اخبار و اعلانات
آمار
| تعداد نشریات | 38 |
| تعداد شمارهها | 1,408 |
| تعداد مقالات | 10,088 |
| تعداد مشاهده مقاله | 11,909,110 |
| تعداد دریافت فایل اصل مقاله | 6,961,235 |
ردگیری هدف با مانور شتابدار با روشQDCAC در شبکههای حسگر بیسیم با مقیاس بزرگ | ||
| الکترومغناطیس کاربردی | ||
| مقاله 9، دوره 12، شماره 2 - شماره پیاپی 29، آبان 1403، صفحه 81-90 اصل مقاله (1.47 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| مرتضی سپه وند1؛ علی ناصری* 2؛ میثم رییس دانایی1؛ محمد حسین خانزاده3 | ||
| 1استادیار،دانشگاه جامع امام حسین(ع)، تهران، ایران | ||
| 2استاد،دانشگاه جامع امام حسین(ع)، تهران، ایران | ||
| 3دانشیار،دانشگاه جامع امام حسین(ع)، تهران، ایران | ||
| تاریخ دریافت: 30 خرداد 1403، تاریخ بازنگری: 13 شهریور 1403، تاریخ پذیرش: 16 مهر 1403 | ||
| چکیده | ||
| روشهای ردگیری مبتنی بر الگوریتمهای بیزین در شبکههای حسگر بیسیم با توجه به دقت ردگیری و مقیاسپذیری مناسب از متداولترین روشها میباشند. اما از سوی دیگر این روشها، به علت سربار مخابراتی زیاد دارای کارآمدی لازم در پهنای باند و انرژی نیستند. باتوجهبه محدود بودن منبع انرژی هر گره، در این مقاله روشی ترکیبی به نام QDCAC مبتنی بر خوشهبندی دینامیک و فیلتر ذرهای چند مدی پیشنهاد شده است. در این روش با استفاده از خوشهبندی دینامیک بر اساس باند کرامر - رائو پسین، موقعیت استخراج شده را بهعنوان ورودی فیلتر ردگیر برای تخمین مکان و سرعت هدف مانور دار به کار گرفته و برای تعیین سرگروه بعدی و بیدارسازی گرههای حسگر مؤثر در ردگیری از مکان تخمین زده شده هدف استفاده میکند. مشاهده میشود که در روش پیشنهادی مذکور علیرغم غیرخطیبودن الگوریتم پیمانه سازی مشاهدات و با وجود کاهش دقت نمونههای ارسالی به میزان ۵۰ درصد (۴ بیت) بهمنظور کاهش سربار اطلاعاتی و کاهش توان مصرفی شبکه، میزان دقت مکان یابی در الگوریتم ردگیری در حد بهتر از 1/7متر حفظ میشود که در گستره ۸۰۰۰متر مربعی میدان تست، مقدار مطلوبی است. | ||
| کلیدواژهها | ||
| شبکه حسگر بیسیم؛ ردگیری؛ باند کرامر - رائو پسین؛ پیمانه سازی؛ فیلتر ذرهای چند مدی و QDCAC | ||
| عنوان مقاله [English] | ||
| Target tracking with accelerated maneuver using QDCAC method in large-scale wireless sensor networks | ||
| نویسندگان [English] | ||
| M. Sepahvand1؛ ALI NASERI2؛ Meysam Raeesdanaee1؛ MOHAMMAD HOSSEIN KHANZADEH3 | ||
| 1Assistant Professor, Imam Hossein University, Tehran, Iran | ||
| 2Professor, Imam Hossein University, Tehran, Iran | ||
| 3Associate Professor, Imam Hossein University, Tehran, Iran | ||
| چکیده [English] | ||
| Tracking methods based on Bayesian algorithms are the most common methods in wireless sensor networks due to tracking accuracy and appropriate scalability. But, on the other hand, due to the high telecommunication overhead, these methods do not have the necessary efficiency in terms of bandwidth and energy. Due to the limitation of the energy source of each node, in this article a combined method called QDCAC based on dynamic clustering and multimode particle filter for target tracking is proposed. In this method, using the dynamic clustering based on the posterior Cramer-Rao lower band, the extracted position is used as the input of the tracking filter to estimate the position and speed of the maneuvering target and uses the estimated location of the target to determine the next master node and wake up the sensor nodes effective in tracking. It can be seen that in the proposed method, despite the non-linearity of the observation quantization algorithm and reducing the accuracy of the sent samples by 50% (4 bits) in order to reduce the information overhead and reduce the power consumption of the network, the accuracy level in the tracking algorithm is better than 1.7 meters, which is a desirable value in the 8,000 square meter test field. | ||
| کلیدواژهها [English] | ||
| Wireless Sensor Network, Tracking, Posterior Cramer-Rao Lower Band, Quantization, Multimode Particle filter, QDCAC | ||
| مراجع | ||
|
[1] A. Arora et al., "A line in the sand: a wireless sensor network for target detection, classification, and tracking," Computer Networks, vol. 46, no. 5, pp. 605-634, 2004. [2] M. D. Monfared, E. D. Pahlevanlo, S. G. Babi, and M. Masoori, "A Centralized Algorithm Based on Voronoi Diagram for Hole Detection Problem in Wireless Sensor Networks," Journal of Electronical & Cyber Defence, vol. 5, no. 3, pp. 39-51, 2017. (In persian) [3] Z. Heidary Ghiri and G. Mirjalily, "Energy-Harvesting Aware Multi-Hop Routing in Wireless Sensor Networks for Defense Applications," Electronic and Cyber Defense, vol. 8, no. 4, pp. 63-73, 2020. (In persian) [4] A. Ghaffari and R. Mahmoudi, "Energy-aware routing in wireless sensor networks using MLP and simulated annealing algorithms," Electronic and Cyber Defense, vol. 9, no. 3, pp. 133-142, 2021. (In persian) [5] A. Nadeau, M. Hassanalieragh, G. Sharma, and T. Soyata, "Energy awareness for supercapacitors using Kalman filter state-of-charge tracking," Journal of Power Sources, vol. 296, pp. 383-391, 2015. [6] W. Tang, G. Zhang, J. Zeng, and Y. Yue, "Information weighted consensus-based distributed particle filter for large-scale sparse wireless sensor networks," IET Communications, vol. 8, no. 17, pp. 3113-3121, 2014. [7] X. Hu, Y.-H. Hu, and B. Xu, "Generalised Kalman filter tracking with multiplicative measurement noise in a wireless sensor network," Signal Processing, IET, vol. 8, no. 5, pp. 467-474, 2014. [8] S. Wen, Z. Cai, and X. Hu, "Constrained Extended Kalman Filter for Target Tracking in Directional Sensor Networks," International Journal of Distributed Sensor Networks, vol. 2015, 2015. [9] M. Mansouri, L. Khoukhi, H. Nounou, and M. Nounou, "Secure and robust clustering for quantized target tracking in wireless sensor networks," Journal of Communications and Networks, vol. 15, no. 2, pp. 164-172, 2013. [10] P. Tichavsky, C. H. Muravchik, and A. Nehorai, "Posterior Cramér-Rao bounds for discrete-time nonlinear filtering," IEEE Transactions on signal processing, vol. 46, no. 5, pp. 1386-1396, 1998. [11] R. Olfati-Saber, "Distributed Kalman filtering for sensor networks," in Decision and Control, 2007 46th IEEE Conference on, 2007: IEEE, pp. 5492-5498. [12] X. Wang, M. Fu, and H. Zhang, "Target tracking in wireless sensor networks based on the combination of KF and MLE using distance measurements," Mobile Computing, IEEE Transactions on, vol. 11, no. 4, pp. 567-576, 2012. [13] A. Keshavarz-Mohammadiyan and H. Khaloozadeh, "Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise," International Journal of Systems Science, vol. 48, no. 5, pp. 899-908, 2017. [14] M. Mirsadeghi and A. Mahani, "Energy efficient fast predictor for WSN-based target tracking," annals of telecommunications-annales des télécommunications, vol. 70, no. 1-2, pp. 63-71, 2014. [15] S. Fan, C. Sun, C. Yang, and B. Ye, "Fast distributed Kalman-Consensus filtering algorithm with local feedback regulation," in Information and Automation, 2015 IEEE International Conference on, 2015: IEEE, pp. 2345-2350. [16] P. Chen, H. Ma, S. Gao, and Y. Huang, "Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations," Mathematical Problems in Engineering, vol. 501, p. 981727, 2015. [17] G.-r. Bian, H.-h. Zhang, F.-c. Kong, J.-R. Cao, and H.-Y. Shi, "Research on Warehouse Target Localization and Tracking Based on KF and WSN," Sensors & Transducers (1726-5479), 2014. [18] A. Keshavarz-Mohammadiyan and H. Khaloozadeh, "Interacting multiple model and sensor selection algorithms for manoeuvring target tracking in wireless sensor networks with multiplicative noise," International Journal of Systems Science, pp. 1-10, 2016. [19] X. Yang, W.-A. Zhang, L. Yu, and K. Xing, "Multi-rate distributed fusion estimation for sensor network-based target tracking," IEEE Sensors Journal, vol. 16, no. 5, pp. 1233-1242, 2016. [20] S. Khan, I. Hwang, and J. J. a. p. a. Goppert, "Exploiting Sparsity for Localization of Large-Scale Wireless Sensor Networks," 2023. [21] H. Razavi, H. Amindavar, and H. J. A. H. N. Aghaeinia, "Indoor tracking using auxiliary particle filter and deep learning in wireless sensor networks," p. 103441, 2024. [22] M. Nain and N. J. W. P. C. Goyal, "Energy efficient localization through node mobility and propagation delay prediction in underwater wireless sensor network," vol. 122, no. 3, pp. 2667-2685, 2022. [23] M. Mirsadeghi and A. Mahani, "Energy efficient fast predictor for WSN-based target tracking," annals of telecommunications-annales des télécommunications, vol. 70, no. 1-2, pp. 63-71, 2015. [24] E. B. Mazomenos, J. S. Reeve, and N. M. White, "A range-only tracking algorithm for wireless sensor networks," in Advanced Information Networking and Applications Workshops, 2009. WAINA'09. International Conference on, 2009: IEEE, pp. 775-780. [25] M. Hernandez, T. Kirubarajan, and Y. Bar-Shalom, "Multisensor resource deployment using posterior Cramér-Rao bounds," IEEE Transactions on Aerospace and Electronic Systems, vol. 40, no. 2, pp. 399-416, 2004. [26] M. Sepahvand, A. Naseri, and M. KHANZADEH, "Target Tracking Algorithm in Wireless Sensor Networks with Optimum Power Consumption Using Quantized Observation," 2018. (In persian) [27] L. Xu, X. R. Li, and Z. Duan, "Hybrid grid multiple-model estimation with application to maneuvering target tracking," IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 1, pp. 122-136, 2016. [28] E. J. Msechu, A. Ribeiro, S. I. Roumeliotis, and G. B. Giannakis, "Distributed Kalman filtering based on quantized innovations," in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008: IEEE, pp. 3293-3296. [29] E. Masazade, R. Niu, and P. K. Varshney, "Dynamic bit allocation for object tracking in wireless sensor networks," IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 5048-5063, 2012.
| ||
|
آمار تعداد مشاهده مقاله: 1,175 تعداد دریافت فایل اصل مقاله: 37 |
||