orbits (LEOs), GNSSbased navigation systems can maximise the autonomy of a spacecraft while reducing the burden and the costs of ground operations. That is why GNSS is also attractive for applications in higher Earth orbits up to the Moon, such as in Moon Transfer Orbits (MTOs). However, the higher the altitude the receiver is above the GNSS constellations, the poorer and the weaker are the relative geometry and the received signal powers, respectively, leading to a significant navigation accuracy reduction. In order to improve the achievable GNSS performance in MTOs, we consider in this paper an adaptive orbital filter that fuses the GNSS observations with an orbital forces model. Simulation results show a navigation accuracy significantly higher than that attainable individually by a standalone GNSS receiver or by means of a pure orbital propagation.

and Pierre-Andre Farine (pierre-andre.farine@epfl .ch) are with the School of Engineering of Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Looking Inside Modern ReceiversT he principle of wireless communications originates from the need to communicate between two points separated by a distance suffi ciently large to prevent the use of wires. Today, the use of wireless communications is an option for nearly any electronic device, resulting in short-range protocols such as Bluetooth technology.In this article, we will call the data to be transmitted the baseband signal s bb (t). On the transmitter side, in order to be radiated with a reasonably sized antenna and propagated efficiently through a given channel, s bb (t) needs to be up-converted to a higher frequency called the carrier frequency f c . To do so, s bb (t) is used to modify certain parameters of the carrier; this process is known as modulation of the carrier by the baseband signal. The carrier is usually a sinusoid and is defined here by c1t 2 5 A c cos 1w c t 2. A general expression of the resulting modulated signal is given by s 1t 2 5 a 1t 2cos 1v c 1t 2t 1 f1t 22,where s(t) is completely defined by its amplitude a(t) and phase f(t) 5 v(t) + f 0 (t). Making a(t) and/or f(t) functions of s bb (t) leads

This paper presents a new distance calculation circuit (DCC) that in artificial neural networks is used to calculate distances between vectors of signals. The proposed circuit is a digital, fully parallel and asynchronous solution. The complexity of the circuit strongly depends on the type of the distance measure. Considering two popular measures i.e. the Euclidean (L2) and the Manhattan (L1) one, it is shown that in the L2 case the number of transistors is even ten times larger than in the L1 case. Investigations carried out on the system level show that the L1 measure is a good estimate of the L2 one. For the L1 measure, for an example case of 4 inputs, for 10 bits of resolution of the signals, the number of transistors is equal to c. 2500. As transistors of minimum sizes can be used, the chip area of a single DCC, if realized in the CMOS 180 nm technology, is less than 0.015 mm2.

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