The BSpace Algorithm - A Statistical Model of Satellite Constellation Performance
This paper describes a statistical algorithm that predicts the best possible collection performance that may be achieved by a large constellation of imaging satellites collecting against a very large target deck. Our model includes an ideal tasking algorithm for cooperative collection. Tasking algorithms are one of the most difficult parts of a collection simulation to model, particularly for cooperative collection in a constellation. We were able to produce midto high-fidelity results for a simulation of half a dozen satellites collecting against approximately 100,000 targets, with a run-time of only 15 minutes. The algorithm is easily implemented in the C programming language on an ordinary workstation.
We study a new technique for optimal data compression subject to conditions of causality and different types of memory. The technique is based on the assumption that some information about compressed data can be obtained from a solution of the associated problem without constraints of causality and memory. This allows us to consider two separate problem related to compression and de- compression subject to those constraints. Their solutions are given and the analysis of the associated errors is provided. Keywords—stochastic signals, optimization problems in signal processing.