Quick cooperative spectrum sensing in cognitive unmanned aerial vehicles networks

Wu, Jun; Zhang, Jia

doi:10.1002/ett.4441

Cited by 7 publications

(16 citation statements)

References 39 publications

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“…In this process, the preconditioning threshold is triggered first, and after that, the SPRT algorithm detects the fall. According to (40), 𝑟 = 1, 𝛥 = 𝑙𝑛𝐴, when the preconditioning threshold is triggered, the algorithm has already determined the user's fall state. According to the results in Fig.…”

Section: ) Simulation Resultsmentioning

confidence: 99%

“…The statistical decision is made in SPRT by using the sequence observations in each signal channel and the likelihood ratio test in each cycle step. SPRT technology is under development, Wu et al [40] proposed Soft truncation (ST)-SPRT, which can improve detection performance in terms of detection performance. This study refers to some ideas of soft-truncated SPRT.…”

Section: Related Workmentioning

confidence: 99%

“…To realize the above design, SPRT detection needs to be modified to add preconditions and weights, which can be expressed as formula (40) and formula (41):…”

Section: F Fall Detection Algorithm For Smart Walkers Based On Improv...mentioning

confidence: 99%

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Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

et al. 2022

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Traditional walkers are commonly used for the elderly in social life, which solves the basic problem of walking, but it is difficult to ensure safety when a fall occurs, and the human-computer interaction is poor. The image recognition method or the IMU sensor method fixed on the user, such as a wearable watch, is used by most of the current fall detection methods. Wearable sensors require the user's wearing operation, which is a little troublesome, and the detection accuracy is related to the way of wearing. The image recognition method requires a high-priced camera and a fixed installation position, which is unable to adapt to outdoor activities. We investigated a low-cost method of mounting the sensor on the body of the walker. We propose in this paper an improved fall detection method, namely Precondition and Limit Threshold SPRT (PLT-SPRT), and a novel fall detection system on the smart walker based on PLT-SPRT. The signals of the upper and lower limb sensors are fused based on the Kalman filter algorithm, and the admittance control parameters are obtained through the system identification method. In this study, the improved sequential probability ratio test algorithm is used to set the null hypothesis and the alternative hypothesis, construct the likelihood ratio and optimize the decision function, which is used to judge whether falls occur. The system is simulated by Matlab software, the user intention after fusion is more accurate, and the optimized decision function is judged accurately. Verified by the embedded system based on STM32 of the smart walker equipment in the real world, it can accurately identify the fallen state, with low detection delay, and the fallen state is detected about 160ms earlier than the traditional threshold-based detection algorithm, at the same time, the accuracy is higher than 94.9%, which meets the high real-time requirements of fall detection and is the ideal solution for smart walkers.

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Section: ) Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

et al. 2022

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“…In view of advantages of the quick detection theory, the CSS problem related to hypothesis test (ie, changepoint detection, sequential test, and Bayesian detection) is gained more in‐depth insight on the performance or efficiency in cognitive UAV or radio networks, that is, .References 4‐20. These related works have varying degrees of advantages or disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…These related works have varying degrees of advantages or disadvantages. For example, a soft truncation‐sequential probability ratio test was proposed by Wu et al 4 to take the sensing efficiency and performance according to various signal‐to‐noise ratio (SNR) at each micro‐sensing slot. Moreover, the authors further analyzed relationship among performance indices of CSS and approximated the actual detection performance and the stopping time by ignoring the excess over the boundaries and the random walk.…”

Section: Introductionmentioning

confidence: 99%

Bayesian quickest detection‐based spectrum sensing for cognitive UAV networks

Liang

2023

Trans Emerging Tel Tech

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With the gradual popularization of unmanned aerial vehicles (UAVs), spectrum resources are increasingly scarce. At this time, cognitive radio (CR) technology came into being, which can provide available spectrum resources for UAVs. However, different from the conventional CR networks, the UAV's location flexibility significantly reduces the spectrum sensing performance and increases the detection delay. Therefore, we propose a Bayesian quickest detection-based spectrum sensing for cognitive UAV networks in this paper. To this aim, we establish a multislot spectrum sensing model in a cognitive UAV network to realize cooperative spectrum sensing (CSS) within the single UAV and avoid disadvantages of cooperation among multiple UAVs. On the basis of this, we make use of Bayesian quickest detection to implement multislot spectrum sensing and evaluate its false alarm probability and detection delay. Further, the tradeoff problem between the false alarm and the detection delay is developed and converted into an optimization stopping time problem. Subsequently, the stopping time problem is theoretically proved and solved. Finally, compared to Bayesian detection, numerical simulation results show that the superiority of Bayesian quickest detection with respect to the false alarm probability and the detection delay is evident, which proves the correctness and effectiveness of the proposed tradeoff problem.

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Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

Bao

2022

Trans Emerging Tel Tech

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Cognitive radio (CR) technology is beneficial for unmanned aerial vehicles (UAVs) to access the spectrum resources needed for communication. Then, in order to integrate CR technology into UAVs without causing excessive interference to the primary users (PUs), cooperative spectrum sensing (CSS) is used as a key function of CR technology to detect the PUs for the UAV's available spectrum. However, the flexible locations of UAVs may significantly degrade the performance and efficiency of CSS within them. Hence, on the basis of a cognitive UAV networks (CUAVN) framework, we propose an internal cooperation paradigm to achieve the cooperative gain of CSS within an UAV. Furthermore, we convert the decision rule as an optimal stopping problem to optimize the decision rule for CSS. The optimization problem is carried out by the error probability regarding the PU presence of the phenomenon and the stopping time required to reach a global decision about the PU status. By optimizing the error probability respectively under the condition of a fixed sensing time, the optimization problem is transformed into an optimal stopping problem and solved by Markov stopping theory. Following this, we further determine a pair of thresholds in the optimal sequential decision rule from the minimal cost function. At last, numerical simulation results verify the correctness and effectiveness of the optimal sequential detection rule, and corroborate that in contrast to the external cooperative paradigm, the superiority of the internal cooperative paradigm under various network environments and parameters, in terms of the CSS performance and the stopping time.

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Quick cooperative spectrum sensing in cognitive unmanned aerial vehicles networks

Cited by 7 publications

References 39 publications

Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

Bayesian quickest detection‐based spectrum sensing for cognitive UAV networks

Optimal sequential decision rule for cooperative spectrum sensing of CUAVNs

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