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The problem of cooperative navigation for a team of platforms employing inter-platform observations is investigated. A decentralised solution in the framework of an information filter with delayed states is presented. In this structure, each platform first estimates its motion using only local sensor data, then shares its information across the network using an algorithm that employs a distributed Cholesky modification. The decentralised solution permits each platform to act in the same modular manner, providing robustness to individual platform failure. The solution yields linear minimum mean-square error estimation performance. As such the estimates generated are optimal; it generates exactly the same estimates as would a conventional extended Kalman filter (EKF), if given the same data. Efficient sparse implementation is accomplished without resorting to approximate methods. Simulation experiments employing a team of ten mobile platforms are described and used to evaluate the decentralised estimation performance. The robustness, flexibility, and cost of the decentralised approach are analyzed and compared with an existing distributed solution.