Passive MIMO Radar Detection with Unknown Colored Gaussian Noise

The target detection of the passive multiple-input multiple-output (MIMO) radar that is comprised of multiple illuminators of opportunity and multiple receivers is investigated in this paper. In the passive MIMO radar, the transmitted signals of illuminators of opportunity are totally unknown, and the received signals are contaminated by the colored Gaussian noise with an unknown covariance matrix. The generalized likelihood ratio test (GLRT) is explored for the passive MIMO radar when the channel coefficients are also unknown, and the closed-form GLRT is derived. Compared with the GLRT with unknown transmitted signals and channel coefficients but a known covariance matrix, the proposed method is applicable for a more practical case whenthe covariance matrix of colored noise is unknown, although it has higher computational complexity. Moreover, the proposed GLRT can achieve similar performance as the GLRT with the known covariance matrix when the number of training samples is large enough. Finally, the effectiveness of the proposed GLRT is verified by several numerical examples.

A-CRNN-Based Method for Coherent DOA Estimation with Unknown Source Number

Estimating directions of arrival (DOA) without knowledge of the source number is regarded as a challenging task, particularly when coherence among sources exists. Researchers have trained deep learning (DL)-based models to attack the problem of DOA estimation. However, existing DL-based methods for coherent sources do not adapt to variable source numbers or require signal independence. Herein, we put forward a new framework combining parallel DOA estimators with Toeplitz matrix reconstruction to address the problem. Each estimator is constructed by connecting a multi-label classifier to a spatial filter, which is based on convolutional-recurrent neural networks. Spatial filters divide the angle domain into several sectors, so that the following classifiers can extract the arrival directions. Assisted with Toeplitz-based method for source-number determination, pseudo or missed angles classified by the estimators will be reduced. Then, the spatial spectrum can be more accurately recovered. In addition, the proposed method is data-driven, so it is naturally immune to signal coherence. Simulation results demonstrate the predominance of the proposed method and show that the trained model is robust to imperfect circumstances such as limited snapshots, colored Gaussian noise, and array imperfections.

Cognitive Radio Networks

Cognitive radio (CR) offers a novel way for effective usage of wireless spectrum by using dynamic spectrum sensing and allocation. One of the main components of CR is to find a spectrum hole for data transmission. Spectrum hole can be found by using spectrum sensing, a geolocation database, or by using a beacon signal. In this chapter, the authors describe algorithms for spectrum sensing in the presence of both additive white Gaussian and colored Gaussian noise. The algorithms include blind, non-blind, and cooperative sensing-based methods. The authors have compared the performance of various methods for IEEE 802.22 standard (which is the first standard incorporating CR).

1/f spectrum estimation based on α-stable distribution in colored Gaussian noise environments

The main goal of this paper is to suppress the effect of unavoidable colored Gaussian noise on declining accuracy of transistor 1/f spectrum estimation. Transistor noises are measured by a nondestructive cross-spectrum measurement method, which is first to amplify the voltage signals through ultra-low noise amplifiers, then input the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. Therefore, by utilizing the properties of α-stable distribution, we propose a cross-spectrum method effective in noisy environments based on samples normalized cross-correlation function. Simulation results and diodes output noise spectrum estimation results confirm the effectiveness of our method.