Journal of Advanced Materials in Engineering (Esteghlal)

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With progress in radar systems, a number of methods have been developed for signal processing and detection in radars. A number of modern radar signal processing methods use time-frequency transforms, especially the wavelet transform (WT) which is a well-known linear transform. The interference canceling is one of the most important applications of the wavelet transform. In Ad-hoc detection methods, the interference is firstly canceled and then a simple detector, like an energy detector, is used. Therefore, we have used wavelet-based approaches to cancel the interference and then an energy detector has been employed. In this paper, it is shown that in practical cases where the performance of matched filter or near-matched filter is degraded, wavelet-based methods are more efficient. Also, we have shown that for cases where targets with slow radial velocity or one close to blind velocity are removed by the MTI filter, wavelet-based denoising has a better performance.

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In this paper, two suboptimum detectors are proposed for coherent radar signal detection in K-distributed clutter. Assuming certain values for several initial moments of clutter amplitude, the characteristic function of the clutter amplitude is approximated by a limited series. Using the Pade approximation, it is then converted to a rational fraction. Thus, the pdf of the clutter amplitude is obtained as a sum of simple exponential functions. Using such a pdf, we develop the suboptimum detectors PGLR and PAALR, which are simplified forms of the GLR and AALR. Computer simulations show that the suggested detectors have appropriate performance compared to OLD, GLR and AALR detectors.

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In this paper, radar detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like the GLRT method). In the second detector, the averaged likelihood ratio is calculated by integrating out the unknown parameters (like the AALR method). Thanks to the numerical nature of these methods, they can be applied to many detection problems which do not have analytical solutions. Simulation results show that both the proposed detectors and the GLRT have approximately the same performance in problems to which the GLRT can be applied. On the other hand, the proposed detectors can be used in many cases for which either no ML estimate of unknown parameters exists or their prior distribution is known.