Non-line-of-sight Identification Method Research Articles

An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method

For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.

GNSS Urban Positioning with Vision-Aided NLOS Identification

The global navigation satellite system (GNSS) has played an important role in a broad range of consumer and industrial applications. In particular, cities have become GNSS major application scenarios; however, GNSS signals suffer from blocking, reflection and attenuation in harsh urban environments, resulting in diverse received signals, e.g., non-line-of-sight (NLOS) and multipath signals. NLOS signals often cause severe deterioration in positioning, navigation, and timing (PNT) solutions, which should be identified and excluded. In this paper, we propose a vision-aided NLOS identification method to augment GNSS urban positioning. A skyward omnidirectional camera is installed on a GNSS antenna to collect omnidirectional images of the sky region. After being rectified, these images are processed for sky region segmentation, which is improved by leveraging gradient information and energy function optimization. Image morphology processing is further employed to smooth slender boundaries. After sky region segmentation, the satellites are projected onto the omnidirectional image, from which NLOS satellites are identified. Finally, the identified NLOS satellites are excluded from GNSS PNT estimation, promoting accuracy and stability. Practical test results show that the proposed sky region segmentation module achieves over 96% accuracy, and that completely accurate NLOS identification is achieved for the experimental images. We validate the performance of our method on public datasets. Compared with the raw measurements without screening, the vision-aided NLOS identification method enables improvements of 60.3%, 12.4% and 63.3% in the E, N, and U directions, respectively, as well as an improvement of 58.5% in 3D accuracy.

Multi-Classification of UWB Signal Propagation Channels Based on One-Dimensional Wavelet Packet Analysis and CNN

Due to the strong penetration ability and high transmission rate of ultra-wideband (UWB) signals, UWB technology plays a very significant role in the field of precise indoor positioning. However, the harsh and volatile indoor environment leads to non-line-of-sight (NLOS) propagation and severe attenuation of UWB signals, which may generate significant ranging and positioning errors. To mitigate NLOS effect and improve positioning accuracy, existing methods use ranging information and channel impulse response (CIR) to identify UWB signal propagation channels. However, these NLOS identification methods often require a priori knowledge and suitable thresholds, and most of them only perform a binary classification between LOS and NLOS in a particular scenario. To address these disadvantages, this paper proposes a novel multi-classification method to classify UWB signal propagation channels based on one-dimensional wavelet packet analysis (ODWPA) and convolutional neural network (CNN). This method first decomposes the CIR of known categories into colored coefficients images using a one-dimensional wavelet packet function, then uses these images to train CNNs with different structures, and finally selects CNN model with the best performance to identify the unknown category of UWB signal propagation channels. The experimental results show that the performance of the proposed method is always the best, regardless of the type of experimental scenario, and the identification accuracy is always higher than 90% and up to 100%.

Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.

Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

Channel Non-Line-of-Sight Identification Based on Convolutional Neural Networks

The distinction between line-of-sight (LOS) and non-line-of-sight (NLOS) channels is important for location awareness related technologies and wireless channel modeling. So far, most of the existing methods identify the LOS and NLOS channels based on the characteristics of radio propagation, e.g., using the Ricean K factor. However, the Ricean K factor is sensitive to the propagation environment, and it is thus difficult to find a proper threshold for NLOS identification. In this letter, we propose a novel NLOS identification method based on the convolutional neural network (CNN). Evaluated by channel measurement data, the proposed algorithm achieves better performance compared with the existing conventional method. Firstly, the CNN network is trained by using the pre-labeled LOS and NLOS data collected from channel measurements. The network parameters are set based on the feedback of training. Then, the method is validated by using different datasets. Compared with the Ricean K factor based identification method, the accuracy of which is 0.86, the proposed method shows higher accuracy of 0.99 for the NLOS channel identification.

Indoor Smartphone Localization Based on LOS and NLOS Identification.

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone. The proposed algorithm uses a trilateration method to estimate the position of the smartphone. The accuracy of the trilateration method depends on the distance estimation error. We first determine whether the propagation path is line-of-sight (LOS) or non-line-of-sight (NLOS), and distance estimation is performed accordingly. This LOS and NLOS identification method decreases the distance estimation error. The proposed algorithm is implemented as a smartphone application. The experimental results show that distance estimation error is significantly reduced, resulting in accurate localization.

A High-Performance Measure for Non-Line-of-Sight Identification in MIMO-OFDM-Based Sensor Networks

This paper proposes a new measure for non-line-of-sight (NLOS) identification in wireless localization systems. The measure is defined based on the space-frequency correlation of multi-input-multi-output orthogonal frequency division multiplexing systems. Here, space correlation refers to the correlation across antenna elements, and frequency correlation refers to the correlation across subcarriers. In this technique, the mean value and standard deviation of space-frequency correlation over multiple transmit and receive antenna combinations are used as the measure for NLOS identification. This method requires minimal variation of spatial correlation across different multipath components. The channel model satisfying this requirement is studied. It is depicted that the proposed technique is applicable to many indoor and outdoor environments in which sensor networks operate. The probability of detection performance of the new NLOS identification method is investigated.

Analysis of Wireless Localization in Nonline-of-Sight Conditions

One major concern of existing wireless localization systems is the identification of nonline-of-sight (NLOS) signal propagation, since NLOS can be considered the dominant source of localization error. Present identification methods usually assume that NLOS could make it not possible to perform localization in a consistent manner. However, the validity of the foregoing assumption has not been properly investigated. This paper presents a theoretical analysis of mobile user localization involving one or more NLOS beacons and shows the given assumption as being invalid when the estimated user location is outside the convex hull of the beacons used in the localization. It also proposes an efficient algorithm for checking whether the estimated location of a mobile user is inside the convex-hull region in both 2-D and 3-D space. Extensive localization experiments on different wireless networks demonstrate that using current NLOS identification methods and classical localization algorithms could yield localization results with grossly underestimated errors.

Novel High Performance MIMO-OFDM Based Measures for NLOS Identification in Time-Varying Frequency and Space Selective Channels

This paper proposes new measures for non-line-of-sight (NLOS) identification in wireless localization systems. The measures are defined based on the space-time-frequency channel correlation features of multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Here, space-time channel correlation refers to the correlation across antenna elements and time, and frequency channel correlation refers to the correlation across subcarriers. The probability-of-detection performance of these NLOS identification methods is investigated. The results represent a high identification performance when the LOS and NLOS powers are in the same order of magnitude.

Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments

In this survey, the currently available ultra-wideband-based non-line-of-sight (NLOS) identification and error mitigation methods are presented. They are classified into several categories and their comparison is presented in two tables: one each for NLOS identification and error mitigation. NLOS identification methods are classified based on range estimates, channel statistics, and the actual maps of the building and environment. NLOS error mitigation methods are categorized based on direct path and statistics-based detection.

Non-line-of-sight identification in ultra-wideband systems based on received signal statistics

Non-line-of-sight (NLOS) propagation can severely degrade the reliability of communication and localisation accuracy in indoor ultra-wideband (UWB) ‘location-aware’ networks. Link adaptation and NLOS bias mitigation techniques have respectively been proposed to alleviate these effects, but implicitly rely on the ability to accurately distinguish between LOS and NLOS propagation scenarios. A statistical NLOS identification technique based on the hypothesis-testing of received signal parameters in UWB propagation channels is discussed. In contrast to narrowband and wideband signals, UWB signals possess higher temporal resolution and robustness to multipath fading. We show that these characteristics result in differences in the statistics of (a) the time-of-arrival (TOA), (b) the received signal strength (RSS) and (c) the root-mean-squared delay spread (RDS) of the received signals, between LOS and NLOS propagation scenarios, which can be exploited for accurate channel identification. We statistically characterise the ability of TOA, RSS and RDS estimates to distinguish between LOS and NLOS propagation based on an extensive indoor measurement campaign. Our measurement results suggest that the RDS of UWB signals can, even in isolation and without complete statistical information, serve as a robust and computationally efficient indicator of the LOS/NLOS nature of propagation. Finally, we demonstrate the efficacy of the discussed NLOS identification method in a location-tracking application based on indoor UWB measurements.