Ratio Of Maximum Eigenvalue Research Articles

High-Performance Passive Eigen-Model-Based Detectors of Single Emitter Using Massive MIMO Receivers

For a passive direction of arrival (DOA) measurement system using massive multiple input multiple output (MIMO), it is mandatory to infer whether the emitter exists or not before performing DOA estimation operation. Inspired by the detection idea from radio detection and ranging (radar), three high-performance detectors are proposed to infer the existence of single or multiple passive emitter from the eigen-space of sample covariance matrix of receive signal vector. The first test statistic (TS) is defined as the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) while the second one is defined as the ratio of Max-EV to noise variance (R-MaxEV-NV). The third TS is the mean of maximum eigen-value (EV) and minimum EV(M-MaxEV-MinEV). Their closed-form expressions are presented and the corresponding detection performance is given. Simulation results show that the proposed M-MaxEV-MinEV and R-MaxEV-NV methods can approximately achieve the same detection performance better than the traditional generalized likelihood ratio test method and energy detection method with a given false alarm probability.

Manmade target extraction based on multistage decision and its application for change detection in polarimetric synthetic aperture radar image

Targets of interest are different in various applications in which manmade targets, such as aircraft, ships, and buildings, are given more attention. Manmade target extraction methods using synthetic aperture radar (SAR) images are designed in response to various demands, which include civil uses, business purposes, and military industries. This plays an increasingly vital role in monitoring, military reconnaissance, and precision strikes. Achieving accurate and complete results through traditional methods is becoming more challenging because of the scattered complexity of polarization in polarimetric synthetic aperture radar (PolSAR) image. A multistage decision-based method is proposed composed of power decision, dominant scattering mechanism decision, and reflection symmetry decision. In addition, the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio are applied to assist the decision. Fully PolSAR data are adopted to evaluate and verify the approach. Experimental results show that the method can achieve an effective result with a lower false alarm rate and clear contours. Finally, on this basis, a universal framework of change detection for manmade targets is presented as an application of our method. Two sets of measured data are also used to evaluate and verify the effectiveness of the change-detection algorithm.

Spectrum sensing in cognitive radio networks with known and unknown noise levels

In this study, the authors consider the problem of signal detection in cognitive radio under the cases of known and unknown noise levels. The analysis is focused on maximum eigenvalue-based method which requires the knowledge of noise variance and the ratio of maximum eigenvalue to the trace of covariance matrix. The analytical formulas are derived for threshold calculation and probability of detection for the considered methods. Performance of the considered methods is measured through simulation analysis. The performance comparison between the considered methods and other existing methods is provided. Simulations based on independent and identically distributed signals and wireless microphone signals are presented to verify the various sensing methods.