Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.     

A new method for finding electromagnetic emitter location

The position of a source of radiation is often obtained from bearing data, taken over an interval of time, and combining it with navigation data. A new method using total least squares (TLS) has been suggested for the accurate estimation of an emitter location when bearing observation errors are random. Further, an iterative two-stage approach involving TLS and Kalman filtering is developed for accurate estimation of the emitter location when bearing observation errors are an algebraic sum of random and systematic errors. The elegance and efficacy of the proposed methods are illustrated through digital computer simulated examples     

PANIC – A surface science package for the in situ characterization of a near-Earth asteroid

This paper presents the results of a mission concept study for an autonomous micro-scale surface lander also referred to as PANIC – the Pico Autonomous Near-Earth Asteroid In Situ Characterizer. The lander is based on the shape of a regular tetrahedron with an edge length of 35 cm, has a total mass of approximately 12 kg and utilizes hopping as a locomotion mechanism in microgravity. PANIC houses four scientific instruments in its proposed baseline configuration which enable the in situ characterization of an asteroid. It is carried by an interplanetary probe to its target and released to the surface after rendezvous. Detailed estimates of all critical subsystem parameters were derived to demonstrate the feasibility of this concept. The study illustrates that a small, simple landing element is a viable alternative to complex traditional lander concepts, adding a significant science return to any near-Earth asteroid (NEA) mission while meeting tight mass budget constraints.     

Transfer orbit stage gyrocompass alignment algorithm twist and swayenvironment for Mar's Observer mission on commercial Titan

The problems presented by the launch of a booster vehicle with a strapdown system on an expandable launch vehicle are addressed. These include the Earth motion by the wind-induced twist and sway motion at the launch pad, vibration-induced motion, and various sensor errors, as well as the limited memory and processing power of the flight computer. A robust numeric gyrocompass alignment algorithm is presented, and its performance in this environment is examined     

Broadband electrostatic noise and low-frequency waves in the Earth’s magnetosphere

Broadband electrostatic noise (BEN) is commonly observed in different regions of the Earth’s magnetosphere, eg., auroral region, plasma sheet boundary layer, etc. The frequency of these BENs lies in the range from lower hybrid to the local electron plasma frequency and sometimes even higher. Spacecraft observations suggest that the high and low-frequency parts of BEN appear to be two different wave modes. There is a well established theory for the high-frequency part which can be explained by electrostatic solitary waves, however, low-frequency part is yet to be fully understood. The linear theory of low-frequency waves is developed in a four-component magnetized plasma consisting of three types of electrons, namely cold background electron, warm electrons, warm electron beam and ions. The electrostatic dispersion relation is solved, both analytically and numerically. For the parameters relevant to the auroral region, our analysis predict excitation of electron acoustic waves in the frequency range of 17 Hz to 2.6 kHz with transverse wavelengths in range of (1–70) km. The results from this model may be applied to explain some features of the low-frequency part of the broadband electrostatic noise observed in other regions of the magnetosphere.     

Integrated drought monitoring index: A tool to monitor agricultural drought by using time-series datasets of space-based earth observation satellites

In this study, integrated drought monitoring index (IDMI) was proposed as a tool to assess and monitor the spatio-temporal dynamics of agricultural drought during the northeast monsoon season for the period from 2000 to 2016 in Tamil Nadu state, south-eastern part of Indian peninsula. The IDMI is characterized as the principal component of precipitation condition index (PCI), soil moisture condition index (SMCI), temperature condition index (TCI), and vegetation condition index (VCI) derived from time-series satellite observations of climate hazards group infra-red precipitation with stations (CHIRPS), European space agency climate change initiative (ESA-CCI) and moderate resolution imaging spectroradiometer (MODIS). The study shows that in the year 2016, about 44.4 and 17.8% of Tamil Nadu state was under extreme and severe drought conditions, respectively. Sensitivity analysis of the study shows that PCI is the most influential parameter to IDMI, followed by VCI and TCI. The validation of IDMI with 3-month standardized precipitation index (SPI) by using Pearson correlation test shows a strong positive correlation between IDMI and 3-month SPI with correlation coefficient (r) value of 0.73 and 0.77 for the wet (2005) and dry year (2016), respectively. The study clearly demonstrates the potential of IDMI derived from time-series datasets of earth observation satellites as a tool in assessment and monitoring of spatio-temporal dynamics of agricultural drought. The proposed IDMI could be effectively used as a reliable tool to monitor agricultural drought and develop its mitigation strategies to minimise the adverse effects of drought on agriculture, water resources, and livelihoods of the people.     

Finite data performance of MUSIC and minimum norm methods

In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given previously (1989, 1991). Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE     

Decorrelation of broadband signals by Toeplitz-Block-Toeplitzstructuring

We propose F-norm of the cross-correlation part of the array covariance matrix as a measure of correlation between the impinging signals and study the performance of different decorrelation methods in the broadband case using this measure. We first show that dimensionality of the composite signal subspace, defined as the number of significant eigenvectors of the source sample covariance matrix, collapses in the presence of multipath and the spatial smoothing recovers this dimensionality. Using an upper bound on the proposed measure, we then study the decorrelation of the broadband signals with spatial smoothing and the effect of spacing and directions of the sources on the rate of decorrelation with progressive smoothing. Next, we introduce a weighted smoothing method based on Toeplitz-block-Toeplitz (TBT) structuring of the data covariance matrix which decorrelates the signals much faster than the spatial smoothing. Computer simulations are included to demonstrate the performance of the two methods