First dust measurements with the Solar Orbiter Radio and Plasma Wave instrument

Impacts of dust grains on spacecraft are known to produce typical impulsive signals in the voltage waveform recorded at the terminals of electric antennas. Such signals are routinely detected by the Time Domain Sampler (TDS) system of the Radio and Plasma Waves (RPW) instrument aboard Solar Orbiter. We investigate the capabilities of RPW in terms of interplanetary dust studies and present the first analysis of dust impacts recorded by this instrument. We discuss previously developed models of voltage pulses generation after a dust impact onto a spacecraft and present the relevant technical parameters for Solar Orbiter RPW as a dust detector. Then we present the statistical analysis of the dust impacts recorded by RPW/TDS from April 20th, 2020 to February 27th, 2021 between 0.5 AU and 1 AU. The study shows that the dust population studied presents a radial velocity component directed outward from the Sun, the order of magnitude of which can be roughly estimated as $v_{r, dust} simeq 50$ km.$s^{-1}$. This is consistent with the flux of impactors being dominated by $beta$-meteoroids. We estimate the cumulative flux of these grains at 1 AU to be roughly $F_beta simeq 8times 10^{-5} $ m$^{-2}$s$^{-1}$, for particles of radius $r gtrsim 100$ nm. The power law index $delta$ of the cumulative mass flux of the impactors is evaluated by two differents methods (direct observations of voltage pulses and indirect effect on the impact rate dependency on the impact speed). Both methods give a result $delta simeq 0.3-0.4$. Solar Orbiter RPW proves to be a suitable instrument for interplanetary dust studies. These first results are promising for the continuation of the mission, in particular for the in-situ study of the dust cloud outside the ecliptic plane, which Solar Orbiter will be the first spacecraft to explore.

The Right Invariant Nonlinear Complementary Filter for Low Cost Attitude and Heading Estimation of Platforms

This paper presents a novel filter with low computational demand to address the problem of orientation estimation of a robotic platform. This is conventionally addressed by extended Kalman filtering of measurements from a sensor suit which mainly includes accelerometers, gyroscopes, and a digital compass. Low cost robotic platforms demand simpler and computationally more efficient methods to address this filtering problem. Hence nonlinear observers with constant gains have emerged to assume this role. The nonlinear complementary filter is a popular choice in this domain which does not require covariance matrix propagation and associated computational overhead in its filtering algorithm. However, the gain tuning procedure of the complementary filter is not optimal, where it is often hand picked by trial and error. This process is counter intuitive to system noise based tuning capability offered by a stochastic filter like the Kalman filter. This paper proposes the right invariant formulation of the complementary filter, which preserves Kalman like system noise based gain tuning capability for the filter. The resulting filter exhibits efficient operation in elementary embedded hardware, intuitive system noise based gain tuning capability and accurate attitude estimation. The performance of the filter is validated using numerical simulations and by experimentally implementing the filter on an ARDrone 2.0 micro aerial vehicle platform.