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In this paper, we study the application of frequency response masking (FRM) technology in polyphase discrete Fourier transform (DFT) filter bank (FB). Due to the inability of FRM methods with conventional structure to simultaneously meet the needs of sharp transition band polyphase DFT FB to improve the signal-to-interference ratio of channelized signals and reduce the complexity of FBs, an improved conjugate frequency modulation frequency response masking (CFM-FRM) method is proposed. The proposed CFM-FRM method solves the problem by frequency modulation, and consumes less multipliers by taking advantage of the orthogonality between sub-filters in CFM-FRM FB. The efficient realization of CFM-FRM based polyphase DFT FB is derived and the multi-size configuration method is illustrated via coefficient decimation. We analyze the adaptability of related FRM works to polyphase DFT FB. The simulation and analysis show that the CFM-FRM method is applicable to polyphase DFT FB, the CFM-FRM based polyphase DFT FB has less multipliers than related methods in other works, and the performances of multi-size FBs meet the requirements. [ABSTRACT FROM AUTHOR]

In radar specific emitter identification (SEI), various types of unintentional modulation on pulse (UMOP) are selected as the features for discriminating between different radars. Unintentional Phase Modulation on Pulse (UPMOP), a typical type of UMOP, can provide crucial information for identifying radars. In most radar SEI algorithms, sacrificing time efficiency for higher accuracy is a common trade-off. This paper proposes a method to solve this problem by combining denoised UPMOP sequences with an Attention-based Gated Recurrent Units (Attention-GRU) model, which showed an excellent performance. Firstly, the cause of UPMOP is analyzed and the phase observation model of radar emitter signals and mathematical model of UPMOP are given. Then, the least-squares method is used to eliminate the linear trend of the phase observation model and obtain a noised estimation of the UPMOP sequences. Thirdly, the uniform B-spline (UBS) curves are then used to fit the noised estimation, resulting in a denoised and refined UPMOP sequence. Finally, the Attention-GRU model is employed to extract features from the denoised UPMOP sequences to identify radar emitters automatically. Results from simulation and measured data experiments show that the overall recognition rate of the algorithm reaches over 93% and the algorithm has excellent performance, with high identification accuracy and relatively low time consumption, even in low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

Radar detection and communication transmission play a crucial role in both civilian and military applications, and their integrated implementation has now become a research hotspot. In this paper, we propose a method aiming at the application requirements of fast estimation to solve the issue of low real-time performance in the estimation of range and velocity for multi-carrier OFDM-based radar and communication (RadCom) shared signal. During the process, the shared signal echo model is transformed into a signal model similar to the uniform line array, and uses a weighted time-frequency smoothing (WTFS) algorithm to achieve decoherence processing, then the ESPRIT-MUSIC (E-M) algorithm is further used to achieve joint range and velocity fast estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed WTFS algorithm has better performance compared with the frequency smoothing algorithm, while the E-M algorithm achieves superior resolution accuracy and fast estimation with similar or lower computational complexity than existing more applied estimation methods. [ABSTRACT FROM AUTHOR]

In this article, an effective algorithm is proposed for trajectory optimization in airborne radar for extended target tracking (ETT). The key idea of the proposed airborne radar trajectory planning (ARTP) scheme is to model this problem as a partially observable Markov decision process (POMDP) and plan the waypoints of airborne radar using deep reinforcement learning (DRL) in order to improve the target's kinematic state and shape tracking accuracy, subject to the airborne radar maneuverability limitation. Specifically, at each tracking interval, the predicted Gaussian Wasserstein Distance (GWD) is analytically calculated and subsequently adopted as a reward function to evaluate the performance of extended target tracking. Then the DRL agent autonomously learns trajectory planning strategies to maximize ETT performance. Simulation results demonstrate that the proposed ARTP algorithm can deliver superior performance in terms of maximizing the overall ETT performance compared with other benchmarks. [ABSTRACT FROM AUTHOR]