Analysis of Target Detection Based on UWB NLOS Ranging Modeling

Dai, Peng; Yang, Yuan; Zhang, Chenchen; Bao, Xiaoyu; Zhang, Han; Zhang, Yi

doi:10.1109/upinlbs.2018.8559809

Cited by 11 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where m is the number of ranging results after filtering; V is the vector of the correction of ranging results; L is observation error constant matrix; ∆ is an unknown parameter in Equation 15; and A is the cosine coefficient matrix in Equation (19).…”

Section: Single Point Positioningmentioning

confidence: 99%

“…The fine timing measurement (FTM) protocol has been standardized by the Institute of Electrical and Electronics Engineers (IEEE) 802.11-2016 and can provide meter-level positioning accuracy [17] through time of flight (TOF) echo technology [18]. However, just like many other ranging measurements, such as UWB [19,20] and Global Positioning System (GPS) [21,22], one of the major challenges for positioning is the mitigation of NLOS effects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

show abstract

Section: Single Point Positioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…( 2). According to [12,53,54], the ∆𝑑 𝐹𝐶𝑁>0 is formed as shown in equation ( 30 where 𝑒 = 𝐹𝑃𝐸 * 𝑡𝑖𝑚𝑒𝑠𝑡𝑎𝑚𝑝 𝑟𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 [47] and slot is the 2 way round trip transmission time value as obtained in Fig. 1 ; 𝑡𝑜𝑓 ̂, tof and ∆tof represent measured time of flight, true time of flight, and the difference between 𝑡𝑜𝑓 ̂ and tof, respectively.…”

Section: ) Tfp Estimationmentioning

confidence: 99%

UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

et al. 2023

View full text Add to dashboard Cite

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response's (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibilitybased support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms.

show abstract

“…In order to develop a UWB radar [9] system capable of detecting targets in urban areas, one of the main difficulties is related to the complexity of the propagation environment induced by the obstacles present in the scene, and generally, the target is not in the line of sight of the radar, which complicates the task more and more. The recent literature includes several works dealing with this problem [10], [11] and in this [12] and in particular the multipath phenomenon. This algorithm is essentially based on a simple and efficient processing using the correlation principle that allows testing the similarity between the reflected signals and the template to detect existing targets in the radar area with high accuracy and low cost.…”

Section: Introductionmentioning

confidence: 99%

Automatic target detection and localization using ultra-wideband radar

Daghouj

Abdellaoui

Fattah

et al. 2022

IJECE

View full text Add to dashboard Cite

<span>The pulse ultra-wide band (UWB) radar consists of switching of energy of very short duration in an ultra-broadband emission chain, and the UWB signal emitted is an ultrashort pulse, of the order of nanoseconds, without a carrier. These systems can indicate the presence and distances of a distant object, call a target, and determine its size, shape, speed, and trajectory. In this paper, we present a UWB radar system allowing the detection of the presence of a target and its localization in a road environment based on the principle of correlation of the reflected signal with the reference and the determination of its correlation peak.</span>

show abstract

Analysis of Target Detection Based on UWB NLOS Ranging Modeling

Cited by 11 publications

References 12 publications

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

Automatic target detection and localization using ultra-wideband radar

Contact Info

Product

Resources

About