Due to its capability of capturing the kinematic properties of a target object, radar micro-Doppler signatures (m-DS) play an important role in radar target classification. This is particularly evident from the remarkable number of research papers published every year on m-DS for various applications. However, most of these works rely on the support vector machine (SVM) for target classification. It is well known that training an SVM is computationally expensive due to its nature of search to locate the supporting vectors. In this paper, the classifier learning problem is addressed by a total error rate (TER) minimization where an analytic solution is available. This largely reduces the search time in the learning phase. The analytically obtained TER solution is globally optimal with respect to the classification total error count rate. Moreover, our empirical results show that TER outperforms SVM in terms of classification accuracy and computational efficiency on a five-category radar classification problem.
An iterative reweighted least squares (IRLS) algorithm is presented in this paper for the minimax design of FIR filters. In the algorithm, the resulted subproblems generated by the weighted least squares (WLS) are solved by using the conjugate gradient (CG) method instead of the time-consuming matrix inversion method. An almost minimax solution for filter design is consequently obtained. This solution is found to be very efficient compared with most existing algorithms. Moreover, the filtering solution is flexible enough for extension towards a broad range of filter designs, including constrained filters. Two design examples are given and the comparison with other existing algorithms shows the excellent performance of the proposed algorithm.
In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly been used in many civil applications. However, they also pose a significant threat in restricted zones. Radar can be used to detect and discriminate UAVs. Due to the low flying altitude of the UAVs, it is found that the radar signals also include some unwanted echoes, reflected by building, ground, trees and grasses etc. Consequently, it has not been possible to get the clean UAVs characteristics for further classification. In this paper, the MTI filter is applied to cancel the ground clutter and based this, an improved MTI filter is further proposed. Compared with the traditional MTI filter, the improved one significantly enhances ground clutter rejection capability while maintaining most of the target power. As the result, the cleaner UAVs classification characteristics can be obtained. The effectiveness of the proposed method has been verified by an experimental CW radar dataset, collected from a helicopter UAV.
In this paper, a full-field photothermal imaging technique, which does not require a time consuming scan as used in the conventional photothermal imaging system, is reported. Imaging on gold nanoparticles (70 nm) and a blue polystyrene bead (193 nm) were conducted and the experimental results demonstrate the visualization ability of the photothermal imaging technique on nanotargets that are below the diffraction limit. The photothermal imaging system can be operated in an ambient environment where vacuum is not required.
A recently developed algorithm is applied to calculate a state space realization of a 3D microscopy image set. It is based on interpreting the image set as the impulse response of a 3D separable system. As an application it is shown how this algorithm, combined with approximation steps, can be used to suppress noise in 3D experimental point spread functions. The approach was motivated by a well known problem that a noisy point spread function degrades the results of deconvolution algorithms for the restoration of 3D fluorescence microscopy image sets. The proposed approach can also be applied
to 3D fluorescence microscopy image sets of cells.
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