Article
Engineering, Aerospace
Aya Mostafa Ahmed, Alaa Alameer Ahmad, Stefano Fortunati, Aydin Sezgin, Maria Sabrina Greco, Fulvio Gini
Summary: This article proposes a reinforcement learning based algorithm for cognitive multitarget detection in the presence of unknown disturbance statistics in massive multiple input multiple output cognitive radar (CR). By optimizing transmitted waveforms and focusing energy in specific range-angle cells to maximize detection probability, the RL-based beamforming outperforms conventional and adaptive approaches in terms of target detection performance, especially under environmentally harsh conditions.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2021)
Article
Computer Science, Information Systems
Jiyoon Noh, Yohan Kwon, Juhyung Lee, Hoki Baek, Jaesung Lim
Summary: The paper proposes an adaptive sliding-window radar spectrum sensing detection scheme to optimize window size by estimating pulsewidth, resulting in performance improvement.
IEEE SYSTEMS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Mohammad Alaee-Kerahroodi, Ehsan Raei, Sumit Kumar, Bhavani M. R. R. Shankar
Summary: This paper presents a spectrum-sharing prototype that demonstrates the application of waveform optimization for coexistence between communications and cognitive radar systems. By sensing the environment in real-time and sequentially optimizing radar transmit waveform, the proposed algorithm's performance is validated and evaluated through experiments.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Shixing Yang, Wei Yi, Andreas Jakobsson, Yao Wang, Hang Xiao
Summary: This paper addresses the weak signal detection problem in a massive colocated multiple-input multiple-output (MIMO) radar. To cope with the sheer amount of data produced by the large-scale antennas, a low-bit quantizer is introduced in the sampling process to enable both for hardware limitations and a high detection performance. The generalized likelihood ratio test (GLRT) detector is proposed for the quantized data, with the batch gradient descent algorithm being introduced to form an estimate of the unknown parameters. Furthermore, as a low-complexity alternative to the GLRT detector, we propose a multi-bit Rao detector, yielding a closed-form test statistic, whose theoretical distribution is also presented. Finally, we refine the design of the quantizer by optimizing the quantization thresholds, which are obtained using the particle swarm optimization algorithm. Results from simulation and experimental data demonstrate the performance of the detectors using both unquantized and quantized data. They corroborate the theoretical analyses and show that the performance with 3-bit quantization yields a performance that approaches the cases without quantization, while reducing the overall complexity of the system substantially.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Engineering, Aerospace
Mengru Sun, Weijian Liu, Jun Liu, Peiqin Tang, Chengpeng Hao
Summary: This article discusses the problem of adaptive detection of a multichannel subspace signal in the presence of constrained interference. The gradient test is derived and found to have the same form as the existing subspace-based generalized likelihood ratio test (SGLRT). The statistical performance of the SGLRT in the presence of orthogonal interference is also derived, showing that both orthogonal interference and signal mismatch can degrade the detection performance of the detectors.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Chenguang Liu, Yunfei Chen, Shuang-Hua Yang
Summary: This work investigates the application of deep learning in communication systems subject to interference from radar systems. The study finds that the learning-based detector can achieve comparable performance to the traditional detector in the radar-communication system without interference cancellation. Preprocessing the received signals with PCA can improve the performance of the fully connected deep neural network when interference is strong. Additionally, LSTM shows more robust performance than FCDNN in the presence of time-related distortion in the channel.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Nikola Petrovic, Marija Petrovic, Vladimir Milovanovic
Summary: With the increasing number of vehicles on the road, accidents have become more common. One solution is to use safety systems that alert drivers to potential accidents and help them brake safely. Most of these systems use millimeter-wave radar as sensors, but real-time data processing from multiple sensors is a challenge. This study presents a hardware generator for early detection of automotive obstacles and reduces system reaction delay and braking distance.
Article
Environmental Sciences
Yong Wu, Zhikun Chen, Dongliang Peng
Summary: This paper addresses the issue of contamination of the reference signal by target echoes in passive bistatic radar (PBR). A novel target detection method centered around purifying the reference signal is proposed. It involves modeling, analysis, localization, and purification of the impure reference signal. The effectiveness of the method is demonstrated through simulation results.
Article
Engineering, Electrical & Electronic
Xuyang Wang, Bo Tang, Ming Zhang
Summary: This paper addresses the problem of waveform design for Rician target detection with multiple-input-multiple-output radar. It employs the relative entropy between the distributions of observations under two hypotheses as the design metric, and develops a novel algorithm based on minorisation-maximisation. The algorithm has lower computational complexity and can be extended to design waveforms under various practical constraints.
IET RADAR SONAR AND NAVIGATION
(2022)
Article
Engineering, Electrical & Electronic
Ziping Wei, Bin Li, Tao Feng, Yiwen Tao, Chenglin Zhao
Summary: Millimeter-wave (mmWave) radar is crucial for emerging autonomous driving. In this work, a new area-based constant false-alarm rate (CFAR) framework is developed to improve the detection signal-to-noise ratio (SNR) by fully exploiting the potential diversity gain provided by mmWave radars. Theoretical analysis shows that the achieved SNR gain of the method grows with the area size of each target on the Range Doppler Map (RDM). Numerical simulations and real experiments demonstrate the significant improvement in detection probability for both single-input and single-output (SISO) and multiple-input multiple-output (MIMO) radar systems.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Luoyan Zhu, Yinsheng Liu, Danping He, Ke Guan, Bo Ai, Zhangdui Zhong, Xi Liao
Summary: This paper investigates an advanced signal processing technique to suppress noise in radar sensing, with a focus on high-performance detection. The algorithm based on Karhunen - Loeve transform (KLT) is derived using frequency modulated continuous wave radar as a case study. The performance of the algorithm is evaluated using different eigenvalue selection strategies, and the detection capability of the processor is demonstrated using constant false alarm ratio detector.
IET SIGNAL PROCESSING
(2022)
Article
Engineering, Aerospace
Nadav Neuberger, Risto Vehmas, Joachim H. G. Ender
Summary: This article presents a novel parameter-controlled design method to transform element-level data into a lower dimensional beamspace for optimal detection and DOA estimation. The method achieves predefined performance criteria by balancing between detection and DOA estimation performance.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Jingxia Li, Ning Zhang, Hang Xu, Bingjie Wang, Li Liu, Mingrui Zhao
Summary: A polarimetric chaotic ground penetrating radar is proposed to improve the detection accuracy of underground pipes. The use of chaotic signal and multi-polarization detection mode enhances pipe responses and accurately determines their shape and distribution.
IEEE SENSORS JOURNAL
(2022)
Article
Chemistry, Analytical
Woosung Hwang, Hongje Jang, Myungryul Choi
Summary: Drones are being widely used but detecting them for defense or security purposes is challenging due to their small size and plastic materials. This paper proposes a radar system that can collect high-resolution data to accurately determine the position of a drone. However, the collected data contains clutter and noise, which needs to be suppressed and canceled. Four clutter cancellation algorithms are evaluated and compared, with the proposed algorithm combining standard deviation with the least mean squares method. Outdoor experiments are conducted to measure and compare the performance of these algorithms.
Article
Engineering, Electrical & Electronic
Budiman P. A. Rohman, Muhammad Andra, Masahiko Nishimoto
Summary: This article presents a new signal processing method for extracting and enhancing respiration signals from radar mounted on drones. Experimental results show that the method effectively suppresses vibration, enhances respiration signals, and performs well under real-world conditions.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
