Signal Selectivity Time Delay Estimation In The Presence Of Impulsive Noise

This thesis focuses on the time delay estimation problem which has various and important applications in the fields of radar, sonar, communications and so on. Corresponding to different application conditions, such as noise/interference properties, relative motions between transmitters, receivers and reflectors, different models are developed and analyzed in detail. Three theoretically and practically valued algorithms are proposed. Computer simulations are given to verify the efficiency of these proposed algorithms.Approaches using second order, fractional lower order and cyclonstationary statistics to estimate time delay (with/without Doppler shift) are summarized. Their applicabilities, and indispensabilities between each other are carefully analyzed.The main work and innovation of this thesis can be concluded as follows:(1) The situation which is no relative motions between transmitters, receivers and reflectors is considered. In the presence of additive impulsive noise, the methods which based on second and higher order statistics are degraded. In this thesis, combination of p th order correlation and cyclic correlation is considered. Furthermore, an approach for estimating time delay with signal selectivity in the presence of impulsive noise is developed. Simulations show that the performance of proposed algorithm is not only better than second order cyclic correlation, but also the covariation.(2) In certain circumstance, relative motions can be discibed as a fixed time delay and Doppler shift. For jointly estimating time delays and Doppler shifts in the presence of impulse noise, a new algorithm is proposed, which utilizes the p th order cyclic ambiguity function. The proposed algotithm has better estimation accuracy than second order, fractional lower order and cyclic ambiguity function.(3) A new adaptive time delay estimation method is proposed for highly corruptive environments considered the cyclostationary property of the source signal, which is based on the combination of the fractional lower order statistics and cyclic correlation. Simulations show that the performance of the proposed algorithm is superior to the LMP (Least Mean p -norm) time delay estimation method and adaptive time delay estimation method based on second order cyclic correlation in alpha-stable distributed noises.