Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength

Wang, Bang; Zhou, Shengliang; Liu, Wenyu; Mo, Yijun

doi:10.1109/tie.2014.2327595

Cited by 181 publications

(92 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rank deficiency of G z leads to ambiguity in sensor measurements, i.e., adding more measurements does not affect the observability conditions, and the system will only remain partially observable with a non-observable mode, as can be seen in the null space basis (9) and (11). For example, if the robot gathers sensor measurements while moving from x 1 = [0, 0, 0] T with a fixed heading, the non-observable modes can be derived as [0, 0, −1, 0] T and [0, 0, 0, −1] T from (11).…”

Section: B Observability Analysis Using Fimmentioning

confidence: 99%

See 1 more Smart Citation

DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis

Lee

Jung

Kim

et al. 2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

In this paper, the observability of the conventional vector field simultaneous localization and mapping (SLAM) is examined by using the Fisher information matrix (FIM). If a mobile robot integrates sensor measurements while moving with a fixed heading, the measurements will be ambiguous because its measurement model is based on bilinear interpolation. To resolve the ambiguity, the authors proposed the novel dual-sensor-based vector-field SLAM (DV-SLAM), which is fully observable by using a mobile robot equipped with two sensors in a specific location to measure vector field signals. By examining its FIM, the condition is derived for the proposed DV-SLAM to be fully observable regardless of how the robot moves. The proposed DV-SLAM is implemented based on the Rao-Blackwellized particle filter with Earth's magnetic field sensors. Simulation and experimental results demonstrate that the proposed dual-sensor-based approach greatly improves the performance of the vector-field SLAM compared with the conventional approach.Index Terms-Dual sensor, Rao-Blackwellized particle filter (RBPF), simultaneous localization and mapping (SLAM), vector-field SLAM.

show abstract

Section: B Observability Analysis Using Fimmentioning

confidence: 99%

“…The use of signal strength to localize mobile nodes or robots has garnered considerable attention because this approach can be implemented with low-priced sensors [11]. In [12], rangeonly SLAM is performed by ranging from the received signal strength (RSS) of wireless sensor nodes, through the inversion of a path-loss model.…”

Section: Introductionmentioning

confidence: 99%

DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis

Lee

Jung

Kim

et al. 2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…[1] proposed RSSI-based indoor positioning method for concrete hydropower station to provide security for workers; [2] proposed a technology for providing indoor positioning of hospital medical supplies; [3] proposed WIFI-based indoor localization using of sub-regional and curve fitting algorithm with positioning accuracy of 2 meters to 2.5 meters; [4] proposed the use of ZigBee indoor localization, compared to its reference indoor localization system based on WIFI, it has 85% reduction in power and 87% on accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Indoor localization algorithms based on ZigBee, Bluetooth [5] [6] and WiFi [3] [7] almost use RSSI technology which needs to add some infrastructure and their accuracy is similar, about 2 meters [3] [4] [5]. Positioning accuracies employing of RFID [8] [9], UWB [10] [11] and ultrasonic [12] [13] are much higher, but they require increasing emission and receiving equipment which also cost much more.…”

Section: Introductionmentioning

confidence: 99%

Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform

Meng¹,

Wang²,

Wang³

et al. 2017

JCC

View full text Add to dashboard Cite

The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smartphone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.

show abstract

“…In terms of study and utilization of outdoor GPS positioning technology, research on the indoor positioning technology started late, and the current indoor positioning focuses on positioned based on WLAN signal strength, utilizing the correlation of the existing indoor Received Signal Strength (RSS) signal and position information to achieve positioning [3][4][5][6]. Currently, RSS-based indoor positioning algorithms mostly adopt the position fingerprint technology [7][8][9][10]. Different from the positioning technology based the signal time of arrival (TOA) and angle of arrival (AOA) [11,12], fingerprint positioning does not require any additional hardware to develop precise time synchronization or angular measurements.…”

Section: Introductionmentioning

confidence: 99%