A Parallel Connected Hybrid Microstrip-Substrate Integrated Waveguide Bandstop Filter

This study presents an original parallel connected hybrid microstrip-substrate integrated waveguide (PCHM-SIW) bandstop filter. A low-pass filter implemented on a microstrip structure and a SIW-based high-pass filter are connected in parallel to each other. In this way, the aim is to obtain a bandstop filter in the novel hybrid design. The parallel connected hybrid microstrip-substrate integrated waveguide (PCHM-SIW) bandstop filter is synthesised, simulated, and produced. The effects of connecting filters in parallel are discussed. It is seen from the results of CST Studio Suite simulation that PCHM-SIW bandstop filter has a bandwidth of 2.85 GHz and a center frequency of 4.26 GHz. The frequency change rate of the center frequency between simulation and measurement is 7.02 % where it is just 3.76 % for the deviation in bandwidth. The results of the simulation and those of the measurement are close to each other. These results converge to ideal analytical results

A novel method for electromagnetic target classification using the music algorithm: Applied to small-scale aircraft targets

This paper introduces a novel target classification method based on the extraction of target features by using natural response related late-time electromagnetic scattered field data. In the feature extraction stage, the use of multiple signal classification (MUSIC) algorithm together with a simple but effective feature fusion approach leads to a significant reduction in the sensitivity of classification accuracy to both aspect angle variations and the signal-to-noise ratio (SNR) levels of the data. Another advantage of the proposed method is that the scattered target data is needed at only a few different target aspects in the stage of classifier design. Furthermore, real time classification within a small fraction of a second is feasible due to computational simplicity offered by this method in the final decision stage. When applied to geometrically complicated targets such as small-scale aircraft, this method provides high accuracy rates even for extremely noisy data

The MUSIC algorithm-based electromagnetic target classification for isolated targets from incomplete frequency domain data

This paper investigates the performance of a new electromagnetic target classification method to recognize isolated targets in the presence of scattered frequency domain data which may be severely incomplete at low frequencies. The suggested method utilizes multiple signal classification (MUSIC) algorithm to construct approximate pole location maps of the targets without determining the exact pole values. In this work, this method is validated for small-scale aircraft targets modeled by thin, conducting wires with incomplete data, which brings additional difficulty in target classification problems. It is shown that the method provides high accurate classification rates even when incomplete frequency domain data with low signal-to-noise ratio values are utilized while it needs only a few different reference aspects and small memory storage in classifier design

A novel electromagnetic target recognition method by MUSIC algorithm

This paper introduces a novel method for aspect invariant electromagnetic target recognition based on the use of multiple signal classification (MUSIC) algorithm to extract late-time resonant target features from the ultra-wideband scattered data. This approach achieves very high accuracy rates even at very low signal-to-noise ratio (SNR) values although it needs scattered data for classifier design at only a few different aspects and makes use of the MUSIC algorithm in a simple and computationally efficient way. Details of the theoretical background, the classifier design process and the demonstrations for conducting and dielectric spherical targets are presented in the following section

A new electromagnetic target classification method with MUSIC algorithm

This paper introduces a novel method for aspect invariant electromagnetic target recognition based on the use of multiple signal classification (MUSIC) algorithm to extract late-time resonant target features from the ultra-wideband scattered data. This method is mainly based on the usage of MUSIC spectra obtained from electromagnetic scattered data as the target features. This approach achieves very high accuracy rates even at very low signal-to-noise ratio (SNR) values although it needs scattered data for classifier design at only a few different aspects and makes use of the MUSIC algorithm in a simple and computationally efficient way. Details of the theoretical background, the classifier design process and the results for conducting and dielectric spherical targets will be presented in the following sections

The Theory and Application of an Electromagnetic Target Recognition Method based on Natural-Resonance for Multi-Targets

This paper presents the application of an electromagnetic target recognition method based on natural resonance mechanism and MUSIC algorithm to the target sets containing single amd multi-targets. The simpler case of the proposed method was applied to single targets previously and successful results were obtained [1]. However, when multi-targets are added to the test target set, the method needs crucial modifications and these modifications are mentioned in detail in this study. Owing to these modifications, the method becomes highly invariant to aspect angle and the distance between the multiple targets observed at once. The final version of the mentioned method is applied for the recognition of a target set containing single and multiple perfectly conducting spheres and sufficient accuracy rates are obtained even for low signal-to-noise ratios