Autonomous Line-of-Sight navigation represents an appealing technique that can be exploited by deep-space spacecraft, particularly miniaturized, to estimate the state during cruising. It is based on the observation of visible bodies' directions, processed onboard to estimate the spacecraft' 6D heliocentric state. Its applicability has been preliminarily investigated by feeding the navigation filter with simplified measurements, simulated by adding a certain noise on the actual direction, based on star-tracker characteristics. However, while this approach is convenient and appropriate for a preliminary study, it is not sufficient to dive into the characteristics and performance of the method, and later on to definitely prove its applicability to real missions. This is because the measurement error cannot be considered relying exclusively on the star tracker's characteristics, as the observation scenario (e.g. planet apparent size, illumination condition, stars background) does play an important role in the measurement error. For this reason, in this work, we include image processing in the simulation loop. First, we define how to simulate realistic and reliable synthetic space images as a function of hardware characteristics and observation scenarios; then we use the generated images within the simulation to compute the measurements. Thanks to this approach, it is also possible to further improve the navigation filter design. In fact, we developed an Adaptive Extended Kalman Filter to cope with variable measurement errors and dynamics conditions. This filter allows the automatic tuning of both the process noise covariance and the measurement noise matrices as a function of the scenario. With this work, we add two important pieces to the road map for fully autonomous deep-space spacecraft: performance evaluation refinement including image-processing, and design of AEKF for the technique.

Line-of-Sight (LoS) navigation is an optical navigation technique that exploits the direction to visible celestial bodies, obtained from an onboard imaging system, to estimate the position and velocity of a spacecraft. The directions are fed to an estimation filter, where they are matched with the actual position of the observed bodies, retrieved from onboard stored ephemerides. As LoS navigation represents a really promising option for the next-generation deep-space spacecraft, the objective of this work is to provide new insights into the performance. First, the information matrix is analyzed to show the influence of the geometry between the spacecraft and the observed planet(s). Then, a Monte Carlo approach is used to investigate the influence of measurement error (ranging from 0.1 to 100 arcsec), and tracking frequency (ranging from four observations per day to one observation every two days). The effect on navigation performance is quantified by two indicators. The first is the 3D position and velocity Root-Mean-Square-Errors, computed once the estimation is considered to be steady-state. The second is the convergence time, which quantifies the required time for the estimation to reach the steady-state behaviour. The simulation is based on a set of four planets, which do not follow the common heliocentric dynamics but rotate around the Sun with the same (distance-independent) angular velocity of the spacecraft. This approach allows the separation of scenario-dependent behaviours from navigation intrinsic properties, as the same relative geometry between observer and observed objects is maintained during the whole simulation. The results provide a useful guide for the next-generation autonomous navigation system, for both the definition of hardware requirements and the design of an appropriate navigation strategy. Considerations are then applied to Near-Earth Asteroid fly-by mission scenarios for the definition of the navigation strategy and hardware requirements. It is shown the importance of relative angles between the spacecraft and the planets. In the single-planet observation scenario, when the angle between the position vectors of the spacecraft and planet approaches a null value, the estimation error decreases. In the double-planets observation scenario, when the separation angle between the two LoS directions gets close to 90°, the estimation error decreases. The main influence on the performance is driven by the measurement error, which with current technologies is shown to be able to provide a position error in the order of a few hundred kilometers, while with a lower measurement error (0.1 arcsec) it would be possible to have a position error below 100 km. Finally, it is demonstrated that tracking frequency plays a secondary role in the performance, and only influences tangibly the convergence time.

This paper aims to investigate the capabilities of exploiting optical line-of-sight navigation using star trackers. First, a synthetic image simulator is developed to generate realistic images, which is later exploited to test the star tracker's performance. Then, generic considerations regarding attitude estimation are drawn, highlighting how the camera's characteristics influence the accuracy of the estimation. The full attitude estimation chain is designed and analyzed in order to maximize the performance in a deep-space cruising scenario. After that, the focus is shifted to the actual planet-centroiding algorithm, with particular emphasis on the illumination compensation routine, which is shown to be fundamental to achieving the required navigation accuracy. The influence of the center of the planet within the singular pixel is investigated, showing howthis uncontrollable parameter can lower performance. Finally, the complete algorithm chain is tested with the synthetic image simulator in a wide range of scenarios. The final promising results show that with the selected hardware, even in the higher noise condition, it is possible to achieve a direction's azimuth and elevation angle error in the order of 1-2 arc sec for Venus, and below 1 arc sec for Jupiter, for a spacecraft placed at 1 AU from the Sun. These values finally allow for a positioning error below 1000 km, which is in line with the current non-autonomous navigation state-of-the-art.

The key challenges in designing a multi-channel biosignal acquisition system for an ambulatory or invasive medical application with a high channel count are reducing the power consumption, area consumption and the outgoing wire count. This article proposes a spread-spectrum modulated biosignal acquisition system using a shared amplifier and an analog-to-digital converter (ADC). We propose a design method to optimize a recording system for a given application based on the required SNR performance, number of inputs, and area. The proposed method is tested and validated on real pre-recorded atrial electrograms and achieves an average percentage root-mean-square difference (PRD) performance of 2.65% and 3.02% for sinus rhythm (SR) and atrial fibrillation (AF), respectively by using pseudo-random binary-sequence (PRBS) codes with a code-length of 511, for 16 inputs. We implement a 4-input spread-spectrum analog front-end in a 0.18 μ m CMOS process to demonstrate the proposed approach. The analog front-end consists of a shared amplifier, a 2nd order Σ Δ ADC sampled at 7.8 MHz, used for digitization, and an on-chip 7-bit PRBS generator. It achieves a number-of-inputs to outgoing-wire ratio of 4:1 while consuming 23 μ A/input including biasing from a 1.8 V power supply and 0.067 mm ² in area.

CubeSats represent a promising solution for low-cost deep-space exploration, especially for Near-Earth Asteroids (NEAs) missions. In this framework, autonomous navigation is a viable option to improve CubeSat capabilities for deep-space exploration. The state of a deep-space cruising spacecraft can be estimated with a Line-of-Sight (LoS) navigation approach, which is based on the observation of visible celestial bodies direction with a camera. It is attractive for small-spacecraft missions, as it does not require additional instrumentation, since cameras or star trackers are usually carried on-board. Feasibility of exploiting LoS navigation for NEAs exploration is investigated, by analysing the relative geometry between observable planets and NEAs at their ascending and descending nodal passages. In literature, a Figure-of-Merit (FoM) has been formulated to quantify analytically the navigation performance for simultaneous tracking of two bodies, and it is used here to investigate LoS navigation applicability to NEAs exploration scenarios. The FoM depends on the relative observation geometry. The lower its value, the higher the expected navigation accuracy. The NEAs dataset has been retrieved from NEODyS-2, a web-based database containing 26822 orbital parameters. NEAs ephemerides have been propagated for the period 2023-2033. The spacecraft has been assumed coincident with the target NEA at the nodes, as the distance asteroid-spacecraft during the encounter is in the order of few hundreds of kilometers. The observation of the six smaller semi-major axis planets (from Mercury to Saturn) is considered. The FoM has been calculated for each pair of planets at each nodal passage, assuming all the bodies whose line-of-sight direction has an angular separation of at least 30° from the Sun direction are visible (common Sun exclusion angle for star trackers). The result consists in a list of NEAs, whose geometry is appealing for LoS navigation, and that can be used for further analysis to design trajectories to reach these bodies with CubeSats. The FoM ranges from ∼ 10¹⁵ to ∼ 10²⁴. Analysing a total of 265067 node passages, in less than 1% the FoM could not be computed because either none or only one planet was visible, while in almost 13% of the cases all planets were observable. A direct link between FoM and expected state estimation error cannot be established, so two test cases are analysed, using CubeSat hardware characteristics. The first, with FoM ∼ 10¹⁵, is characterised by a final position estimation error around 100 km, while for the second, with FoM ∼ 10²², it is around 2000 km.