In this paper, the goal is theoreticaly investigating and presenting a ground moving target detector in single channel SAR based on detection theory methods. Accordingly, the detector structure for ground moving targets has been developed based on generalized likelihood ratio test (GLRT) from raw received signal. For this purpose, unknown parameters of the ground moving target’ s returned signal, including azimuth location and velocity in both azimuth and range directions are replaced with their ML estimations. Then, generalized likelihood ratio test is performed which leads to estimator-correlator detection structure. Estimation of the unknown parameters of the target reflectivity which is necessary for the proposed detector, needs optimization of an objective function through a grid search in multidimensional space of the unknown reflectivity parameters. To reduce the computational load of this massive multidimensional grid search, conversion of the multidimensional received signal space to equivalent several 1D spaces is used. Effectiveness of the proposed method is demonstrated through experimental results by evaluation of detection performance curves.

In this paper, the problem of signal detection based on suboptimum-constant false alarm rate (CFAR) detector, in the presence of a mixture of K-distributed clutter, is studied. In this case, a new suboptimum detector named generalized likelihood ratio test and maximum a posteriori (GLRT-MAP) is proposed and compared with generalized likelihood ratio test and linear quadratic (GLRT-LQ) suboptimum detector. The CFAR properties of the GLRT-MAP detector are investigated and compared with that of the GLRT-LQ detector. The simulation results show that the GLRT-MAP is a completely CFAR detector regardless of the clutter distribution and correlation in covariance matrix, whereas the GLRT-LQ is only CFAR detector regardless to clutter distribution. The performance analyses of the GLRT-MAP and GLRT-LQ are investigated by means of Monte Carlo simulation, and results are provided in results section.

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.

Detection of the Range Spread Target in High Resolution Radar(HRR) is Studied and a Novel Method Based on Strong Scattering Centers in Complex White Noise is Proposed. The Proposed Detector Uses Two-Threshold Strategy. The First Threshold Determines Strong Scattering Centers of the Target Then; These Centers are Used in a GLRT Test to Decide About Presence of Target. Then, It is Compared With Conventional Detectors in Different Target Scattering Models and Good Performance is Reported. In Addition, the Detector has Constant False Alarm Rate (CFAR) Property.

A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown parameters by Maximum Likelihood (ML) estimation for the use in the Generalized Likelihood Ratio Test (GLRT). By computer simulations, it has been shown that for large data records, this detector is Constant False Alarm Rate (CFAR) with respect to AR model driving noise variance. Also, measurements show the detector excellent performance in a practical setting. The detector’s performance in various simulated and actual conditions and the result of comparison with Kelly’s GLR and AR-GLR detectors are also presented.

In this paper, in order to improve target range resolution, target detection problem in FM-based passive bistatic radar (PBR) is modeled as an M-ary hypothesis testing problem. To do so, we exploit some adjacent channels being transmitted from a same FM transmitter and solve the detection problem based on generalized likelihood ratio test (GLRT) framework. Due to the masking effect of strong targets as well as the time-variant nature of FM radio signal, we implement the proposed detector in a multistage manner. Indeed, in our proposed detector, targets are detected sequentially and the previously detected targets are treated as interferences to be removed yielding the detection of the weakest ones. Extensive simulation results are provided to demonstrate the capability of the proposed detection algorithm. Our simulation results show that the proposed multi-channel detector not only improves the target range resolution but also results in detection performance improvement offered by combination gain equal to the number of active channels.

In many of detection problems the received signals models under two hypotheses, H0 and H1, are the same except that some model parameters have fixed value under H0. These models are so called Nested Models. One of the most important examples is detection of a target with unknown amplitude in the clutter. In this problem, one can assume similar models for received signals under H0 and H1 unless the target amplitude is assumed to be zero under H0. If the Bayesian approach used for treating unknown parameters, it can be shown that the likelihood ratio can be calculated as the ratio of the posterior and the prior probability of unknown parameters. Using this method a new detector for detection in Gaussian clutter is presented in this paper. Simulation results show that the proposed detector has much better performance compared with conventional GLRT detectors. It is also shown that a CFAR property is achieved provided that a small modifications in decision rule.