Derek S. Linden

An Evolved Antenna for Deployment on Nasa’s Space Technology 5 Mission

We present an evolved X-band antenna design and flight prototype currently on schedule to be deployed on NASA’s Space Technology 5 (ST5) spacecraft. Current methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity, and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that would ordinarily not be found. The ST5 antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly-polarized wave and wide bandwidth. Two evolutionary algorithms were used: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. As of this writing, one of our evolved antenna prototypes is undergoing flight qualification testing. If successful, the resulting antenna would represent the first evolved hardware in space, and the first deployed evolved antenna.

Computer-automated evolution of an x-band antenna for nasa's space technology 5 mission

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASAs Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

Automated Antenna Design with Evolutionary Algorithms

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a signican t amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically nd novel antenna designs that are more eectiv e than would otherwise be developed. Here we present automated antenna design and optimization methods based on evolutionary algorithms. We have evolved ecien t antennas for a variety of aerospace applications and here we describe one proof-of-concept study and one project that produced gh t antennas that ew on NASA’s Space Technology 5 (ST5) mission.

Causally-guided evolutionary optimization and its application to antenna array design

In recent years, evolutionary computation has been successfully used to solve problems involving engineering design and invention, sometimes producing results that are qualitatively different than previous traditionally-designed solutions. However, while evolutionary methods appear to be a promising tool for supporting design, their usefulness is substantially limited by their computational expense and inability to integrate expert knowledge with evolutionary search. Here we develop and evaluate methods for causally-guided evolutionary design based on expert-supplied cause-effect relations that guide how genetic operators are applied in contrast to conventional genetic operations which are carried out blindly and randomly, using these methods for antenna array design. To our knowledge, this is the first study that biases genetic operations in response to the specific performance characteristics of the individuals to which they are applied, and the first to use explicit cause-effect relations to guide this process. Our experimental evaluation compares using evolutionary systems with and without causal guidance to design directional dipole antenna arrays that meet pre-specified performance criteria. We find that causally-guided systems produce optimal solutions with significantly greater frequency and significant computational savings, suggesting that this approach may substantially improve the use of evolutionary computation in engineering design.

Evolutionary optimization of a quadrifilar helical antenna

Automated antenna synthesis via evolutionary design has recently garnered much attention in the research literature. Evolutionary algorithms show promise because, among search algorithms, they are able to effectively search large, unknown design spaces. NASAs Mars Odyssey spacecraft is due to reach final Martian orbit insertion in January, 2002. Onboard the spacecraft is a quadrifilar helical antenna that provides telecommunications in the UHF band with landed assets, such as robotic rovers. Each helix is driven by the same signal which is phase-delayed in 90 deg increments. A small ground plane is provided at the base. It is designed to operate in the frequency band of 400-438 MHz. Based on encouraging previous results in automated antenna design using evolutionary search, we wanted to see whether such techniques could improve upon Mars Odyssey antenna design. Specifically, a co-evolutionary genetic algorithm is applied to optimize the gain and size of the quadrifilar helical antenna. The optimization was performed in-situ in the presence of a neighboring spacecraft structure. On the spacecraft, a large aluminum fuel tank is adjacent to the antenna. Since this fuel tank can dramatically affect the antennas performance, we leave it to the evolutionary process to see if it can exploit the fuel tanks properties advantageously. Optimizing in the presence of surrounding structures would be quite difficult for human antenna designers, and thus the actual antenna was designed for free space (with a small ground plane). In fact, when flying on the spacecraft, surrounding structures that are moveable (e.g., solar panels) may be moved during the mission in order to improve the antennas performance.

Evolutionary Optimization of Yagi-Uda Antennas

Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

Evolutionary design of an X-band antenna for NASA's Space Technology 5 mission

We present an evolved X-band antenna design and flight prototype currently on schedule to be deployed on NASAs Space Technology 5 spacecraft in late 2004. The antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. The highest performance antenna found using a genetic algorithm was fabricated and tested.

Human-competitive evolved antennas

Abstract We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASAs Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.

Rapid Re-Evolution of an X-Band Antenna for Nasa’s Space Technology 5 Mission

One of the challenges in engineering design is adapting a set of created designs to a change in requirements. Previously we presented two four-arm, symmetric, evolved antennas for NASA’s Space Technology 5 mission. However, the mission’s orbital vehicle was changed, putting it into a much lower earth orbit, changing the specifications for the mission. With minimal changes to our evolutionary system, mostly in the fitness function, we were able to evolve antennas for the new mission requirements and, within one month of this change, two new antennas were designed and prototyped. Both antennas were tested and both had acceptable performance compared with the new specifications. This rapid response shows that evolutionary design processes are able to accommodate new requirements quickly and with minimal human effort.

An evolved anti-jamming adaptive beamforming network

Interference in wireless networks is undesirable, whether it is due to unintentional or malicious causes. Adaptive beamforming is a spatial filtering technique that can prevent jammers from disrupting wireless networks. This paper presents an evolvable hardware (EH) application in which an evolutionary algorithm (EA) is used to configure an adaptive beamformer to achieve two goals: (1) steering nulls towards jamming signals and (2) directing gain in the direction of the desired signal. This is the first demonstration of an EA-configured adaptive beamformer to counter a jamming system. Simulation results show that the EA is able to thwart up to three jamming signals. The results suggest that EH is a promising approach towards wireless network security.