Low Signal-to-noise Ratio Levels Research Articles

IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)

This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accuracy across various SNR(Signal-to-Noise Ratio) environments. Experimental results demonstrate IncepSeqNet’s outperformance of existing models, particularly at low SNR levels. Furthermore, we have confirmed its applicability in practical 5G systems by using real-world signal data.

P2T2: A physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI.

Estimating T2 relaxation time distributions from multi-echo T2-weighted MRI (T2W) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN) based methods have been proposed to address the complex inverse problem of estimating T2 distributions from MRI data, but they are not yet robust enough for clinical data with low Signal-to-Noise ratio (SNR) and are highly sensitive to distribution shifts such as variations in echo-times (TE) used during acquisition. Consequently, their application is hindered in clinical practice and large-scale multi-institutional trials with heterogeneous acquisition protocols. We propose a physically-primed DNN approach, called P2T2, that incorporates the signal decay forward model in addition to the MRI signal into the DNN architecture to improve the accuracy and robustness of T2 distribution estimation. We evaluated our P2T2 model in comparison to both DNN-based methods and classical methods for T2 distribution estimation using 1D and 2D numerical simulations along with clinical data. Our model improved the baseline model's accuracy for low SNR levels (SNR<80) which are common in the clinical setting. Further, our model achieved a ∼35% improvement in robustness against distribution shifts in the acquisition process compared to previously proposed DNN models. Finally, Our P2T2 model produces the most detailed Myelin-Water fraction maps compared to baseline approaches when applied to real human MRI data. Our P2T2 model offers a reliable and precise means of estimating T2 distributions from MRI data and shows promise for use in large-scale multi-institutional trials with heterogeneous acquisition protocols. Our source code is available at: https://github.com/Hben-atya/P2T2-Robust-T2-estimation.git.

Precursors for synthetic aperture radar

In this paper we study waveform design for synthetic-aperture radar (SAR) imaging through dispersive media. Under the assumptions of scalar wave propagation through a causal dielectric medium and single-scattering from an isotropic point scatterer, we use asymptotic analysis to derive the asymptotic approximation to the scattered electric field. From this asymptotic approximation, we define a sensing precursor that we propose for the transmit waveform for SAR imaging through dispersive material. We compare our sensing precursor with previously defined optimal waveforms (Varslot et al 2011 SIAM J. Appl. Math. 71 1780–800) in terms of both propagation and scattering capabilities, as well as imaging performance. With the filtered back-projection imaging algorithm that we use, we find that for high levels of signal-to-noise ratio (SNR), the optimal waveforms contain higher frequencies and thus produce better images. For low levels of SNR, the transmitted optimal waveforms and the sensing precursors are similar, thus giving comparable images.

Blind Space Time Block Coding Categorization over AF Relaying Broadcasts

Categorization of space time block code (STBC) signals has become widely acknowledged as a crucial foundational mechanism for creating intelligent wireless transmissions in both the governmental and business sectors. The use of multiple antennas at a broadcaster complicates a signal categorization task because assumptions about the number and transmission matrix of the sent antennas must be made. STBC categorization has only been investigated in the context of non-relaying environments, and no methods for relaying transmissions have been reported. This work proposes a revolutionary strategy for categorizing STBC signals that can be implemented in amplify-and-forward (AF) relaying networks. Time-domain characteristics of the STBC waveforms provide the basis of the mathematical ingredients used in the offered categorization process. The employed STBC waveform is reflected in the spikes observed in the fast Fourier transform of the second-order lag product of the collected waveforms. This creates the foundation for an effective discriminating feature. Advantages of the described strategy include not requiring any prior awareness of the modulation type, channel conditions, signal-to-noise ratio (SNR), or the block timing synchronization of the STBC waveforms. The indicated strategy has been shown through simulation experiments to be capable of providing appropriate categorization accuracy despite the existence of transmission faults, even at relatively low SNR levels.

Highly Sensitive Readout Interface for Real-Time Differential Precision Measurements with Impedance Biosensors.

Field deployment is critical to developing numerous sensitive impedance transducers. Precise, cost-effective, and real-time readout units are being sought to interface these sensitive impedance transducers for various clinical or environmental applications. This paper presents a general readout method with a detailed design procedure for interfacing impedance transducers that generate small fractional changes in the impedance characteristics after detection. The emphasis of the design is obtaining a target response resolution considering the accuracy in real-time. An entire readout unit with amplification/filtering and real-time data acquisition and processing using a single microcontroller is proposed. Most important design parameters, such as the signal-to-noise ratio (SNR), common-mode-to-differential conversion, digitization configuration/speed, and the data processing method are discussed here. The studied process can be used as a general guideline to design custom readout units to interface with various developed transducers in the laboratory and verify the performance for field deployment and commercialization. A single frequency readout unit with a target 8-bit resolution to interface differentially placed transducers (e.g., bridge configuration) is designed and implemented. A single MCU is programmed for real-time data acquisition and sine fitting. The 8-bit resolution is achieved even at low SNR levels of roughly 7 dB by setting the component values and fitting algorithm parameters with the given methods.

Second-Order Statistics for STBC Classification Over Amplify-and-Forward Cooperative Systems

The space-time block code (STBC) classification has recently been recognized as a prominent fundamental tool for the development of smart communication systems, which are applied in both the defense and private sectors. The use of multiple antennas at the transmitter introduces a new dimension of difficulty to the signal classification task since it is essential to make judgments about the number and arrangement of broadcast antennas. The reported research has only addressed STBC classification over non-cooperative environments, with no solutions offered for cooperative transmissions. A comprehensive STBC classification algorithm for amplify-and-forward cooperative transmissions is proposed in this investigation. The classification procedure is predicated on analytical formulas established for the time-domain features of the STBC signals. There are two phases involved in implementing the offered algorithm into practice. The first stage includes estimating the correlation functions of the incoming signals across two different time periods using second-order statistics, and a false alarm oriented assessment is utilized in the second stage to produce a judgment. The proposed algorithm’s many benefits involve not necessitating any previous information about the modulation form, channel characteristics, signal-to-noise ratio (SNR), or the start of STBC blocks. Simulation experiments demonstrate that the proposed algorithm can give adequate classification performance in the presence of transmission imperfections even at relatively low SNR levels.

Suppressing the Saturated Negative Effects to Recover the Effective Sensing FID Signal From an Overhauser Magnetometer via Segmented Linear Regression

The free induction decay (FID) signal of Overhauser geomagnetic sensors may suffer from saturation distortion in the mobile magnetic survey, which results in the loss of signal envelope information. Aiming to suppress the negative effects of saturation, this article proposes a recovery method for the FID signal from saturation based on segmented linear regression. The influences of the signal-to-noise ratio (SNR) and the saturation level on the recovery effect of the proposed method were investigated. Numerical simulations show that the proposed method has a good recovery performance under low SNR and high saturation levels. Furthermore, we set up an Overhauser geomagnetic sensor experimental test platform and conducted extensive experiments. The experimental results show that when the FID signal was partially saturated with an SNR of 20.47 dB, the initial amplitude and the relaxation time recovery error were reduced by a factor of 7.30 and 6.76, respectively. Even if the FID signal is fully saturated, the proposed method can still obtain the initial amplitude and the relaxation time accurately.

A decrease in physiological arousal accompanied by stable behavioral performance reflects task habituation.

Despite the evidence of a positive relationship between task demands and listening effort, the Framework for Understanding Effortful Listening (FUEL) highlights the important role of arousal on an individual’s choice to engage in challenging listening tasks. Previous studies have interpreted physiological responses in conjunction with behavioral responses as markers of task engagement. The aim of the current study was to investigate the effect of potential changes in physiological arousal, indexed by the pupil baseline, on task engagement over the course of an auditory recall test. Furthermore, the aim was to investigate whether working memory (WM) capacity and the signal-to-noise ratio (SNR) at which the test was conducted had an effect on changes in arousal. Twenty-one adult hearing aid users with mild to moderately severe symmetrical sensorineural hearing loss were included. The pupil baseline was measured during the Sentence-final Word Identification and Recall (SWIR) test, which was administered in a background noise composed of sixteen talkers. The Reading Span (RS) test was used as a measure of WM capacity. The findings showed that the pupil baseline decreased over the course of the SWIR test. However, recall performance remained stable, indicating that the participants maintained the necessary engagement level required to perform the task. These findings were interpreted as a decline in arousal as a result of task habituation. There was no effect of WM capacity or individual SNR level on the change in pupil baseline over time. A significant interaction was found between WM capacity and SNR level on the overall mean pupil baseline. Individuals with higher WM capacity exhibited an overall larger mean pupil baseline at low SNR levels compared to individuals with poorer WM capacity. This may be related to the ability of individuals with higher WM capacity to perform better than individual with poorer WM capacity in challenging listening conditions.

Overlap Sliding Window Algorithm for Better BER in Turbo Decoding

With the proliferation of smartphones and exchange of voluminous data through them, it becomes essential for service providers to deliver quality multimedia through the network even at low signal-to-noise ratio (SNR) situations for extended battery life. For meeting this requirement, the stress is on finding attractive coding schemes that can deliver high data rates with good bit error rate (BER) at low SNR levels. Turbo codes were the mainstay up to 3G networks. Currently, LDPC codes are being recognized as the codes that can deliver the data rates required for 4G and beyond. Sliding window algorithm addressed the problem of high latency and the requirement of a large number of devices with MAP algorithm to a greater extent, and it is in use for turbo decoding. But it suffers degraded BER, especially at low SNR levels. The proposed overlap sliding window (OLSW) algorithm can offer better BER with expected marginal degradation in terms of device requirements, keeping the same latency.

Filtered Multicarrier Waveforms Classification: A Deep Learning-Based Approach

Automatic signal recognition (ASR) plays an important role in various applications such as dynamic spectrum access and cognitive radio, hence it will be a key enabler for beyond 5G communications. Recently, many research works have been exploring deep learning (DL) based ASR, where it has been shown that simple convolutional neural networks (CNN) can outperform expert features based techniques. However, such works have been primarily focusing on single-carrier signals. With the advent of spectrally efficient filtered multicarrier waveforms, we propose in this paper, to revisit the DL based ASR to account for the variety and complexity of these new transmission schemes. Specifically, we design two types of classification algorithms. The first one relies on the cyclostationarity characteristics of the investigated waveforms combined with a support vector machine (SVM) classifier; while the second one explores the use of a four-layer CNN which performs both features extraction and classification. The proposed approaches do not require any a priori knowledge of the received signal parameters, and their performance is evaluated in a multipath channel through simulations for a signal-to-noise ratio (SNR) ranging from −8 to 20 dB. The simulation results show that, despite cyclostationary characteristics being highly discriminative, the CNN outperforms the cyclostationary based classification especially for short time received signals, and low SNR levels.

Single Acoustic Sensor-Based Time–Frequency Spectrum Sensing Approach for Land Vehicle Detection

This paper introduces a time-frequency spectrum sensing (TFSS) approach based on the short-time Fourier transform (STFT) for vehicle detection using a single acoustic sensor. Spectrum sensing on time-frequency plane offers various applications including vehicle detection, tracking, and classification. The proposed technique estimates the threshold from the power spectral density (PSD) distribution of white Gaussian noise (WGN) which is fitted with Chi-square distribution. This threshold is further used to extract the dominating frequency harmonics and detect the presence of a vehicle from the PSD of the signal on the spectrogram plane. Various performance measures are computed on experimentally generated datasets of the moving vehicles for evaluation of the classical amplitude-based energy detector (ED) and proposed algorithm. The main challenging issue for vehicle monitoring system is, to detect the moving vehicle with a minimum probability of false alarm rate at a low signal-to-noise ratio (SNR). The TFSS can detect the signal at a low SNR level up to −11dB with reduced false alarm rate by 50% as compared to ED. It also provides satisfactory performance measures and receiver operating characteristic curve.

Robust and Fast Temperature Extraction for Brillouin Optical Time-Domain Analyzer by Using Denoising Autoencoder-Based Deep Neural Networks

A method of robust and fast temperature extraction for Brillouin optical time-domain analyzer (BOTDA) sensing systems using the denoising autoencoder (DAE) based deep neural networks (DNN) is demonstrated. After appropriate training, the DAE suppresses the noise on the measured Brillouin gain spectrum (BGS), and improves the signal-to-noise ratio (SNR) by 9.96dB in our experiment. To extract temperature, the DAE as a basic block is stacked to form the DNN model. Since the DNN model is based on DAE, both denoising and fast temperature extraction can be simultaneously finished using only one DNN model. Thus, it is more robust and faster than the conventional Lorentzian curve fitting (LCF) method, especially when the input data has low SNR level. In the case of 4.6dB SNR, the standard deviation (SD) of the measured temperature at the end of 40km fiber under test (FUT) is reduced from 2.4°C to 1.2°C by using DAE based DNN when compared with that using the LCF method, and the corresponding root-mean-square error (RMSE) is reduced from 2.4°C to 1.3°C. Moreover, since the temperature information can be extracted directly from the experimental BGS data without the need of time-consuming curve fitting and subsequent conversion from Brillouin frequency shift (BFS) to temperature, the speed of temperature extraction using the DAE based DNN is faster by 500 times than that using LCF. Combining the advantages of both denoising and fast processing speed, the DAE based DNN would be a practical way of temperature extraction for the BOTDA systems.

A Multi-Target SNR-Progressive Learning Approach to Regression Based Speech Enhancement

We propose a multi-target, signal-to-noise-ratio (SNR)-progressive learning (SNR-PL) framework for regression based speech enhancement (SE). At low SNR levels, it is often not easy to directly learn the complicated regression required in SE. We therefore decompose the original SE problem of mapping noisy to clean speech features, with a large SNR gap, into a series of sub-problems, each with a small SNR increment and presumably easier to learn. In our configurations, each hidden layer of the proposed regression neural network is guided to explicitly learn an intermediate target with a specified but small SNR gain. Tested on both deep neural network (DNN) and long short-term memory (LSTM) architectures, SNR-PL consistently outperforms the conventional “black box” DNN framework in terms of both objective measure superiority and network model compactness. Furthermore, with the best configured LSTM-based SNR-PL model, we often observe that the performance is easily saturated or even degraded when increasing the number of intermediate targets, due to the fact that useful information is lost in dimension reduction when involving more target layers. Accordingly, to address this information loss issue, we explore densely connected networks on top of the LSTM structure where the input and the preceding intermediate targets are concatenated together to learn the next target. Finally, to fully utilize the rich and complementary information of intermediate targets, a simple post-processing strategy is adopted to further improve the performance. Evaluated on the simulation speech data, experimental results in unseen noises cases demonstrate that the proposed approach consistently performs better than the conventional LSTM approach in terms of objective speech enhancement measures for speech intelligibility and quality. Furthermore, when evaluated on real data provided by the CHiME-4 Challenge for automatic speech recognition (ASR) of noisy microphone array speech, we show that the proposed approach with intermediate outputs can directly improve the ASR performance, while the conventional LSTM approach increases the word error rate.

Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing

In this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (-4 to -18 dB). It has observed that the classification accuracy at high/moderate (-4 to -8 dB) and low (-8 to -18 dB) SNR levels are 79.5% and 75.2%, respectively. To the best of authors' knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems.

1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

In this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (−40dB to +20dB). The traditional electronic support measure (ESM) systems require high SNR for radar signal classification. LPI (low probability of intercept) radar signals that are received by ESM systems are usually corrupted by noise. So, we demonstrate through extensive simulations that it is possible to achieve high classification performance at low SNR levels providing that the underlying radar signals are known in advance. MF bank classifier, 1D Convolutional Neural Networks (CNNs) and the minimum distance classifier using spectral-domain features (the skewness, the kurtosis, and the energy of the dominant frequency) have been derived for the radar signal classification and their performances have been compared with each other and with the optimal classifier.

Performance Comparison of Closed-Form Least Squares Algorithms for Hyperbolic 3-D Positioning

An accurate 3-D wireless local positioning system (LPS) is a highly demanded tool for increasing safety in, e.g., emergency response and security operations. An LPS is an attractive approach that can meet stringent requirements and can achieve acceptable accuracies for a long time during extended operations in global navigation satellite system (GNSS)-denied environments. In this work, three closed-form (CF) least squares (LS) algorithms were considered, where two of them were adapted to exploit the knowledge about nuisance parameters for accurate 3-D positioning based on time difference of arrival (TDoA) measurements. The algorithms utilize the single set (SS) of the TDoA measurements, an extended SS (ExSS) of the TDoA measurements, or the full set (FS) of the TDoA measurements, and were denoted, respectively, as the CFSSLS, CFExSSLS, and CFFSLS solutions. The performance of the algorithms was investigated with simulations and real-world measurements, where the wireless system transmitters were placed in a quasi-coplanar arrangement. At moderate to high signal-to-noise ratio (SNR) levels, the CFSSLS solution has the best performance, followed by the CFExSSLS solution and then by the CFFSLS solution. At low SNR levels, the CFFSLS algorithm outperformed the other two algorithms. Both the CFSSLS and CFFSLS solutions estimate nuisance parameters that are utilized in refining the vertical position estimate of the receiver. The CFFSLS solution delivers more accurate refined vertical position estimates since it utilizes more nuisance parameters, i.e., more information. The experimental results confirmed the simulation study in which the CFFSLS algorithm outperformed the other two algorithms, where the experimental environment was dominated by total non-line-of-sight (NLoS) conditions and low SNR levels at the receiver to be located. Therefore, it is recommended to use the FS TDoA measurements for 3-D positioning in bad signal conditions, such as high noise levels and NLoS propagation.

Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments

Human speech processing is inherently multi-modal, where visual cues (e.g. lip movements) can help better understand speech in noise. Our recent work [1] has shown that lip-reading driven, audio-visual (AV) speech enhancement can significantly outperform benchmark audio-only approaches at low signal-to-noise ratios (SNRs). However, consistent with our cognitive hypothesis, visual cues were found to be relatively less effective for speech enhancement at high SNRs, or low levels of background noise, where audio-only (A-only) cues worked adequately. Therefore, a more cognitively-inspired, context-aware AV approach is required, that contextually utilises both visual and noisy audio features, and thus more effectively accounts for different noisy conditions. In this paper, we introduce a novel context-aware AV speech enhancement framework that contextually exploits AV cues with respect to different operating conditions, in order to estimate clean audio, without requiring any prior SNR estimation. In particular, an AV switching module is developed by integrating a convolutional neural network (CNN) and long-short-term memory (LSTM) network, that learns to contextually switch between visualonly (V-only), A-only and both AV cues at low, high and moderate SNR levels, respectively. For testing, the estimated clean audio features are utilised using an innovative, enhanced visually-derived Wiener filter (EVWF) for noisy speech filtering. The context-aware AV speech enhancement framework is evaluated in dynamic real-world scenarios (including cafe, street, bus, and pedestrians) at different SNR levels (ranging from low to high SNRs), using benchmark Grid and ChiME3 corpora. For objective testing, perceptual evaluation of speech quality (PESQ) is used to evaluate the quality of the restored speech. For subjective testing, the standard mean-opinion-score (MOS) method is used. Comparative experimental results show the superior performance of our proposed context-aware AV approach, over A-only, V-only, spectral subtraction (SS), and log-minimum mean square error (LMMSE) based speech enhancement methods, at both low and high SNRs. The preliminary findings demonstrate the capability of our novel approach to deal with spectro-temporal variations in real-world noisy environments, by contextually exploiting the complementary strengths of audio and visual cues. In conclusion, our contextual deep learning-driven AV framework is posited as a benchmark resource for the multi-modal speech processing and machine learning communities.

Noise Robust Voice Activity Detection Based on Multi-Layer Feed-Forward Neural Network

DOI: 10.26650/electrica.2019.18042 This paper proposes a voice activity detection (VAD) method based on time and spectral domain features using multi-layer feed-forward neural network (MLF-NN) for various noisy conditions. In the proposed method, time features that were short-time energy and zero-crossing rate and spectral features that were entropy, centroid, roll-off, and flux of speech signals were extracted. Clean speech signals were used in training MLF-NN and the network was tested for noisy speech at various noisy conditions. The proposed VAD method was evaluated for six kinds of noises which are white, car, babble, airport, street, and train at four different signal-to-noise ratio (SNR) levels. The proposed method was tested on core TIMIT database and its performance was compared with SOHN, G.729B and Long-Term Spectral Flatness (LSFM) VAD methods in point of correct speech rate, false alarm rate, and overall accuracy rate. Extensive simulation results show that the proposed method gives the most successful average correct speech rate, false alarm rate, and overall accuracy rate in most low and high SNR level conditions for different noise environments. Cite this article as : Arslan O, Engin EZ. Noise Robust Voice Activity Detection Based on Multi-Layer Feed-Forward Neural Network. Electrica 2019; 10.26650/electrica.2019.18042.

Maximum Likelihood Joint Angle and Delay Estimation from Multipath and Multicarrier Transmissions with Application to Indoor Localization over IEEE 802.11ac Radio

In this paper, we tackle the problem of joint angle and delays estimation (JADE) of multiple reflections of a known signal impinging on multiple receiving antennae. Based on the importance sampling (IS) concept, we propose a new non-iterative maximum likelihood (ML) estimator that enjoys guaranteed global optimality and enhanced high-resolution capabilities for both single- and multi-carrier models. The new ML approach succeeds in transforming the original multi-dimensional optimization problem into multiple two-dimensional ones thereby resulting in huge computational savings. Moreover, it does not suffer from the off-grid problems that are inherent to most existing JADE techniques. By exploiting the sparsity feature of a carefully designed pseudo-pdf that is intrinsic to the new estimator, we also propose a novel approach that enables the accurate detection of the unknown number of paths over a wide range of practical signal-to-noise ratios (SNRs). Computer simulations show the distinct advantage of the new ML estimator over state-of-the art JADE techniques both in the single- and multi-carrier scenarios. Most remarkably, they suggest that the proposed IS-based ML JADE is statistically efficient as it almost reaches the Camer-Rao lower bound (CRLB) even in the adverse conditions of low SNR levels. Using real-world channel measurements collected from four access points (APs) with IEEE 802.11ac standard’s setup parameters in an indoor environment, we also show that the proposed ML estimator achieves a localization performance below 15 cm accuracy.

Error compensation in indoor positioning systems based on software defined visible light communication

Visible Light Communication (VLC) paradigm allows the reusing of existing illuminating infrastructures in order to provide data communication. VLC can be considered a promising technology also for positioning in indoor environments, due to its potentially high accuracy and low costs. However, the main obstacle to the use of VLC for localization purposes is the high level of environmental noises, mainly due to sunlight. A novel approach, for easily measuring environmental noises and compensating their effects on localization results performed by an Indoor Positioning System (IPS) based on VLC, is proposed in this work. Frequency Division Multiplexing (FDM) is adopted to divide the total bandwidth into a series of non-overlapping frequency sub-bands corresponding to each signal, while an estimation of Signal to Noise Ratio (SNR), obtained through real time Power Spectral Density measurements in the proper frequency ranges, is exploited to compensate the error in positioning due to sunlight and other wide-band external optical disturbing signals. The proposed approach has been validated through experimental tests, carried out using a simple deployment of low power lamps, low cost hardware and a software defined approach. In the region under test, receiver position has been experimentally detected with higher accuracy in comparison to classical FDM approach, confirming the correctness and effectiveness of our proposed technique. In order to further validate the proposed approach, an additive measurement campaign has been successfully carried out considering a scenario characterized by very low SNR levels.