The United States Presidential Policy puts the United States on an an aggressive trajectory for reaching to moon by 2022. The COVID-19 crisis has inhibited the traditional working environment for many aerospace companies. Redmoon Systems aims to provide a consulting force capable of filling in the gaps to enable the US to achieve its stated goals.
Our team has a history of innovation within the US Space program. We have staffed programs for the Department of Defense as well as NASA at companies such as Lockheed Martin, Raython, Boeing, and Northrop Grumman. Some examples of technologies pioneered by our engineers are
Advanced radar tracking algorithms for the F-18 aircraft
Infrared on-orbit cameras for NASA missions
Missile and re-entry vehicle tracking systems and technologies,
Space debris detection tracking and removal technologies
Our team is standing by to enable you to meet your aerospace and defense research goals.
Summary: Group satellite together in such a way as to maximize coverage Data: For any possible grouping of satellites, a coverage percentage Goal: Assign each of N satellites to k groups, such that total mean coverage is maximized
Satellites change position and require constant reoptimization
Brute force solving is out of the question; even trivial subsets of the satellites form too many combinations to check Quantum technology offers a promise to perform combinatorial optimization much faster, while yielding better coverage outcomes
This type of situation is common in the internet communications field as well where satellite coverage may be required to provide persistent coverage of subscribers.
Redmoon Systems has an optimization technology which is able to achieve 15% more coverage than any existing method. Our software zeroes in on the best possible constellation configuration for any specific satellite and ground target problem.
The above presentation discusses a problem that is present in the satellite industry. There is a solution to this problem involving advanced computing technologies operated by NASA. We have partnered with NASA and DWave Systems to serve as a contact point for this particular solution. If you are interested, please email us
The above presentation was put together using NASA’s General Mission Analysis Tool software. We wanted to find out exactly how much fuel was being used for different NASA missions. We were hoping to baseline our navigation software so that we could see which areas needed improvement.
We learned that electric propulsion could be used in conjunction with our navigation software to enable a fine level of control and steering. This also boosts efficiency and enables long mission duration, resulting in more space exploration potential. This technology has actually been demonstrated in practice in the Dawn and Deep Space 1 missions. Our aim is to extend the applicability of a proven technology to *OTHER* types of missions and economic market sectors within space exploration.
A short presentation provided Jenny Fleury’s perspective on the Firecat Mars Mission and discusses the synergies made possible by combining measurements from optical and radar systems. Basically the optical provides information that the radar sensor network doesn’t provide, and vice versa.
Typically, the optical sensor provides a spatial resolution map and can be used to identify visible features. The radar can be used to obtain range and range rate (distance and velocity along the line of sight).
What if alternate sensor nodes were include in the the Firecat Mars mission? One way to do this would be to deploy a space based radar payload in solar orbit just outside the asteroid belt. This mission could enable range mapping of the asteroid belt, which is a valuable endeavor because it can be used to calibrate the optical system located on mars. Once the orbits are accurately determined using the radar data, the image processing could simplified for Firecat to yield better spectral ID (noise reduction based on range information).
In other worlds the signal levels coming from the asteroids to the mars optical sensor remains low compared to the Firecat moon-earth sensing mission, however a significant reduction in noise levels from the mars radar would boost the effective signal to noise ratio, thereby improving accuracy.
At Redmoon Systems, we are exploring the connection between quantum mechanics and classical mechanics in the context of space dynamics, exploration and mission architecture. Typically when missions are planned, the usual approach is to determine motion of bodies (the spacecraft itself as well) involving patched conic solutions for the the nearest gravitational sphere of influence. This article by Esther Barrabés Vera describes a gentle shift in perspective.
Another approach is to think of the potential of a body as its wave function, in analogy to quantum theory. A planet may pass through space that is sometimes occupied by a planet with or without the planet actually being present. Depending on the circumstances or timing, the spacecraft may experience gravitational forces related to the gravitational potential energy of the planet. If this occurs, the spacecraft may attempt to capture into the potential well of the planet. The potential energy required to capture the spacecraft is like a wavefunction, or a slightly delocalized version of the classical model of reality.
The reason why this could be important is that underneath the traditional model of gravity is a more complex and rich universe! This relatively newly accessed regime is the realm of space manifold dynamics.
Taking into consideration two planets at the same time enables a more complex dance for a spacecraft of satellite. The mathematics to describe this type of interaction is fairly sophisticated and also reasonably well developed. By analogy, the difference is between the motion of a surfer on the ocean, diving in between waves compared against the motion of billiard balls on a pool table. The analogy is not precise, however it is worth noting that the dance of eternity takes place in our solar system with natural objects such as comets and meteors.
Studying these objects has helped open doors for us to see the complex movements in action, so that we can reproduce their motion in our mathematical models, and ultimately in our exploration of the galaxy. This all has nothing to do with quantum mechanics or quantum behavior, most people would assert.
However my goal is not to show evidence of quantum effects such as tunneling, entanglement, or teleportation of these macroscopic objects. I would simply like to consider the possibility that a gravitational potential could be interpreted as a wavefunction of a planet or star. What use this perspective has beyond a form of intuition has not been determined as yet.
The main asteroid belt encircles the inner solar system. Roughly 3.2 AU from the sun, it is thought that the asteroid belt was not formed from by a planetary collision because the total combined mass of all asteroids is far too small (not even as much as Earth’s moon).
At Redmoon Systems, our intent is to detect, track and characterize the objects in the belt using infrared sensing technologies. One of these technologies is known as Firecat Observatory, Mars Mission.
Firecat is a passive sensor system located on the surface of Mars. Miniaturized and remotely deployed, the sensor consists of a single aperture through which an infrared multi-spectral detector array can detect light reflected from the asteroids nearest to Mars.
Inspired by a NASA review panel led by Jessy Cowan Sharp of NASA Ames, our team is running simulations to determine the best configuration for Firecat Observatory Mars Mission. Factors to be considered are the number of pixels, the pointing system, the power source, and transmitter configuration.
According to our initial design study, we have determined that the signal levels from three different rings or locations with the asteroid belt will be approximately:
When we learn physics in school, it is difficult to understand how to represent a falling ball as a math problem whose solution describes the real world. The first method we learned was Newtonian mechanics which is where everyone draws a force diagram, little arrows representing how a flower wants to evolve.
In college, we may learn something called Lagrangian mechanics which is a little different. For instance, instead of modeling forces, we talk about energy. Energy is often easier to find because it is always mass times velocity squared.
Lagrangian mechanics allows you to solve complex problems easily because you never need to know the forces at work.
But there is a trick to it. It requires identifying something called generalized coordinates, which can sometimes be challenging.
Actually, the invisible science behind Lagrangian mechanics revolves around a method or process known as the Calculus of Variations.
This involves two ideas: 1: that the differential equations of motion governing the dynamics of a classical or mechanical system can be deduced from a cost function and the fact that nature seeks to minimize the integral of this cost function. 2: that the way to identify the equations of motion require that we make small, even infinitesimal, changes in our path through the solution space.
In classical mechanics, the cost function has to do with energy minimization along a solution curve. Specifically, the cost function (Lagrangian) is defined on the tangent bundle (i.e. set of all tangent spaces to the solution curve, which contains velocity vectors). In Euclidean geometry, the “cost” function is actually the Euclidean distance, and is a metric defined on the solution curve or manifold itself (not the tangent bundle). The solution to problem in classical mechanics is a geodesic curve, i.e. one which minimizes the Lagrangian cost function.
In other domains, such as the infrared, it may be possible to construct a cost function based on the least action principle. However we must expand our notion of cost function.
In the example below, the epsilon parameter is a constant between 0 and 1 for each colored curve. Epsilon determines the scale of the effective blackbody curve, which is represented by the value epsilon = 1.
In the absence of any experimental data on the thermal radiator, all values of epsilon are equally likely. One may think of the radiator in as existing (from our perspective) in an undetermined state. By analogy to the Everett interpretation of coherent quantum states, unique versions of the radiator can be said to exist in separated realities. When an experimental observation of the radiator is made, a specific radiation curve is identified. The precise nature of the radiator is then known in “our” reality. In quantum mechanics, this process is called decoherence. A macroscopic object is unlikely to be in a true quantum state as such states require very low temperatures, however the uncertainty in radiant flux can be interpreted using the Everett representation.
The modeling task therefore is to perform a controlled decoherence of a large number of possible co-existant states into a single macroscopic state.
This can be performed using linear algebra, and the idea of a quadratic form as the cost function defined on a solution manifold. The matrix representing the form controls the mixing of individual time streams (realities). [per the Everett interpretation]
Note that the gray body curve is smooth, in contrast to the curve of the selective radiator. An example of a selective radiator would be a metal such as copper which when reduced to powder and burned in an open flame produces a specific set of peaks corresponding to its valence energy levels.