how to deal with signal to make it suitable for kalman filter
Kalman filter have basic requirements that the random processes like input signal, measurement signal,process noise and measurement noises all should be white Gaussian in nature.what Kalman filter actually do is that it uses the system model and input signal and generates its own output which is actually a predicted value now using the observation from the system it calculate the correction to be applied to the predicted output and hence final results are better than observations obtained from the system. Hence very first step- is that you need a system model. i am totally disagree with @Deniz notion that in non-linear case you should use particle filter. particle filter is used when the random process as mentioned above are not white Gaussian. Even if your system is non linear you can use Extended kalman filter or unscented kalman filter. As ur case is concerned i think you should apply extended or unscented kalman filter as velocities for navigation will keep on changing.