Juno is a New Frontiers mission to study Jupiter and carries as one of its payloads a six-frequency microwave radiometer to perform atmospheric sounding of the Jovian atmosphere to pressures of approximately 250 bars [1]. Juno was launched from Kennedy Space Center on August 5, 2011 and reached Jupiter orbit on July 4, 2016. The Microwave Radiometer (MWR) operates from 600 MHz to 22 GHz and was designed and built at the Jet Propulsion Laboratory. Because the mission is operating in the harsh Jovian radiation environment, it required an ambitious radiometer design that pushed the limits for an internally calibrated radiometer. The MWR uses noise diodes and a PIN-diode Dicke switch located inside the receiver. Typically, internally calibrated radiometers are designed to minimize the loss and the temperature gradient between the antenna and the noise calibration sources. However, for the MWR, the receivers with the calibration sources needed to reside inside a centralized radiation vault and the large antennas were up to 1.5 m away on the outward facing side of the spacecraft. Producing calibrated antenna temperatures requires a correction for up to 3 dB of front-end loss with a 150 K temperature gradient. In other words, 50% of the received signal at the internal calibration plane originates in the front-end of the radiometer. Additionally, the MWR is operating on a spacecraft rotating through the strong Jovian magnetic field, requiring targeted magnetic shielding of the ferrite isolator inside the receiver.

Many surveillance and reconnaissance tasks make use of multi-cameras in order to ensure that a particular mission is accomplished. These networks of cameras are useful as they can reduce the cost of human observers, are continuously observant (unlike humans who may fall asleep on the job), and can be implemented for a fairly low cost. However, in many scenarios, it does not make sense or may not be possible to have a fixed camera installation because the surveillance may only be needed for a short duration or it may take too long to do a proper install and observation is needed now. In terms of short terms and immediacy, mobile robots acting as a camera network provide an interesting middle ground. They can be deployed quickly to cover immediate needs, and they can be packed up and moved to another area if needs change. However, as the duration of the mission in which they are used increases, the robotic team will run out of power. This paper addresses some of the issues with keeping a surveillance team active while their batteries drain. Multiple task-reallocation methods are used in conjunction with an analysis of the effects of fixed vs mobile docking stations. Simulations were run requiring the team to provide camera coverage of a group of mobile “pedestrians” moving dynamically through a scene and the results are presented.

This paper focuses on the Titan surface parameters retrieval with emphasis on a combination of passive and active microwave measurements from Cassini spacecraft on the areas characterized by large liquid surfaces and neighboring land areas. The methodology consists of a combination of direct modeling and inversion algorithms. First, these surfaces have been described by means of a double layer model which considers an upper liquid hydrocarbons layer and a lower layer compatible with the radar response of the neighboring areas. This model is introduced into a Bayesian framework for the purpose of inferring the likely ranges of some parameters, in particular the optical thickness of the hypothesized liquid hydrocarbons layer and the wind speed. Second, the optical thickness information is used as an input to a forward radiative transfer model calculation to obtain simulated brightness temperatures. Comparison of the observed and computed brightness temperatures allows addressing the consistency of the observations from the two instruments.

Robotic teams are often proposed for solving a number of problems, ranging from exploring unknown environments to monitoring areas for security or environmental contamination. These teams are composed of individual robots which may lack the capabilities to complete a task on their own. One critical capability required by teams regardless of the mission is the ability to have sufficient battery life to remain active for the duration of the mission. We present an approach for maintaining battery life by developing a hierarchical team composed of deployable robots and docking stations. Unlike other approaches, the approach presented here focuses on docking stations supporting multiple deployed robots simultaneously. In order to do so the docking stations must continually optimize their locations with respect to the robots in need of service. Discussion of the optimization is presented, along with simulation in multiple environments to illustrate the scalability of the approach to large robotic teams. The on-going transition of this algorithm to actual hardware is also discussed.

The performance of systems for human activity recognition depends heavily on the placement of cameras observing the scene. This work addresses the question of the optimal placement of cameras to maximize the performance of these types of recognition tasks. Specifically, our goal is to optimize the quality of the joint observability of the tasks being performed by the subjects in an area. We develop a general analytical formulation of the observation problem, in terms of the statistics of the motion in the scene and the total resolution of the observed actions that is applicable to many observation tasks and multi-camera systems. A nonlinear optimization approach is used to find the internal and external (mounting position and orientation) camera parameters that optimize the recognition criteria. In these experiments, a single camera is repositioned using a mobile robot. Initial results for the problem of human activity recognition are presented.