In this paper, we present a receding horizon solution to the problem of optimal scheduling for multiple sensors monitoring a group of dynamical targets. The term target is used here in the classic sense of being the object that is being sensed or observed by the sensors. This problem is motivated by the space situational awareness (SSA) problem. The multisensor optimal scheduling problem can be posed as a multiagent Markov decision process on the information space which has a dynamic programming (DP) solution. We present a simulation-based stochastic optimization technique that exploits the structure inherent in the problem to obtain variance reduction along with a distributed solution. This stochastic optimization technique is combined with a receding horizon approach which uses online solution of the control problems to obviate the need to solve the computationally intractable multiagent information space DP problem and hence, makes the technique computationally tractable. The technique is tested on a moderate scale SSA example which is nonetheless computationally intractable for existing solution techniques.

The satellite scheduling community has provided several algorithms for allocating interaction windows between ground stations and satellites, from simple greedy approaches to more complex hybrid-genetic techniques. Single-location ground station problems, where requests have fixed time intervals and no priorities, are known to be solvable in polynomial time. To the best of our knowledge, no algorithm has been provided yet for solving multiple-location, prioritized scheduling problems optimally. We present an optimal polynomial time algorithm for a fixed number of ground stations (or satellites), based on a modified algorithm from the general scheduling literature. We conclude the paper by simulating the cases that demonstrate that the new optimal algorithm outperforms the reference greedy and genetic algorithms.

In this paper, we present a receding horizon solution to the optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a partially observed Markov decision problem whose solution is given by an information space (I-space) dynamic programming (DP) problem. We present a simulation-based stochastic optimization technique that, combined with a receding horizon approach, obviates the need to solve the computationally intractable I-space DP problem. The technique is tested on a sensor scheduling problem, in which a sensor must choose among the measurements of N dynamical systems in a manner that maximizes information regarding the aggregate system over an infinite horizon. While simple, such problems nonetheless lead to very high dimensional DP problems to which the receding horizon approach is well suited.

This paper makes preliminary steps at exploring a novel application of cognitive radios (CRs) for reliable satellite communications. We propose the use of dynamically adjusted frequency hopping (FH) sequences for satellite transmissions. Such sequences are more robust against smart eavesdropping and targeted interference than fixed FH sequences. In our approach, FH sequence is adjusted according to the outcome of out-of-band sensing, carried out by a CR module that resides in the satellite itself or at the receiving ground station. Our protocol, called OSDFH, relies on exploiting the spectrum sensing capabilities of CRs for proactive detection of channel quality. We analyze the characteristics of the proposed OSDFH using a finite state Markov chain (FSMC) framework. Level crossing rate (LCR) analysis is used to determine the transition probabilities of the Markov chain. These probabilities are then used to measure the “channel stability,” a metric that reflects the freshness of sensed channel interference. We use simulations to study the effects of different system parameters on the performance of our proposed protocol.

Wireless communications are prone to both unintentional and intentional RF interference. Such interference has significant impact on the reliability of packet transmissions. In this paper, we consider interference mitigation in frequency hopping (FH) systems. We employ cognitive radios (CRs) for proactive interference sensing in such systems. Through this proactive approach, we propose a scheme for dynamic adjustment of the FH sequence. Our protocol, called SSDFH, relies on exploiting the spectrum sensing capabilities of CRs for proactive detection of channel quality. We analyze the characteristics of the proposed SSDFH using a continuous-time Markov chain framework. Level crossing rate (LCR) analysis is used to determine the transition rates for the Markov chain, which are then used to measure the “channel stability,” a metric that reflects the freshness of sensed channel interference. The selection of different protocol parameters is studied by means of analysis. In particular, we provide a numerical procedure for determining the “optimal” total sensing time that minimizes the probability of “black holes.” We run simulations to study the performance of our proposed protocol.

In this paper, we present an adaptive digital communication system using field programmable gate array (FPGA) technology. This system adapts the Universal Software Radio Peripheral (USRP) to better suit the space and power limitations of the CubeSat satellite form factor and the Space Plug-and-Play Avionics (SPA) protocol. The result is a highly-adaptive, plug and play software-defined radio (SDR) that is easily incorporated into any CubeSat design.

Reconfigurable antennas, with the ability to radiate more than one pattern at different frequencies and polarizations, are necessary in modern telecommunication systems. The requirements for increased functionality (e.g., direction finding, beam steering, radar, control, and command) within a confined volume place a greater burden on today's transmitting and receiving systems. Reconfigurable antennas are a solution to this problem. This paper discusses the different reconfigurable components that can be used in an antenna to modify its structure and function. These reconfiguration techniques are either based on the integration of radio-frequency microelectromechanical systems (RF-MEMS), PIN diodes, varactors, photoconductive elements, or on the physical alteration of the antenna radiating structure, or on the use of smart materials such as ferrites and liquid crystals. Various activation mechanisms that can be used in each different reconfigurable implementation to achieve optimum performance are presented and discussed. Several examples of reconfigurable antennas for both terrestrial and space applications are highlighted, such as cognitive radio, multiple-input–multiple-output (MIMO) systems, and satellite communication.

In this paper, we present a receding horizon solution to the problem of optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a Partially Observed Markov Decision Process (POMDP) whose solution is given by an Information Space (I-space) Dynamic Programming (DP) problem. We present a simulation based stochastic optimization technique that, combined with a receding horizon approach, obviates the need to solve the computationally intractable I-space DP problem. The technique is tested on a simple sensor scheduling problem where a sensor has to choose among the measurements of N dynamical systems such that the information regarding the aggregate system is maximized over an infinite horizon.