Abstract: Systems and methods are described for estimating a state of a process and an input to the process using a sensor network. Each sensor node in the sensor network is directly to one or more adjacent sensor nodes and indirectly coupled to the remaining sensor nodes through the one or more adjacent sensor nodes. Each sensor node iteratively calculates a new estimated state based on estimations of the state and the input to the process calculated by the sensor node in a previous iteration. The new estimated state is then adjusted based on a difference between a predicted and actual output of a sensor and is further adjusted based on differences between a previous estimated state calculated by the sensor node and estimated states calculated by adjacent sensor nodes.

Abstract: Systems and methods are described for estimating a state of a process and an input to the process using a sensor network. Each sensor node in the sensor network is directly to one or more adjacent sensor nodes and indirectly coupled to the remaining sensor nodes through the one or more adjacent sensor nodes. Each sensor node iteratively calculates a new estimated state based on estimations of the state and the input to the process calculated by the sensor node in a previous iteration. The new estimated state is then adjusted based on a difference between a predicted and actual output of a sensor and is further adjusted based on differences between a previous estimated state calculated by the sensor node and estimated states calculated by adjacent sensor nodes.

Abstract: Systems and methods are described for estimating a state of a process and an input to the process using a sensor network. Each sensor node in the sensor network is directly to one or more adjacent sensor nodes and indirectly coupled to the remaining sensor nodes through the one or more adjacent sensor nodes. Each sensor node iteratively calculates a new estimated state based on estimations of the state and the input to the process calculated by the sensor node in a previous iteration. The new estimated state is then adjusted based on a difference between a predicted and actual output of a sensor and is further adjusted based on differences between a previous estimated state calculated by the sensor node and estimated states calculated by adjacent sensor nodes.

Abstract: A method and system for distributed spatial control of a formation of vehicles includes receiving at a first formation vehicle via a peer-to-peer communication interface, direction of travel and formation density information that indicate a course of travel for the first vehicle while travelling as a member of the formation of vehicles. The peer-to-peer formation density information indicates a distance to maintain from other neighboring formation vehicles. A formation vehicle self-navigation command is generated for navigating the first vehicle when travelling in one dimensional, two dimensional, or three dimensional space as a member of the formation of vehicles. The self-navigation command is based on the peer-to-peer direction of travel and formation density information. The direction of travel information is based on locally determined spatial relationships of a portion of the formation of vehicles.

Abstract: A method and system for distributed spatial control of a formation of vehicles includes receiving at a first formation vehicle via a peer-to-peer communication interface, direction of travel and formation density information that indicate a course of travel for the first vehicle while travelling as a member of the formation of vehicles. The peer-to-peer formation density information indicates a distance to maintain from other neighboring formation vehicles. A formation vehicle self-navigation command is generated for navigating the first vehicle when travelling in one dimensional, two dimensional, or three dimensional space as a member of the formation of vehicles. The self-navigation command is based on the peer-to-peer direction of travel and formation density information. The direction of travel information is based on locally determined spatial relationships of a portion of the formation of vehicles.