Intelligent indoor localization based on CSI via radio images and deep learning

Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

Section: Quantitative Relation Assignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Zhang,

Xu,

Gao

et al. 2024

“…η > 0 represents the coefficient of the regularization term. We use the back propagation algorithm and minimize the loss function to train the network, so as to update the network parameters [24][25][26]. CBAM-1DCNN is mainly composed of feature extraction module and classification module.…”

Section: Feature Fusionmentioning

confidence: 99%

An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

Zhang,

Wei,

Gao

2023

Self Cite

Determining the number of sources under low signal-to-noise ratio (SNR) and signal interference with the same frequency and modulation presents a significant challenge. To address this challenge, we propose a novel method for detecting the number of signal sources from single-channel that leverages signal reconstruction and deep learning. The method employs subspace projection based on the Hankel matrix to reconstruct the measured single-channel signals, effectively suppressing noise. Furthermore, we incorporate the correlation of information and the integrity of feature in the signal, by fusing the in-phase component, quadrature component, and frequency spectrum feature of the reconstructed complex signal into a one-dimensional feature suitable for convolutional neural network (CNN). To address the source number detection task, we design a one-dimensional CNN based on convolutional block attention module, transforming it into a classification problem. Finally, experimental measurements demonstrate the effectiveness of our proposed method, with an detection accuracy of 94% even at an SNR of −10 dB.

“…A number of fully automated field agricultural robots have been successfully commercialized [6,7], etc. However, due to the limited signal penetration capability and building occlusions, the availability of widely used satellite-based positioning techniques is restricted in protected agriculture scenarios [8].…”

Section: Introductionmentioning

confidence: 99%

Precise visual positioning of agricultural mobile robots with a fiducial marker reprojection approach

Zhang,

Gong,

Sun

et al. 2023

Semi-structured greenhouse environment often features repetitive and weak texture, naturally bringing challenges to high-precision vision-based positioning techniques. This paper proposes a precise visual positioning method for agricultural mobile robots in the greenhouse, which improves their positioning accuracy via discriminatively minimizing fiducial marker reprojection errors. First, fiducial markers are used to enhance environment features, and a markers-based visual positioning task is formulated as a Perspective-n-Point (PnP) problem. The projection constraints of keypoints and the pose constraints of the coordinate systems provide a theoretical basis for robot positioning. Second, a reprojection error minimization approach is proposed by taking into account the markers’ distance and image noise. Far-away markers are more prone to greater observation errors than those close to the robots, the improved PnP algorithm considering distance weighting ensures higher positioning accuracy. Synthetic and field experiments are carried out to evaluate the performance of the proposed method. Synthetic experiments show that the rotation error and translation error of the proposed method are less than 0.7 degrees and 0.5% within a range of 12 m. The MAE and RMSE of field dynamic positioning experiments are 8.57cm and 8.59cm, respectively. Experimental results show that the proposed method is significantly better than traditional methods in dealing with distance-related noise at keypoints.