The peculiarities of correlation-extreme visual navigation are considered. Descriptors with 64 elements of feature points of surface images are selected on the basis of the speed-up robust feature method. An analysis of possible criteria correlation functions is carried out to find the best match between the template descriptors and current images. The use of normalized correlation function is proposed based on the matrix multiplication properties of descriptors. It allows minimizing the number of false matches in comparison with the Euclidean distance in the descriptor space. The proposed matching strategy sufficiently decreases the computation time.

Methods of classification of correlation extremal navigation systems and geophysical fields are examined. The block diagram of correlation extremal navigation systems, which works in risk conditions of current navigational and mapping information, is introduced. The block of efficient mapping is set in the diagram; it realizes famous approach of simultaneous positioning and mapping on the base of recurrent Bayes estimation. It is shown, that block allows obtaining more precise mapping information during the flight process.

The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. Filtering block has been developed with the help of which navigation data received from UAV is filtered. UAV motion with camera on board has been conducted and photos have been captured from it. Photos have been processed by OpenSurf method, with the help of which feature points has been detected, filtered and compared with previous image. Result of research shows that with help of comparing of two neighboring images we can reconstruct relief above which UAV flew.

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The problem of step counting has been considered for personal navigation while walking and using mobile sensors with low accuracy.Step counting has been realized by three main methods of processing the acceleration vector magnitude in time domain. The comparison of this methods has been done while processing the data from mobile phone sensors for different conditions and types of pedestrian/their motion patterns. In order to have representative statistics the walking trajectories have been selected long enough (at least 100 meters) except the method of normalized auto-correlation based step counting where short distances have been processed. The requirements to definite method of step counting have been formulated.