Presence Of Strong Interference Research Articles

Identification of emitting times based on improved CRNN model

Abstract The number of times that equipment can emit is limited, and exceeding the usage limit will affect its shooting accuracy. Existing research mainly focuses on the classification and detection of emitting times, with few studies focusing on the recognition of emitting times. This paper combines multi-scale feature extraction with the CRNN model and proposes an improved CRNN emission frequency recognition model. After experimental verification, the improved model has a high prediction accuracy on high-quality emitted sound data, with an accuracy of over 95.2%. The accuracy of the data in the presence of strong interference is 88.6%. The experimental results show that the method proposed in this paper is effective.

Fault Diagnosis of Train Wheelset Bearing Roadside Acoustics Considering Sparse Operation with GA-RBF

Trackside acoustic signals are useful for non-contact measurements as well as early warnings in the diagnosis of train wheelset bearing faults. However, there are two important problems when using roadside acoustic signals to diagnose wheel-to-wheel bearing faults; one is the presence of strong interference from strong noise and high harmonics in the signal, and the other is the low efficiency of bearing fault identification caused by it. Therefore, from the viewpoint of solving the two problems, a sparse operation method is proposed for denoising and detuning the modulation of the roadside acoustic signal, and a machine learning classifier with a Genetic Algorithm (GA)-optimized Radial Basis Neural Network (RBFNN) is proposed to improve the rate at which the features of roadside acoustic signal faults are recognized. Firstly, the background noise is filtered out from the Doppler-corrected acoustic signal using the Sparse Representation method, and the inverse wavelet transform is reconstructed into a noiseless signal. Secondly, the interference high-harmonic signal in the signal is filtered out using the Resonant Sparse Signal Decomposition (RSSD) method. Then, the GA is selected to optimize the parameters of the RBF neural network and build a fault diagnosis model. Finally, the extracted acoustic signal feature set is trained on the network model, and the trained model is used for testing. In summary, the sparse operation on the roadside acoustic signal processing and the GA-RBFNN diagnosis model were verified as being very effective in the diagnosis of roadside acoustic train wheel pair faults through the simulation experiment.

An efficient sparse method for direction-of-arrival estimation in the presence of strong interference.

Target direction-of-arrival (DOA) estimation is often difficult in the presence of strong interference-especially when the target DOA is very close to the interference DOA-since the strong interference signal can mask the weak target signal and make the DOA estimation hard. To address this problem, an efficient sparse method for DOA estimation is proposed in this paper, in which the effect of strong interference on the target DOA estimation is significantly reduced. An on-grid version of the grid evolution technique is then developed to nonuniformly refine the grid, thereby reducing the computational complexity while retaining reasonable accuracy. Numerical simulations and experimental results demonstrate that, compared to state-of-art methods, the proposed method achieves higher efficiency and better DOA estimation performance in the presence of strong interference.

Passive distributed arrays cross-location algorithm with windowed deconvolution

To eliminate the appearance of false peaks in underwater passive distributed cross-location, a cross-location algorithm with windowed deconvolution in the spatial domain is proposed. The algorithm can effectively detect and locate passive targets and effectively eliminate passive cross-location false peaks. First, the windowed deconvolution algorithm is applied in an integral form to individual narrow sub-band spatial spectra. The spatial spectra are summed to obtain the broadband spatial energy spectrum. The estimated propagation loss and the one-dimensional broadband energy spectrum are used to deduce the sound source level of the two-dimensional scanning grid points. In the proposed method, the mean value and standard deviation of the source level are inversely deduced using multiple arrays, and the standard deviation is used to weight the fusion result for each scan point to weaken the energy output corresponding to the false position. Simulation results show that the algorithm can effectively detect and locate targets in the presence of strong interference. The crossover result for the energy line shows that ambiguity to the left and right side of the line array is overcome.

Sparse Bayesian learning for wideband direction-of-arrival estimation via beamformer power outputs in a strong interference environment.

Sparse Bayesian learning (SBL) offers a useful tool for wideband direction-of-arrival (DOA) estimation, but its performance is limited in the presence of strong interferences. To solve this problem, this letter attempts to extend the SBL to estimate DOAs via the beamformer power outputs (BPO) because the beamformer can efficiently suppress the interferences. A Bayesian probabilistic model effective for the BPO is proposed. Based on this, a BPO-based SBL method is put forward by adopting the variational Bayesian inference to estimate the DOAs from the BPO. Simulation and experimental results confirm the good performance of the proposed method.

Co-Design of Consensus-Based Approach and Reliable Communication Protocol for Vehicular Platoon Control

Automated and coordinated vehicle platoon driving is challenging as the complexity of vehicle control and the presence of unreliable wireless inter-vehicle communication. This subject has an interdisciplinary nature, involving control theories, vehicle dynamics, and communication. Due to the constant interaction between control logic and communication topology, a co-design approach for the controller and for reliable communication protocol becomes necessary. This paper describes the AddP-CACC - a consensus-based solution to vehicle platoons. The proposed solution turns the communication topology into a design parameter that dynamically reconfigures the controller. In addition, the controller automatically compensates for outdated information caused by network losses and delays. AddP-CACC was implemented in the PLEXE tool and was compared with other related works. Simulation results have shown the robustness and performance of the proposed solution in various scenarios that would include realistic propagation conditions with interference caused by other vehicles. The results have also evidenced the ability of the proposed solution to maintain a stable vehicular platoon by using different communication topologies, different vehicle flows, even in the presence of strong interferences, delays, and fading conditions.

BlinkListener

Eye blink detection plays a key role in many real-life applications such as Human-Computer Interaction (HCI), drowsy driving prevention and eye disease detection. Although traditional camera-based techniques are promising, multiple issues hinder their wide adoption including the privacy concern, strict lighting condition and line-of-sight (LoS) requirements. On the other hand, wireless sensing without a need for dedicated sensors gains a tremendous amount of attention in recent years. Among the wireless signals utilized for sensing, acoustic signals show a unique potential for fine-grained sensing owing to their low propagation speed in the air. Another trend favoring acoustic sensing is the wide availability of speakers and microphones in commodity devices. Promising progress has been achieved in fine-grained human motion sensing such as breathing using acoustic signals. However, it is still very challenging to employ acoustic signals for eye blink detection due to the unique characteristics of eye blink (i.e., subtle, sparse and aperiodic) and severe interference (i.e., from the human target himself and surrounding objects). We find that even the very subtle involuntary head movement induced by breathing can severely interfere with eye blink detection. In this work, for the first time, we propose a system called BlinkListener to sense the subtle eye blink motion using acoustic signals in a contact-free manner. We first quantitatively model the relationship between signal variation and the subtle movements caused by eye blink and interference. Then, we propose a novel method that exploits the "harmful" interference to maximize the subtle signal variation induced by eye blinks. We implement BlinkListener on both a research-purpose platform (Bela) and a commodity smartphone (iPhone 5c). Experiment results show that BlinkListener can achieve robust performance with a median detection accuracy of 95%. Our system can achieve high accuracies when the smartphone is held in hand, the target wears glasses/sunglasses and in the presence of strong interference with people moving around.

Underwater source localization in the presence of strong interference

The underwater localization of a broadband target in the presence of strong interference and noise has been widely investigated. A novel clutter suppression approach based on oblique projections is proposed, exploiting the prior information of the expected target response. The method uses a generalised likelihood ratio formulation to select the oblique projection best matching the measured data. The performance of the proposed method is verified through numerical examples and measured sonar data. The results indicate that the method provides good performance for time difference of arrival estimation and target localization under low signal to clutter ratio as compared to existing methods.

Double‐adaptive chirplet transform for radar signature extraction

This study presents a novel method for time-frequency (TF) signal analysis and chirp rate estimation dedicated for electromagnetic spectrum sensing, electronic warfare, electronic intelligence and/or passive bistatic radar purposes in which frequency modulated signals occur. The approach is based on the double-adaptive chirplet transform, providing optimal analysis window parameters, which is a crucial problem during TF processing. The presented methodology is based on a Gaussian window with two degrees of freedom, which results in a strong concentration of energy on the TF plane around the main component, even if the initial window parameters were mismatched. As an example of the method's usefulness, different types of real-life radar pulses were processed: firstly, two types of non-linear frequency-modulated waveforms were examined as a suitable illustration of the changeability of the analysis window parameters. Secondly, the linear frequency modulated signals were analysed in the presence of strong interference and multipath propagation. The waveforms were processed and compared, creating individual radar signatures, which may allow transmitter classification and signal reconstruction to be carried out in further processing. Moreover, the estimation limitations were compared to the Cramer-Rao lower bound, and an appendix organising mathematical fundamentals for the analysed methods is provided.

A Practical Downlink NOMA Scheme for Wireless LANs

Non-orthogonal multiple access (NOMA) has emerged as a new multiple access paradigm for wireless networks. Although many results have been produced for NOMA, most of them are limited to theoretical exploration and performance analysis in cellular networks. Very limited progress has been made so far in the design of practical NOMA schemes for wireless local area networks (WLANs). In this paper, we propose a practical downlink NOMA scheme for WLANs and evaluate its performance in real-world wireless environments. Our NOMA scheme has three key components: precoder design, user grouping, and successive interference cancellation (SIC). On the transmitter side, we first formulate the precoding design problem as an optimization problem and then devise an efficient algorithm to construct precoders for downlink NOMA transmissions. We further propose a lightweight user grouping algorithm to ensure the success of SIC at the receivers. On the receiver side, we propose a new SIC method to decode the desired signal in the presence of strong interference. In contrast to existing SIC methods, our SIC method does not require channel estimation to decode the signals, thereby improving its resilience to interference. We have built a prototype of the proposed NOMA scheme on a wireless testbed. Experimental results show that, compared to orthogonal multiple access (OMA), the proposed NOMA scheme can significantly improve the weak user's date rate (93% on average) and considerably improve WLAN's weighted sum rate (36% on average).

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbour (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

Beamforming with semi-coprime arrays.

A semi-coprime array (SCA) interleaves two undersampled uniform linear arrays (ULAs) and a Q-element standard ULA. The undersampling factors of the first two arrays are QM and QN, respectively, where M and N are coprime. The resulting non-ULA is highly sparse. Taking the minimum of the absolute values of the conventional beampatterns of the three arrays results in a beampattern that is free of grating lobes. A SCA requires fewer sensors than other popular sparse arrays such as coprime arrays, nested arrays, and minimum redundant arrays for a given aperture. Also, a SCA exhibits better side lobe patterns than other sparse arrays. This means that a SCA is better able to mask signals away from the look direction and detect weak sources in the presence of strong interferers. In this paper, the author explores the direction of arrival estimation with the SCA. The results illustrate the SCA's ability to reduce the number of sensors and decrease system cost and complexity in various signal processing applications.

Robust transform domain signal processing for GNSS

Robust signal processing techniques are effective mitigation approaches that can enable Global Navigation Satellite System (GNSS) receiver operations even in the presence of strong interference. We extend robust GNSS signal processing to the case where interference admits a sparse representation in a transform domain (TD). More specifically, an orthonormal transformation is used to project the received GNSS samples into an appropriate TD. Robust signal processing strategies are then applied. The robust transform domain (RTD) cross-ambiguity function (CAF) is obtained by applying a zero-memory nonlinearity (ZMNL) to the TD samples, which, in turn, are used for the evaluation of the CAF. Different nonlinearities are analyzed and different implementations for the evaluation of the RTD CAF are proposed. The RTD approach is characterized from a theoretical point of view, with the usage of Monte Carlo simulations and through the processing of GNSS signals generated with a hardware simulator.

DOA estimation method of weak sources for an array antenna under strong interference conditions

ABSTRACTThe concept of a preserving source orthogonality and characteristic beam is defined according to an array data model. It is proven theoretically that the corresponding eigenvectors maintain the preserving orthogonality when the powers and the angle intervals between the signals are large. The theory is then applied to the direction of arrival (DOA) estimation for the array antenna in the presence of strong interference. A novel DOA estimation algorithm based on the elimination of the eigenvectors of interference is proposed. The simulation results and comparison of the jamming jam method (JJM) algorithm show that the proposed algorithm does not require the position of the interference source to be known. Moreover, its interference suppression ability and statistical estimation performance are superior to those of the JJM, and thus, it is more suitable for low SNR and small angle intervals.

Eigenanalysis-based adaptive interference suppression for underwater target estimation

It is generally difficult for a passive sonar system to localize a weak source due to the complexity and variability of the ocean environment, especially in the presence of strong interferences. In this paper, an eigenanalysis-based adaptive interference suppression method (EAAIS) is presented for estimating the weak source bearing. Assuming the target of interest (TOI) is in a known bearing sector, we first define a contribution ratio for each eigenvector of the cross-spectral density matrix (CSDM). The eigenvectors not dominated by the TOI are adaptively identified and removed for interference suppression. The remaining eigenvectors are then used to reconstruct the CSDM for TOI bearing estimation with adaptive beamforming methods. In comparison with the basic ECA method, the proposed method has better interference rejection capability and wider fields of application. Simulation and experimental results also show that the proposed method can achieve accurate localization estimates even in the presence of strong interferences.

A Consensus-Based Approach for Platooning with Intervehicular Communications and Its Validation in Realistic Scenarios

Automated and coordinated vehicles' driving (platooning) is very challenging due to the multibody control complexity and the presence of unreliable time-varying wireless intervehicular communication (IVC). We propose a novel controller for vehicle platooning based on consensus and analytically demonstrate its stability and dynamic properties. Traditional approaches assume the logical control topology as a constraint fixed a priori , and the control law is designed consequently; our approach makes the control topology a design parameter that can be exploited to reconfigure the controller, depending on the needs and characteristics of the scenario. Furthermore, the controller automatically compensates outdated information caused by network losses and delays. The controller is implemented in Plexe , which is a state-of-the-art IVC and mobility simulator that includes basic building blocks for platooning. Analysis and simulations show the controller robustness and performance in several scenarios, including realistic propagation conditions with interference caused by other vehicles. We compare our approach against a controller taken from the literature, which is generally considered among the most performing ones. Finally, we test the proposed controller by implementing the real dynamics (engine, transmission, braking systems, etc.) of heterogeneous vehicles in Plexe and verifying that platoons remain stable and safe, regardless of real-life impairments that cannot be modeled in the analytic solution. The results show the ability of the proposed approach to maintain a stable string of realistic vehicles with different control-communication topologies, even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.

A computational study of spectral matching algorithms for identifying Raman spectra of polycyclic aromatic hydrocarbons

To facilitate experimental development of a Raman‐based chemical sensor for identifying 16 carcinogenic polycyclic aromatic hydrocarbons (PAHs) in food and selection of an appropriate spectral matching algorithm for use with the sensor, computer simulations are carried out for evaluating the performance of several spectral matching algorithms in identifying Raman spectra of target PAHs in the presence of strong interference from co‐existent PAH spectra. The studied algorithms are the following: the Pearson correlation coefficient, the Euclidean distance, and the cosine distance in the spectral space and in a normalized principal component space (CD‐NPCA). The simulations are performed with mixture Raman spectra synthetized from a reference Raman spectral library of the 16 PAHs in the 1000–1700 cm−1 fingerprint spectral range that is calculated using density functional calculations. Receiver operating curves are generated for each target PAH and spectral algorithm pair to assess the performance of the algorithms. It is shown from the study that the CD‐NPCA outperforms the others in terms of speed and discriminating power for identifying the target spectra in the mixture spectra due to dimensionality reduction and an angular augmentation effect of input spectral data. This study provides a cost‐effective way for designing the Raman‐based sensor for PAH detection and paves the way for future experimental development of such a sensor. Copyright © 2016 John Wiley & Sons, Ltd.

The Ergodic Rate Density of Slotted and Unslotted CSMA Ad-Hoc Networks

The performance of random wireless ad-hoc networks (WANETs) is primarily limited by their self-interference. The utilization of a decentralized carrier sensing multiple access (CSMA) protocol protects the participating receivers from the presence of strong interferers and enhances the performance compared to the simpler ALOHA protocol. In this work, we analyze the ergodic rate density (ERD) of slotted and unslotted CSMA WANETs in the small backoff probability regime. Our main result is the derivation of simple expressions, which describe the ERD of CSMA WANETs as a function of the backoff probability, the path-loss exponent, and the ERD of the same WANET when applying the ALOHA protocol. The ERD expressions for both the slotted and the unslotted variants are shown to grow with the back-off probability. For the slotted variant the gain of CSMA over ALOHA is equal to the backoff probability. On the other hand, for the unslotted variant this gain is smaller by a constant factor, which is within the range of 0.57 to 0.67 for all cases of practical interest. Simulation results validate the precision of the derived expressions and demonstrate their capability to predict the optimal system parameters with very good accuracy.

Estimating the spatial spectra of Gaussian processes with co-prime sensor arrays

A co-prime sensor array (CSA) is a nonuniform line array formed by interleaving two undersampled uniform line arrays. The CSA requires fewer sensors to span the same aperture as a densely sampled uniform line array (ULA), allowing the CSA to match the resolution of the ULA for direction of arrival estimation of narrowband planewaves. However, each CSA subarray suffers from aliasing, or grating lobes, due to the spatial undersampling. Vaidyanathan and Pal (2011) proved that if the subarray undersampling factors are co-prime, the aliasing can be unambiguously resolved by multiplying the spatial spectra of the subarrays. This product spatial spectra is the spatial cross-spectral density between the arrays, and is an estimate of the spatial power spectral density (PSD). In this talk, we extend the classic results of Jenkins and Watts (1968) on the periodogram PSD estimator for Gaussian processes to obtain the product processor's bias for spatially wide-sense stationary processes, and the processor's covariance for spatially white Gaussians. Additionally, we demonstrate that the CSA's product PSD estimate is not necessarily positive definite. Consequently, the CSA product spectrum may fail to detect weak signals in the presence of strong interferers. [Work supported by ONR BRC Program.]

Eigenanalysis-Based Adaptive Interference Suppression and Its Application in Acoustic Source Range Estimation

Passive localization of weak sources in the presence of strong interferences is generally a difficult task. In this paper, an eigenanalysis-based adaptive interference suppression (EAAIS) method is proposed for estimating the bearing and subsequently the weak source range. Assuming the target of interest (TOI) is in a known bearing sector, first we define a contribution ratio for each eigenvector of the cross-spectral density matrix (CSDM). The eigenvectors not dominated by the TOI are adaptively identified and removed for interference suppression. The remaining eigenvectors are then used to reconstruct the CSDM for TOI bearing estimation. Finally, the acoustic source range estimation is achieved with the reconstructed CSDM, by matching the beam intensity striation pattern, where it is not required to estimate the waveguide-invariant parameter. The effectiveness of the proposed TOI localization is demonstrated using several experimental data sets.