Automatic modulation classification (AMC) is an essential part in a cognitive radio receiver. Benefited from the discriminative constellation characteristics among most modulations, AMC methods based on constellation diagrams usually achieve pleasant performance. However, in noncooperation communication systems, constellation diagrams expressing modulations explicitly are difficult to obtain via blind symbol timing synchronization, especially in complicated wireless channels. Therefore, this article proposes a novel constellation diagram-based AMC architecture called attentive Siamese networks (ASNs) by considering multitiming constellation diagrams (MCDs) and selecting the proper symbol timings at the feature level, which is a more robust way than the conventional signal-level symbol timing synchronization. In detail, convolutional neural networks sharing the same parameters first extract deep feature vectors for MCDs. Then, an attention inference module weights all the deep feature vectors. Finally, AMC is realized based on the weighted feature vectors. Moreover, the ASN architecture can be trained end-to-end. Comparing with the state-of-the-art methods that take diverse representations of received baseband signals as input, experimental results based on the RadioML 2018.01A dataset and non-Gaussian noise dataset demonstrate that ASN achieves a remarkable improvement, whose classification accuracy goes over 99% when the signal-to-noise ratio (SNR) > 10 dB.

This paper investigates the problem of locating a moving target using bistatic range (BR) and bistatic range rate (BRR) measurements in multi-input and multi-output (MIMO) radar systems. The proposed estimator constructs two separate semidefinite programming (SDP) problems, in which both the BR and BRR measurement noise powers can be neglected. By utilizing the optimization toolbox, we efficiently solve the SDP problems and give the source position and velocity successively. Unlike the traditional techniques, the proposed method does not require the exact prior knowledge of the measurement noise powers. Simula-tion results validate the performance of the proposed method.

By exploiting the relationship between probability density and the differential geometry structure of received data and geodesic distance, the recently proposed information geometry (IG) method can provide higher accuracy and resolution ability for direction of arrival (DOA) estimation than many existing methods. However, its performance is not robust even for high signal-to-noise ratio. To have a deep understanding of its unstable performance, a theoretical analysis of the IG method is presented by deriving the relationship between the cost function and the number of array elements, powers and DOAs of source signals, and noise power. Then, to make better use of the nonlinear and super resolution property of the cost function, a Scaling TRansform based INformation Geometry (STRING) method is proposed, which simply scales the array received data or its covariance matrix by a real number. However, the expression for the optimum value of the scalar is complicated and related to the unknown signal DOAs and powers. Hence, a decision criterion and a simple search based procedure are developed, guaranteeing a robust performance. As demonstrated by computer simulations, the proposed STRING method has the best and robust angle resolution performance compared with many existing high resolution methods and even outperforms the classic Cramer-Rao bound, although at the cost of a bias in the estimation results.

Wireless localization systems have significant impact in the field of human-driven edge computing (HEC). It became very attractive among the researchers and used in applications of numerous areas such as medical, industrial, public safety, logistics, and so on. Ultra-wideband (UWB) technology used in localization systems owing to achieving high accuracy in real-time. In this paper, we exhibit a UWB based localization system based on the edge computing (EC) paradigm to analyze the wandering behavior of the patients who are suffering from dementia disease in the large-scale form. Physical changes in the brain are responsible for dementia disease. The appearance of wandering behavior is a common manner of the patients, which also a threat and interference for caregivers. We used the UWB standard appliance to symbolize various sorts of wandering patterns, including pacing, lapping, and two random movements in the large 2D map. The flow of all the movements illustrated in the X and Y-axis. Support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms used to classify all the patterns and accuracy result is above 99%. The result shows that the proposed system can achieve high accuracy in classification and satisfactory for applications in the medical area.

As known, utilization of the information about signal waveforms can improve the direction-of-arrival (DOA) estimation results. However, with a fast moving platform, the Doppler effect occurs, which distorts the known waveforms and may result in large DOA estimation bias and even errors for conventional DOA estimation methods for sources with known waveforms. To deal with this problem, a robust DOA estimation method for sources with known waveforms against Doppler shifts is developed. The proposed method first transforms the nonlinear mixing of Doppler shifts in the model to an approximately linear one using discrete-time Fourier transform and finite Taylor series expansion. Then, multiple oblique projectors are constructed to separate each component corresponding to different order of derivatives. Finally, estimations of DOAs, complex amplitudes, and Doppler shifts are obtained simultaneously. Simulation results show that the proposed method has a much more robust DOA estimation performance than existing methods for sources with known waveforms.