Although utilization of polarimetry techniques for recognition of military and civilian targets is well established in the narrowband context, it is not yet fully established in a broadband sense as compared to planetary area of research. The concept of combining polarimetry together with certain areas of broadband technology and thus forming a robust signature and feature set has been the main theme of this thesis. This is important, as basing the feature set on multiple types of signatures can increase the accuracy of the recognition process. In this thesis, the concept of radar target recognition based upon a polarization signature in a broadband context is examined. A proper UWB radar signal can excite the target dominant resonances and, consequently, reveal information about the target principle dimensions; while diversity in the polarization domain revealed information about the target shape. The target dimensions are used to classify the target, and then information about its shape is used to identify it. Fused together and inferred from the target characteristic polarization states, it was verified that the polarization information at dominant resonant frequencies have both a physical interpretation and attributes (as seen in section ‎3.4.3) related to the target symmetry, linearity, and orientation. In addition, this type of information has the ability to detect the presence of major scattering mechanisms such as strong specular reflection as in the case of the cylinder flat ends. Throughout the thesis, simulated canonical targets with similar resonant frequencies were used, and thus identification of radar targets was based solely on polarization information. In this framework, the resonant frequencies were merely identified as peaks in the frequency response for simple or low damping targets such as thin metal wires, or alternatively identified as the imaginary parts of the complex poles for complex or high damping targets with significant diameter and dielectric properties. Therefore, the main contribution of this thesis originates from the ability to integrate the optimum polarization states in a broadband context for improved target recognition performance. In this context, the spectral dispersion originating from the broad nature of the radar signal, the lack of accuracy in extracting the target resonances, the robustness of the polarization feature set, the representation of these states in time domain, and the feature set modelling with spatial variation are among the important issues addressed with several approaches presented to overcome them. The general approach considered involved a subset of “representative” times in the time domain, or correspondingly, “representative frequencies” in the frequency domain with which to associate optimum polarization states with each member of the subset are used. The first approach in chapter ‎3 involved the polarization representation by a set of frequency bands associated with the target resonant frequencies. This type of polarization description involved the formulation of a wideband scattering matrix to accommodate the broad nature of the signal presentation with appropriate bandwidth selection for each resonance; good estimation of the optimum polarization states in this procedure was achievable even for low signal-to-noise ratios. The second approach in chapter ‎4 extended the work of chapter ‎3 and involved the modification of the optimum polarization states by their associated powers. In addition, this approach included an identification algorithm based on the nearest neighbour technique. To identify the target, the identification algorithm involved the states at a set of resonant frequencies to give a majority vote. Then, a comparison of the performance of the modified polarization states and the original states demonstrated good improvement when the modified set is used. Generally, the accuracy of the resonance set estimate is more reliable in the time domain than the frequency domain, especially for resonances well localized in time. Therefore, the third approach in chapter ‎5 deals with the optimum states in the time domain where the extension to a wide band context was possible by the virtue of the polarization information embodied in the energy of the resonances. This procedure used a model-based signature to model the target impulse response as a set of resonances. The relevant resonance parameters, in this case, the resonant frequency and its associated energy, were extracted using the Matrix Pencil of Function algorithm. Again, this approach of sparse representation is necessary to find descriptors from the target impulse response that are time-invariant, and at the same time, can relate robustly to the target physical characteristics. A simple target such as a long wire showed that indeed polarization information contained in the target resonance energies could reflect the target physical attributes. In addition, for noise-corrupted signals and without any pulse averaging, the accuracy in estimating the optimum states was sufficiently good for signal to noise ratios above 20dB. Below this level, extraction of some members of the resonance set are not possible. In addition, using more complex wire models of aircraft, these time-based optimum states could distinguish between similar dimensional targets with small structural differences, e.g. different wing dihedral angles. The results also showed that the dominant resonance set has members belonging to different structural sections of the target. Therefore, incorporation of a time-based polarization set can give the full target physical characteristics. In the final procedure, a statistical Kernel function estimated the feature set derived previously in chapter ‎3, with aspect angle. After sampling the feature set over a wide set of angular aspects, a criterion based on the Bayesian error bisected the target global aspect into smaller sectors to decrease the variance of the estimate and, subsequently, decrease the probability of error. In doing so, discriminative features that have acceptable minimum probability of error were achievable. The minimum probability of error criterion and the angular bisection of the target could separate the feature set of two targets with similar resonances.