Distributed multi‐object localisation by consensus on compressive sampling received signal strength fingerprints

Wang, Dongli; Zhou, Yan; Wei, Yanhua; Pei, Tingrui

doi:10.1049/iet-com.2014.1155

Cited by 9 publications

(7 citation statements)

References 54 publications

(94 reference statements)

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“…where k = 1, 2, 3, 4 and i = 1, 2, 3. Those three weights then can be used in (6) to estimate the optimal position of the test point.…”

Section: Hierarchical Model Of Ahpmentioning

confidence: 99%

“…Pseudocode 1: The pseudocode of the proposed algorithm. 6 Wireless Communications and Mobile Computing value. b is the forgetting factor in d t = ð1 − bÞ/ð1 − b t+1 Þ. b is used to lower the influence that the output at the prior moment (old data) has on the input data (new data) which is needed for new iteration at moment t. As the number of iterations increases, the estimation error's covariance matrix will become smaller and closer to the zero matrix.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…The differences between the assorted k Euclidean distances are not big enough. The weights assigned to the k RPs in the existing papers [5][6][7][8][9] are thereby almost the same, and the weighted sum of the location coordinates is almost equal to the average of the location coordinates. Our simulation results show that this method leads to inaccurate location results.…”

Section: Introductionmentioning

confidence: 99%

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Wireless Localization Method Based on AHP‐WKNN and Amendatory AKF

Hua

Pan

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we propose an AHP-WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K -nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP-WKNN method and the modified AKF can improve positioning accuracy effectively.

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“…where k = 1, 2, 3, 4 and i = 1, 2, 3. Those three weights then can be used in (6) to estimate the optimal position of the test point.…”

Section: Hierarchical Model Of Ahpmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wireless Localization Method Based on AHP‐WKNN and Amendatory AKF

Hua

Pan

et al. 2021

Wireless Communications and Mobile Computing

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“…Blind nodes are sensor nodes with unknown positions. To estimate distance, blind nodes use radio channel information, such as the received signal strength indicator (RSSI) [6,7], time of arrival (ToA) [8,9], time difference of arrival (TDoA) [10,11], angle of arrival (AoA) [12], or some combination of these methods. The RSSI-based distance estimation is attractive because of its low cost, long range, and simplicity.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Localization Algorithm with a Novel Error Control Mechanism for Wireless Sensor Networks with Few Anchor Nodes

Sun

Wang

et al. 2018

Journal of Sensors

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A novel iterative localization algorithm with high accuracy and low anchor node dependency for large-scale wireless sensor networks is proposed in this paper. At each iteration, blind nodes are located using a weighted linear least squares-based algorithm. To prevent errors in the blind nodes from propagating and accumulating throughout the network, an anchor geometric feature-based error control mechanism is used to select the nodes that participate in the localization and to estimate the localization confidence. The simulation results show that the algorithm can be used when only a few anchor nodes are involved. This algorithm is more advanced than traditional methods, which often require a large number of well-placed anchor nodes to operate appropriately. By optimizing the decision parameter v of the algorithm, the average localization error of the algorithm is approximately 0.43 meters. When the ratio of anchor nodes (the ratio of the number of anchor nodes to the number of sensor nodes in the network) is 1.25% (i.e., 5 anchor nodes for 400 sensor nodes), the received signal strength indicator (RSSI) variance is 8 dBm, and the radio range is 50 meters. A comparison of the proposed algorithm with global localization methods, including multidimensional scaling (MDS), semidefinite programming (SDP), and shortest-path access (SPA), shows that the proposed algorithm achieves higher location accuracy and stability when the number of anchor nodes is varied. The efficiency of the proposed localization algorithm is evaluated in a real sensor network, and the accuracy is high and robust to radio channel variance.

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“…the agent node). Such range-based localisation techniques can be categorised into the time-of-arrival (TOA) [3][4][5], timedifference-of-arrival [6,7], angle-of-arrival [8], received signal strength (RSS) [9,10], and some hybrid schemes [11][12][13]. No matter which range measurement method is adopted, the power of transmitting signal plays a critical role in the accuracy of position estimate.…”

Section: Introductionmentioning

confidence: 99%