RSS-Based Byzantine Fault-Tolerant Localization Algorithm Under NLOS Environment

Self Cite

Ship fire is one of the greatest dangers to ship navigation safety. Nevertheless, typical detection methods have limited detection effectiveness and accuracy due to distance restrictions and ship motion. Although the issue can be addressed by image recognition algorithms based on deep learning, the computational complexity and efficiency for ship detection are tough. This paper proposes a lightweight target identification technique based on the modified YOLOv4-tiny algorithm for the precise and efficient detection of ship fires, taking into account the distinctive characteristics of ship fires and the marine environment. Initially, a multi-scale detection technique is applied to broaden the detection range and integrate deep semantic information, thereby enhancing the feature information of small targets and obscured objects and improving the detection precision. Then, the proposed algorithm employs the SE attention mechanism for inter-channel feature fusion to improve the capability of feature extraction and the precision of ship fire detection. Last but not least, picture transformation and migration learning are added to the small ship fire dataset to accelerate the convergence pace, improve the convergence effect, and reduce dataset dependence. The simulation experiments reveal that the proposed I-YOLOv4-tiny + SE model outperforms the benchmark algorithm in terms of ship fire detection accuracy and detection efficiency and that it satisfies the real-time ship fire warning criteria in demanding maritime environments.

Section: Discussionmentioning

confidence: 99%

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Mei

et al. 2022

Self Cite

“…The paper then compares it with the joint ML method. The effect of the fault and various noises are taken into account in [ 39 , 40 ]. Ref.…”

Section: Range-based Algorithmsmentioning

confidence: 99%

“…Ref. [ 39 ] addresses byzantine fault and NLOS effect. Byzantine fault is considered by including non-Gaussian interference noise corrupting the transmission data, and the NLOS effect is modelled by adding a bias term to the propagation model.…”

Section: Range-based Algorithmsmentioning

confidence: 99%

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Kabiri

Cimarelli

Bavle

et al. 2022

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored.

“…[ 33 ] and Mei, X. [ 34 ], all choose to ignore the difference of the standard deviation of noise after the Taylor expansion of the term containing the amount of noise in the model transformation. Therefore, if the received signal strength contains noise, the final estimated position has some deviation, which increases as the noise standard deviation increases, and the deviation also increases, resulting in the poor estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation

Ding

Zhong

Wang

et al. 2022

A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex estimator is then transformed into a convex estimator by semi-definite programming, and the global minimum of the target location estimation is obtained. This algorithm aims at the L0 known problem and then extends its application to the case of L0 unknown. The simulations and experimental results show that the proposed algorithm has better accuracy than the existing positioning algorithms.