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Frequency hopping (FH) signal has been widely used in military and civilian fields because of its superior anti-jamming performance. Meanwhile, its natural characteristics, i.e., hopped carrier frequency, brings a great challenge to the reconnaissance of FH emitters. To locate a high-speed moving FH emitter, the estimation of range difference (RD) and range rate difference (RRD) is a key process in passive localization. This paper proposes a joint RD and RRD estimation algorithm with the consideration of range migration (RM) during observation time. In this method, a new operation named frequency reversing transform is first presented to remove the random phase of the FH signal. Then, RD and RRD can be coherently estimated after RM correction via the scaled Fourier transform. This method accomplishes the RD and RRD estimation without knowing the hopped carrier frequencies of received FH signals, and it can be fast implemented by complex multiplications, fast Fourier transform (FFT) and inverse FFT operations without any searching process. Numerical experiments indicate that compared with several representative methods, the proposed estimation method achieves a better anti-noise performance with lower computational cost.

The presence of transmitter and receiver location uncertainties has been known to remarkably deteriorate the target localization accuracy in multi-static passive radar (MPR) system. This paper explores the use of calibration targets, the positions and velocities of which are known to the MPR system, to counter the loss in target localization accuracy arising from the transmitter/receiver location uncertainties. We firstly evaluate the Cramér-Rao lower bound (CRLB) for bistatic range (BR) and bistatic range rate (BRR)-based target localization in the presence of calibration targets, which analytically indicates the potential of calibration targets in enhancing localization accuracy. After that, a novel closed-form solution is proposed for target localization using the BR and BRR measurements from the unknown target as well as the additional BR and BRR measurements from the calibration targets. The proposed solution includes two processing steps, referred to as calibration step and localization step respectively. The calibration step is devoted to refine the inaccurate transmitter and receiver locations using the BR and BRR measurements from the calibration targets, and then the localization step is devoted to determine the target position and velocity based on the refined transmitter/receiver locations and the BR/BRR measurements from the unknown target. The accuracy of proposed solution is shown analytically to accomplish the CRLB under sufficiently small BR/BRR measurement noises and transmitter/receiver location errors. Simulation results verify the effectiveness and superiority of the proposed solution over existing algorithms.

By employing lens antenna arrays, the number of radio frequency (RF) chains in millimeter-wave (mmWave) communications can be significantly reduced. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which may result in a significant loss of energy efficiency (EE). In this paper, we propose a switch selecting network to solve this issue, where the analog part of the beamspace MIMO system is realized by a sub-connected switch selecting network rather than the PS network. Based on the proposed architecture and inspired by the cross-entropy (CE) optimization developed in machine learning, an optimal hybrid cross-entropy (HCE)-based hybrid precoding scheme is designed to maximize the achievable sum rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate, but also outperform the PSs schemes concerning energy efficiency.