Range Error Correction Research Articles

Removing the Need for Ground Truth UWB Data Collection: Self-Supervised Ranging Error Correction Using Deep Reinforcement Learning

Removing the Need for Ground Truth UWB Data Collection: Self-Supervised Ranging Error Correction Using Deep Reinforcement Learning

Efficient Localization Algorithm With UWB Ranging Error Correction Model Based on Genetic Algorithm-Ant Colony Optimization-Backpropagation Neural Network

Efficient Localization Algorithm With UWB Ranging Error Correction Model Based on Genetic Algorithm-Ant Colony Optimization-Backpropagation Neural Network

Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017

Abstract The Ionospheric Total Electron Content (TEC) measured in the ray path of the signals directly contributes to the Range Error (RE) of the satellite signals, which affects positioning and navigation. Employing the Co-Kriging-based Surrogate Model (COKSM) to predict TEC and RE correction has proven prolific. This research attempted to test and compare the prediction capability of COKSM with an Artificial Intelligence-based Feed Forward Neural Network model (FFNN) during five X-Class Solar Flares of 2021–22. Also, the results are validated by comparing them with the IRI PLAS 2017 model. TEC, solar, and geomagnetic parameters data for Hyderabad GPS station located at 17.31° N latitude and 78.55° E longitude were collected from IONOLAB & OMNIWEB servers. The COKSM uses six days of input data to predict the 7th day TEC, whereas prediction using the FFNN model is done using 45 days of data before the prediction date. The performance evaluation is done using RMSE, NRMSE, Correlation Coefficient, and sMAPE. The average RMSE for COKSM varied from 1.9 to 9.05, for FFNN it varied from 2.72 to 7.69, and for IRI PLAS 2017 it varied from 7.39 to 11.24. Likewise, evaluation done for three different models over five different X-class solar flare events showed that the COKSM performed well during the high-intensity solar flare conditions. On the other hand, the FFNN model performed well during high-resolution input data conditions. Also, it is notable that both models performed better than the IRI PLAS 2017 model and are suitable for navigational applications.

Self-Supervised Deep Location and Ranging Error Correction for UWB Localization

In this paper, we propose a self-supervised deep network that mainly applies location and ranging error corrections to a classical UWB localization approach to improve its accuracy. The core of this method is the self-supervised learning strategy which removes the requirement for ground-truth in the training process, and thus reduces the training cost of the method. To this end, we first use an existing classical UWB localization approach to provide an initial tag location, and then build a deep location and ranging error correction (DLRC) network to jointly estimate the tag position corrections and distance corrections. The self-supervised training strategy is built based on these regressed corrections and the initial tag position as well as the fixed anchor positions and raw ranging measurements, through the topological structure of the UWB sensors. Finally, the initial tag location can be corrected by the trained DLRC network. For this proposed method, it makes use of high-level representations of the UWB measurements by the deep network, while the geometric information important for UWB localization is also considered though the self-training strategy and the classical approach. Therefore, it can effectively improve the localization performance compared to the stat-of-the-art classical approach. This improvement is also verified by conducting real-world experiments.

Cross-Track Error Correction and Evaluation of the Tiangong-2 Interferometric Imaging Radar Altimeter

The Interferometric Imaging Radar Altimeter (InIRA) carried on the Tiangong-2 Space Laboratory can observe three-dimensional sea surface topography over a swath tens of kilometers wide. The InIRA represents a test mission that will provide valuable experience for the design and data processing technology of future wide-swath altimeters. In addition to the tropospheric delay, ionospheric delay, and sea state bias that affect traditional altimeters, InIRA was also affected by the cross-track error derived from roll error, baseline length error, and interference phase error, which had to be properly corrected. In this paper, range error correction based on correction models and cross-track error correction based on the reference topography data method were applied to a 15-day InIRA dataset and evaluated using the Jason-2, Jason-3, Saral/AltiKa, and Sentinel-3A. The results showed that the standard deviation between corrected InIRA and the combined dataset was 7.96 cm after eliminating the influence of tides and dynamic atmospheric correction, which was comparable to nadir altimeter measurement accuracy.

Edge Inference for UWB Ranging Error Correction Using Autoencoders

Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.

Ranging precision for underwater laser proximity pulsed laser target detection

Abstract This paper presents a novel echo signal calculation model for the underwater laser detection of short-range targets. An underwater laser ranging process simulation model is established combined with the echo time identification method. The distance distribution of the underwater target laser range was simulated through time delay and peak detection methods. Simulation results show that the distance dispersion forms an approximately Gaussian distribution. The precision of the system as-assessed by time delay method is higher than the peak detection method. The system ranging accuracy decreases as target distance increases. Seawater simulation results are consistent with experimental underwater short-range goals determined in a target range experiment at sea. The small angle scattering approximation error increases when the target distance is far away — at a certain target distance, the simulation results do slightly deviate from the experimental results. The results presented here may provide a workable reference for the parameter design and ranging error correction of underwater near-range target laser detection systems.

Ranging error correction based on a prior error model

Wireless technology provides accurate positioning in indoor environments using time of arrival (TOA) based ranging techniques. However, the positioning accuracy is degraded due to the ranging errors caused by multipath and non-line-of-sight (NLOS) propagation. In this paper, a ranging error correction method is proposed to improve positioning performance. A TOA ranging error model (TREM) is built to provide the prior information for ranging error correction first. The mean value of TREM within a certain interval is used as the ranging error correction value (RECV). As the RECV may be unreasonable sometimes, we adjust it according to the actual positioning situation and then exploit the final RECV to correct ranging data. The experimental results show that the proposed method could well reduce ranging errors and the positioning performance is obviously improved when using corrected ranging data.

A Technique for Real‐Time Ionospheric Ranging Error Correction Based On Radar Dual‐Frequency Detection

AbstractIonospheric refraction is one of the principal error sources for limiting the accuracy of radar systems for space target detection. High‐accuracy measurement of the ionospheric electron density along the propagation path of radar wave is the most important procedure for the ionospheric refraction correction. Traditionally, the ionospheric model and the ionospheric detection instruments, like ionosonde or GPS receivers, are employed for obtaining the electron density. However, both methods are not capable of satisfying the requirements of correction accuracy for the advanced space target radar system. In this study, we propose a novel technique for ionospheric refraction correction based on radar dual‐frequency detection. Radar target range measurements at two adjacent frequencies are utilized for calculating the electron density integral exactly along the propagation path of the radar wave, which can generate accurate ionospheric range correction. The implementation of radar dual‐frequency detection is validated by a P band radar located in midlatitude China. The experimental results present that the accuracy of this novel technique is more accurate than the traditional ionospheric model correction. The technique proposed in this study is very promising for the high‐accuracy radar detection and tracking of objects in geospace.

Total electron content models and their use in ionosphere monitoring

In Global Navigation Satellite Systems (GNSS) using L band frequencies, the ionosphere causes signal delays that correspond to link‐related range errors of up to 100 m. Whereas this error can be corrected in dual‐frequency measurements by a linear combination of L1 and L2 phases, in single‐frequency measurements, additional information is needed to mitigate the ionospheric error which is proportional to the total electron content (TEC) of the ionosphere. This information can be provided by TEC maps deduced from corresponding GNSS measurements or from model values. Besides direct range error correction in navigation and remote sensing applications, TEC or electron density models play a key role in ionospheric monitoring and forecasting. In this paper we discuss the development and use of TEC models for calibrating TEC, reconstructing reliable TEC maps, and forecasting TEC behavior based on GNSS measurements. European and global TEC maps and corresponding 1 h ahead forecasts are distributed via the operational space weather and ionosphere data service (http://swaciweb.dlr.de) to the international community. The Neustrelitz TEC Model is a basic approach for a family of regional and global TEC models used in different types of applications. The model approximates typical TEC variations depending on the location, time, and level of solar activity with only a few coefficients.

Tropospheric refractive angle and range error corrections utilizing exoatmospheric sources

Tropospheric refractive range and angle errors limit the performance of radar and navigation systems that operate at low elevation angles. For some locations and for many applications, antenna pointing corrections based on surface refractivity alone or, if necessary, a vertical refractivity profile are adequate. However, new systems that operate at very low elevation angles require improved accuracies. More accurate angle error corrections can be obtained by utilizing calibration sources (often referred to as ‘targets of opportunity’), such as the limb of the sun, radio sources, or satellites, the angular positions of which are precisely known. Since the angle error is dependent on the target distance, an expression for angle error as a function of target height is derived. Unfortunately, radio sources cannot be used for range calibration, and since the ranges of most satellites are not known to a sufficient accuracy, the range error correction is still a problem. It is shown in this paper that it is possible to develop a method for obtaining an improved range error correction by utilizing measured angle error data for the same ray path. An expression for the refractive angle error correction for a target based on the measured angle error of the calibration source is first derived; also the angular refraction is expressed as a function of the angle error. It is then shown that the range error correction for a target can be expressed as a function of the angular refraction and can thus be calculated from a set of angle error measurements, thereby providing an improved correction. In the formulation of this problem a time independent spherically stratified nonionized atmosphere with a monotonically decreasing index of refraction with altitude is adopted, so the solution is only valid at frequencies above which ionospheric effects are negligible and for atmospheres which do not have inversion layers in refractivity. Also, it is assumed that both the calibration source and target are above the troposphere.