Improving the performance of a radio-frequency localization system in adverse outdoor applications. - In: EURASIP journal on wireless communications and networking, ISSN 1687-1499, (2021), 123, S. 1-26
In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate's performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.
https://doi.org/10.1186/s13638-021-02001-6
Estimation bounds of beat signal in the R-mode localization system. - In: IEEE access, ISSN 2169-3536, Bd. 9 (2021), S. 69278-69286
The R-Mode system is a terrestrial navigation system currently under development, which exploits existing means of medium frequency radio transmission. The positioning and timing performance depends on the estimation of the signals' phase offset, from which the ranging information is derived. For an analogous problem such as the single-tone phase estimation, the Cramér-Rao bound (CRB) describes the minimal achievable performance in the mean squared error sense. For R-Mode, the problem involves the estimation of the phase offset for a beat signal, which can be described as the difference of phase estimation for the two aiding carriers next to the signal. This estimates are not statistically independent for finite observation, as we show in this paper. The effect becomes stronger for short observation times, which are important for a near real time application. In this contribution, we are interested in phase offset estimation for the signal models relevant to R-Mode: a beat signal and a beat signal combined with an MSK signal. A closed-form lower CRB is proposed for the aforementioned signal models phase estimation, as well as a generalization of the bound for the phase-difference estimation. Based on this derivation, optimized bit sequences are shown to improve performance of the estimates. The validity of the proposal is verified based on a simulation setup. Measurements acquired during a measurement campaign serve to further justify the usefulness of the bound. Some possible applications of such a bound are R-Mode coverage prediction and the associated phase estimators' performance.
https://doi.org/10.1109/ACCESS.2021.3076845
Subsampling approaches for compressed sensing with ultrasound arrays in non-destructive testing. - In: Sensors, ISSN 1424-8220, Bd. 20 (2020), 23, 6734, insges. 23 S.
Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramér–Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.
https://doi.org/10.3390/s20236734
Mitigation of leakage in FMCW radars by background subtraction and whitening. - In: IEEE microwave and wireless components letters, Bd. 30 (2020), 11, S. 1105-1107
Leakage in frequency-modulated continuous-wave (FMCW) radar with a homodyne receiver induces strong signal components in the lower frequency parts of the radar observations. There, the dynamic range of the observations has been reduced, such that close and weak targets are hard to detect. In this letter, a signal processing method is proposed to mitigate the leakage. First, background subtraction is applied to cancel the leakage. As the cancellation is imperfect, a noisy signal portion remains: the leakage noise. A statistical model is developed to describe the leakage noise as a colored noise process. This model is parameterized from measurements and used to whiten the observations. As a result, the dynamic range is improved, and the close targets become better detectable.
https://doi.org/10.1109/LMWC.2020.3023850
Methodology for benchmarking radio-frequency channel sounders through a system model. - In: IEEE transactions on wireless communications, Bd. 19 (2020), 10, S. 6504-6519
Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.
https://doi.org/10.1109/TWC.2020.3003617
Compensation of motion-induced phase errors and enhancement of Doppler unambiguity in TDM-MIMO systems by model-based estimation. - In: IEEE sensors letters, ISSN 2475-1472, Volume 4 (2020), issue 10, 7003504, 4 Seiten
Utilization of multiple input multiple output (MIMO) systems in radar and channel sounding has gained increased attention in recent years. Quite often, time-division multiplexing (TDM) is employed to realize orthogonal waveforms at the transmitter. Apart from its advantages, TDM has two severe drawbacks. First, motion-induced phase variations become indistinguishable from phase migration due to the signal's arrival direction. This is termed angle-Doppler coupling, which causes ambiguities in angle, and Doppler estimation. Second, the unambiguously resolvable Doppler, i.e., the Doppler bandwidth, is reduced. In this letter, a model-based estimation approach will be proposed, which compensates for angle-Doppler coupling, and restores the Doppler bandwidth. A data model for the MIMO observations is derived, which is exploited by a maximum likelihood estimator to infer angle, delay, and Doppler from the observations. The performance of the proposed approach will be testified by simulations.
https://doi.org/10.1109/LSENS.2020.3020700
eadf: representation of far-field antenna responses in Python. - In: SoftwareX, ISSN 2352-7110, Bd. 12 (2020), 100583, S. 1-6
https://doi.org/10.1016/j.softx.2020.100583
Singular spectrum analysis-based algorithm for vitality monitoring using M-sequence UWB sensor. - In: IEEE sensors journal, ISSN 1558-1748, Bd. 20 (2020), 9, S. 4787-4802
https://doi.org/10.1109/JSEN.2019.2962721
Clutter removal of near-field UWB SAR imaging for pipeline penetrating radar. - In: IEEE journal of selected topics in applied earth observations and remote sensing, ISSN 2151-1535, Bd. 13 (2020), S. 1527-1539
https://doi.org/10.1109/JSTARS.2020.2983891
Mitigation of RF impairments of a 160-GHz MMIC FMCW radar using model-based estimation. - In: IEEE transactions on microwave theory and techniques, Bd. 68 (2020), 3, S. 1065-1073
https://doi.org/10.1109/TMTT.2019.2950204