TSP Volume 69 | 2021

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TSP Volume 69 | 2021

This paper addresses the problem of target detection against a background of Gaussian clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector and detection performance under the Neyman-Pearson criterion for general frequency snapshot selection with arbitrary DOF. When the clutter statistics are unknown, we use a uniformly random frequency snapshot selection method and show how the DOF employed affects the detection performance. 

Time-frequency analysis is often used to study non stationary multicomponent signals, which can be viewed as the surperposition of modes. To understand such signals, it is essential to identify the ridges associated with the modes in the time-frequency plane.

Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance.

We study distributed filtering for a class of uncertain systems over corrupted communication channels. We propose a distributed robust Kalman filter with stochastic gains, through which upper bounds of the conditional mean square estimation errors are calculated online. 

In this paper, a smart pilot sequence assignment method is proposed to minimize inter-cell interference generated in a massive multi-input multi-output (MIMO) system due to pilot contamination in uplink TDD (Time Division Duplex) mode. The proposed method employs a zero-one integer linear programming method as the assignment algorithm.

Wide-sense cyclostationary processes are an important class of non-stationary processes that have a periodic structure in their first- and second-order moments. This article extends the notion of cyclostationarity (in the wide sense) to processes where the mean and covariance functions might depart from strict periodicities and constant amplitudes.

In this paper, power allocation is examined for the coexistence of a radar and a communication system that employ multicarrier waveforms. We propose two designs for the considered spectrum sharing problem by maximizing the output signal-to-interference-plus-noise ratio (SINR) at the radar receiver while maintaining certain communication throughput and power constraints.

Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target. 

Time-frequency (TF) representations of time series are intrinsically subject to the boundary effects. As a result, the structures of signals that are highlighted by the representations are garbled when approaching the boundaries of the TF domain. In this paper, for the purpose of real-time TF information acquisition of nonstationary oscillatory time series, we propose a numerically efficient approach for the reduction of such boundary effects.

Adopting low-resolution analog-to-digital converters (ADCs) for receive antennas of a multiple-input multiple-output (MIMO) system can remarkably reduce the hardware cost, circuit power consumption as well as amount of data to be transferred from RF components and the baseband-processing unit.


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