joint detection
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Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 257
Author(s):  
Xiuqi Deng ◽  
Xin Bian ◽  
Mingqi Li

In recent years, Faster-than-Nyquist (FTN) transmission has been regarded as one of the key technologies for future 6G due to its advantages in high spectrum efficiency. However, as a price to improve the spectrum efficiency, the FTN system introduces inter-symbol interference (ISI) at the transmitting end, whicheads to a serious deterioration in the performance of traditional receiving algorithms under high compression rates and harsh channel environments. The data-driven detection algorithm has performance advantages for the detection of high compression rate FTN signaling, but the current related work is mainly focused on the application in the Additive White Gaussian Noise (AWGN) channel. In this article, for FTN signaling in multipath channels, a data and model-driven joint detection algorithm, i.e., DMD-JD algorithm is proposed. This algorithm first uses the traditional MMSE or ZFinear equalizer to complete the channel equalization, and then processes the serious ISI introduced by FTN through the deepearning network based on CNN or LSTM, thereby effectively avoiding the problem of insufficient generalization of the deepearning algorithm in different channel scenarios. The simulation results show that in multipath channels, the performance of the proposed DMD-JD algorithm is better than that of purely model-based or data-driven algorithms; in addition, the deepearning network trained based on a single channel model can be well adapted to FTN signal detection under other channel models, thereby improving the engineering practicability of the FTN signal detection algorithm based on deepearning.


2021 ◽  
Vol 14 (1) ◽  
pp. 39
Author(s):  
Qian Zhang ◽  
Weibo Huo ◽  
Jifang Pei ◽  
Yongchao Zhang ◽  
Jianyu Yang ◽  
...  

The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection.


2021 ◽  
Author(s):  
Lucia Migliorelli ◽  
Emanuele Frontoni ◽  
Simone Appugliese ◽  
Giuseppe Pio Cannata ◽  
Virgilio Carnielli ◽  
...  

2021 ◽  
Vol 97 ◽  
pp. 103540
Author(s):  
Bereket H. Woldegiorgis ◽  
Chiuhsiang J. Lin ◽  
Riotaro Sananta

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2566
Author(s):  
Jarosław Magiera

This paper presents a method for the joint detection and direction of arrival (DOA) estimation of low probability of detection (LPD) signals. The proposed approach is based on using the antenna array to receive spread-spectrum signals hidden below the noise floor. Array processing exploits the spatial correlation between phase-delayed copies of the signal and allows us to evaluate the parameter used to make the decision about the presence of LPD transmission. The DOA estimation is based on the covariance between signals received by sensors for the fixed geometry of the antenna array. Moreover, the paper provides a method for mitigating narrowband interferences prior to signal detection. The presented methods were verified through simulations which proved that the confident detection of a one-second transmission in an additive white Gaussian noise channel is possible even when the noise is 24 dB higher than the power of the received signal. The performance of DOA estimation is analyzed in a wide range of signal-to-noise and interference-to-noise ratios. It is found that the DOA may be estimated with an RMS error not exceeding 10 degrees, even if interference occupies 15% of the analyzed frequency band.


Author(s):  
WANSONG XU ◽  
TIANWU CHEN ◽  
FANYU DU

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.


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