A method for blind estimation of static time errors in time interleaved A/D converters is investigated. The method assumes that amplitude and gain errors are removed before the time error estimation. Even if the amplitude and gain errors are estimated and removed, there will be small errors left. In this paper, we investigate how the amplitude and gain errors influence the time error estimation performance.

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As a part of aircraft navigation three-dimensional position (horizontal position and altitude) must be computed continuously. For accuracy and reliability reasons several sensors are usually integrated together, and here we are dealing with dead-reckoning integrated with terrain-aided positioning. Terrain-aided positioning suffers from severe nonlinear structure, meaning that we have to solve a nonlinear recursive Bayesian estimation problem. This is not possible to do exactly, but recursive Monte Carlo methods, also known as particle filters, provide a promising approximate solution.To reduce the computational load of the normally rather computer intensive particle filter we present algorithms which take advantage of linear structure. These algorithms are all based on a Rao-Blackwellisation technique, i.e. we marginalise the full conditional posterior density with respect to the linear part, which here is altitude. The algorithms differ in the way the linear part is estimated, but the principle is to use multiple Kalman filters. The particle filter is then used for estimating horizontal position only. Simulations show that the computational load is reduced significantly.