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In this chapter, we have presented an overview of nonlinear estimation approaches suitable for navigation problems. Starting with first principles, three classes of nonlinear, recursive estimators were derived, the performance was demonstrated using a common navigation example application, and comparisons were made between the approaches.

The growing availability of a wide range of sensors and improved computational resources has heralded a new era of multisensor navigation. Because many of these sensors have nonlinear and non‐Gaussian error models, researchers are developing a range of recursive navigation algorithms to meet these requirements.

When used within their associated limitations, nonlinear estimation algorithms hold enormous promise for addressing the most difficult navigation problems.