Signal Space Research Articles

Anomaly Detection Method of BDS Signal-in-Space Based on Autoregressive Distributed Lag Model

The signal-in-space (SIS) anomaly of BeiDou navigation satellite system (BDS) is an important factor affecting its high accuracy SIS quality assessment. Detecting and eliminating SIS anomaly is not only an important method to build SIS fault model of BDS, but also helps to guarantee the integrity of BDS navigation and positioning. Based on the problem that the traditional empirical threshold method cannot accurately identify the start and end times of anomalies in anomaly detection, which leads to anomaly detection leakage, a combined detection method based on autoregressive distributed lag model and empirical threshold is proposed in this paper. Before the calculation, the spurious anomalies of SIS are removed by data cleaning. The high-precision SIS ranging error (SISRE) is recovered by Space State Representation (SSR) correction number, and then projected to the user’s line of sight direction, and the anomaly detection threshold is determined by using the combined threshold of empirical threshold and autoregressive distributed lag (ARDL) model. The feasibility and effectiveness of the method were analyzed by using the data collected in 2021. The test results show that, compared with the traditional threshold method, the proposed method can more accurately detect the start and end points of SIS anomalies caused by clock anomalies, which is also the detection accuracy is improved. In addition, the anomaly detection method proposed in this paper is used to count the anomalies throughout the year, and the results show that the highest frequency of anomalies is found in geostationary orbit (GEO) and inclined geosynchronous orbit (IGSO), and these anomalies are mainly caused by satellite clocks.

Personalized Ground Vehicle Lane-Keeping Assist System Design: An Adaptive Sliding Mode Control Formulation With L1+α Reachability

This paper proposes a personalized ground-vehicle lane-keeping assist system (LKA). To begin with, a control-oriented model unifying nonlinear vehicle-road dynamics and a linear driver lane-keeping steering angle model is derived. Subsequently, a novel LKA control strategy, alloying an L1+α -reachable adaptive sliding-mode controller and a shifted-logistic-function-based smooth parameter projection scheme, is formulated. Through a non-quadratic Lyapunov redesign, the sliding manifold is asymptotically reached, throughout which the trajectory of the switching function's value is confined inside the L1+α signal space. In addition, the suggested smooth projection operator can prevent the unbounded control parameter drift whilst maintaining the control command's smoothness. A simulation study employing the CarSim-Simulink joint platform and a cyber driver model is performed to evaluate the proposed personalized LKA adaptive controller and compare it with a linear-robust-control-based solution.

Pressure vessel leakage detection method based on online acoustic emission signals

Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired. To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.

Analysis of Nonlinear Fiber Kerr Effects for Arbitrary Modulation Formats

Coherent optical transmission systems can be modeled as a four-dimensional (4D) signal space resulting from the two polarization states, each with two quadratures. Recently, nonlinear analytical models have been proposed capable of capturing the impact of Kerr nonlinearity on 4D constellations. None of these addresses the inter-channel nonlinear interference (NLI) imposed by arbitrary modulation formats in multi-channel wavelength division multiplexed (WDM) systems. In this paper, we introduce a general nonlinear model for multi-channel WDM systems that is valid for arbitrary modulation formats, even asymmetric ones. The proposed model converges to the previous models, including the EGN model, in the special case of polarization multiplexed systems. The model focuses on the cross-phase modulation (XPM) nonlinear term that lies at the heart of the NLI in multi-channel WDM systems operating on standard high dispersion single-mode fiber. We show that strategic mappings of the modulation format's coordinates to the polarization states can reduce the NLI undergone by these formats.

A Feasibility Study for Signal-in-Space Design for LEO-PNT Solutions With Miniaturized Satellites

The global navigation satellite systems (GNSSs) are increasingly suffering from interferences, such as coming from jammers and spoofers, and their performance is still modest in challenging urban and indoor scenarios. Therefore, there are efforts worldwide to develop complementary positioning, navigation, and timing (PNT) solutions. One such complementary method under current research is the so-called LEO-PNT, namely, PNT solutions based on low-Earth orbit (LEO) satellites, and in particular on small-sized or miniaturized satellites. Such satellites have low-to-moderate costs of building, launching, and maintenance. Several challenges are to be overcome when designing a new LEO-PNT solution, concerning all three satellite segments: 1) the signal-in-space (SIS) or space segment; 2) the ground segment; and 3) the user/receiver segment. This article presents a survey of the SIS design challenges under the inherent constraints of wireless-channel propagation impairments as well as some design recommendations for SIS features. We address different constellation types, achievable coverage limits, and geometric dilution of precision (GDOP) bounds, as well as achievable carrier-to-noise ratios (CNRs) under a realistic wireless channel model, based on a MATLAB Quadriga simulator. We also discuss several optimization criteria regarding LEO-PNT SIS design, by taking into account the tradeoff between a low cost/low number of satellites in orbit on the one hand, and a sufficient coverage and good CNR for PNT purposes on the other hand.

Timing: Tag Interference Modeling for RFID Localization in Dense Deployment

Due to low cost and low complexity, passive ultrahigh-frequency radio-frequency identification (UHF RFID) is gaining popularity in indoor localization. According to current RFID channel models, multipath propagation and noise are two significant factors that affect the accuracy of measured RF signals. To minimize the impacts of measurement errors on localization, scene analysis algorithms are proposed and got lots of attention in recent years. These algorithms make use of reference tags as similarity markers in neighboring locations to improve localization accuracy. However, densely deployed tags induce a new challenge, antenna interference, leading to the poor signal similarity between nearby tags. Nonetheless, there are no refined channel models for explaining the effect of spacing distances between tags on RF signal fluctuations, thus the choice of spacing distances becomes an outstanding issue for scene analysis localization algorithms. In this article, we propose Timing, the Tag Interference ModelING, for analyzing the relationship between signal fluctuations and spacing distances. Unlike inductive coupling in the near field, as the tags are all located at the far field of interacted antenna, we introduce microwave transmission line theory for channel model analysis. The experimental results verify that Timing is well suited to authentic situations. Additionally, we examine the performance of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -NN) algorithm under various spacing distances. The findings demonstrate that the spacing distance identified by Timing as a stable point achieves better accuracy with scene analysis algorithms. Therefore, Timing could be used as a critical foundation for the choice of spacing distances in future tag deployment applications.

One-stop function for gravitational-wave detection, identification, and inference

I define here a novel function on a modeled space of gravitational-wave signals, before studying its properties as a statistic for detection, as an objective function for identification, and as an effective likelihood function for inference. The main motivation behind this work is the open data-analysis problem for signals from extreme-mass-ratio inspirals, which is severely hindered by the presence of strong non-local parameter degeneracy in the signal space. I demonstrate the utility of the proposed function for the analysis of such signals, and suggest various possible directions for future research.

Demonstration of Spatial Modulation Using a Novel Active Transmitter Detection Scheme with Signal Space Diversity in Optical Wireless Communications.

Line-of-sight (LOS) indoor optical wireless communications (OWC) enable a high data rate transmission while potentially suffering from optical channel obstructions. Additional LOS links using diversity techniques can tackle the received signal performance degradation, where channel gains often differ in multiple LOS channels. In this paper, a novel active transmitter detection scheme in spatial modulation (SM) is proposed to be incorporated with signal space diversity (SSD) technique to enable an increased OWC system throughput with an improved bit-error-rate (BER). This transmitter detection scheme is composed of a signal pre-distortion technique at the transmitter and a power-based statistical detection method at the receiver, which can address the problem of power-based transmitter detection in SM using carrierless amplitude and phase modulation waveforms with numerous signal levels. Experimental results show that, with the proposed transmitter detection scheme, SSD can be effectively provided with ~0.61 dB signal-to-noise-ratio (SNR) improvement. Additionally, an improved data rate ~7.5 Gbit/s is expected due to effective transmitter detection in SM. The SSD performances at different constellation rotation angles and under different channel gain distributions are also investigated, respectively. The proposed scheme provides a practical solution to implement power-based SM and thus aids the SSD realization for improving system performance.

Virtual Sensing Network for Statistical Process Monitoring

Physical sensing is increasingly implemented in modern industries to improve information visibility, which brings real-time signals that are spatially distributed and temporally varying. These signals are often nonlinear and nonstationary in the high-dimensional space, which pose significant challenges to monitoring and control of complex systems. Therefore, this paper presents a new “virtual sensing” approach that places imaginary sensors at different locations in signaling trajectories to monitor evolving dynamics within the signal space. First, we propose self-organizing principles to investigate distributional and topological features of nonlinear signals for optimal placement of imaginary sensors. Second, we design and develop the network model to represent real-time flux dynamics among these virtual sensors, in which each node represents a virtual sensor, while edges signify signal flux among sensors. Third, the establishment of a network model as well as the notion of transition uncertainty enable a fine-grained view into system dynamics and then extend a new Flux Rank (FR) algorithm for process monitoring. Experimental results show that the network FR methodology not only delineate real-time flux patterns in nonlinear signals, but also effectively monitor spatiotemporal changes in the dynamics of nonlinear dynamical systems.

Irradiance Independent Spectrum Reconstruction from Camera Signals Using the Interpolation Method.

The spectrum of light captured by a camera can be reconstructed using the interpolation method. The reconstructed spectrum is a linear combination of the reference spectra, where the weighting coefficients are calculated from the signals of the pixel and the reference samples by interpolation. This method is known as the look-up table (LUT) method. It is irradiance-dependent due to the dependence of the reconstructed spectrum shape on the sample irradiance. Since the irradiance can vary in field applications, an irradiance-independent LUT (II-LUT) method is required to recover spectral reflectance. This paper proposes an II-LUT method to interpolate the spectrum in the normalized signal space. Munsell color chips irradiated with D65 were used as samples. Example cameras are a tricolor camera and a quadcolor camera. Results show that the proposed method can achieve the irradiance independent spectrum reconstruction and computation time saving at the expense of the recovered spectral reflectance error. Considering that the irradiance variation will introduce additional errors, the actual mean error using the II-LUT method might be smaller than that of the ID-LUT method. It is also shown that the proposed method outperformed the weighted principal component analysis method in both accuracy and computation speed.

Rationale and selection of voice signal pre-processing space in the authentication system

The article analyzes and investigates directions for improving the quality characteristics of voice authentication systems in various access systems. One of the main ways of improving the quality characteristics of these user authentication systems is related to the use of phase information of the voice signal. The urgent scientific task of researching new procedures for pre-processing the user’s voice signal of the authentication system is being solved. Refinement of pre-processing procedures was carried out, considering the use of phase data of the voice signal. The developed pre-processing procedures make it possible to reduce the impact of random errors in the user’s voice signal registration materials, which also appear due to the influence of external noise. The results are obtained in statistical analysis of simulation results using experimental voice data of the user of the authentication system. The phase space of the voice signal allows you to expand the possibilities of pre-processing due to the use of a priori information about the nature of the phase data change. The presented research results should be used in voice authentication systems, improving speech recognition systems, and solving speaker identification tasks.

Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method.

Piano score recognition is one of the important research contents in the field of music information retrieval, and it plays an important role in information processing. In order to reduce the influence of vocals on the progress of piano notes and restore the harmonic information corresponding to piano notes, the article models the harmonic information and vocal information corresponding to piano notes in the frequency spectrum. We use the phase space reconstruction method to extract the nonlinear feature parameters in the note audio and use some of the parameters as the training set to construct the support vector machine (SVM) classifier and the other part as the test set to test the recognition effect. Therefore, the method of adaptive signal decomposition and SVM is introduced into the signal preprocessing link, and the corresponding recognition process is established. In order to improve the performance of the support vector machine, the article uses measurement learning method to obtain the measurement learning and uses the measurement learning to replace the Euclidean distance of the Gaussian kernel function of the support vector machine. The SVM method of adaptive signal decomposition and the SVM method of principal component analysis are introduced into the preprocessing process of the note signal, and then the preprocessed signal is reconstructed in phase space, and the corresponding recognition process is established. The method of directly reconstructing the phase space of the original signal has higher accuracy and can be applied to the note recognition of continuous music segments. The final experimental results show that, compared with the current popular piano score recognition algorithm, the recognition accuracy of the proposed piano score recognition algorithm is improved by 3.5% to 12.2%.

ZCNET: Achieving High Capacity in Low Power Wide Area Networks

In this paper, a novel LPWAN technology, ZCNET, is proposed, which achieves significantly higher network capacity than existing solutions, such as LoRa, Sigfox, and RPMA. The capacity boost of ZCNET is mainly due to two reasons. First, a ZCNET node transmits signals that occupy a small fraction of the signal space, resulting in a low collision probability. Second, ZCNET supports 8 parallel root channels within a single frequency channel by using 8 Zadoff-Chu (ZC) root sequences. The root channels do not severely interfere with each other, mainly because the interference power is spread evenly over the entire signal space. A simple ALOHA-style protocol is used for medium access, with which a node randomly chooses the root channel and the range it occupies within the root channel. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations, and the results confirm that ZCNET achieves significant gains over LoRa, Sigfox, and RPMA. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications.

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

This letter presents an enhanced design of multi-dimensional (MD) constellations which play a pivotal role in many communication systems such as code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are attractive as their structural properties, if properly designed, lead to signal space diversity and hence improved error rate performance. Unlike the existing works which mostly focus on MD constellations with large minimum Euclidean distance (MED), we look for new MD constellations with additional feature that the minimum product distance (MPD) is also large. To this end, a non-convex optimization problem is formulated and then solved by the convex-concave procedure (CCCP). Compared with the state-of-the-art literature, our proposed MD constellations lead to significant error performance enhancement over Rayleigh fading channels whilst maintaining almost the same performance over the Gaussian channels. To demonstrate their application, we also show that these MD constellations give rise to good codebooks in sparse code multiple access systems.

Low-Complexity Detection for Multidimensional Codebooks Over Fading Channels

Signal space diversity (SSD) introduced by Boutros and Viterbo tremendously improves the error performance over fading channels without any power or bandwidth sacrifice. Maximum benefit is obtained with full diversity (FD) multidimensional codebooks and the rather complex maximum likelihood (ML) detection. In the present work, we propose a generalized combinatorial representation and an associated low-complexity list-based detection algorithm that works for any non-full or full diversity multidimensional codebook. The algorithm includes three options of lists offering different complexity-performance trade-offs. One of the lists has random size and is guaranteed to contain the ML point, while the other two lists have fixed or limited size. Some analytical results are presented for the list sizes. The algorithm also includes a smart search to find the most probable codebook point in the list. Numerical results show that the proposed algorithm yields optimal or close-to-optimum performance with a noteworthy detection complexity reduction. Therefore, its adoption can be of practical interest, especially in applications where powerful error-correcting codes are not supported.

Joint Channel Estimation and Data Detection for Hybrid RIS Aided Millimeter Wave OTFS Systems

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

Generic Stability Criteria for Switched Nonlinear Systems With Switching-Signal-Based Lyapunov Functions Using Takagi–Sugeno Fuzzy Model

The aim of this article is to establish generic stability conditions for switched nonlinear systems with mode-dependent average dwell-time (MDADT) switching rules via Takagi–Sugeno (T–S) fuzzy modeling, which cover all unstable modes, all stable modes, and partially unstable modes as special cases. Different from traditional multiple and multiple discontinuous Lyapunov function (MDLF) methods, the proposed novel switching-signal-based multiple discontinuous Lyapunov function (SMDLF) approach divides each mode-running interval dynamically according to the actual switching of the system. The developed approach can not only yield less conservative bounds on the MDADT but can also handle all unstable subsystems, which cannot be done by means of the traditional MDLF. A class of SMDLF is constructed for continuous-time switched T–S fuzzy systems, and relaxed stability conditions are presented for the system with both stable and unstable subsystems, using a larger switching signal space. Then, novel stability conditions are deduced for all stable and unstable subsystems in terms of slow and fast MDADT switching signals, respectively. Finally, comparative simulation examples are provided to verify the advantages and effectiveness of the proposed approach.

Quasi Monte Carlo time-frequency analysis

We study signal processing tasks in which the signal is mapped via some generalized time-frequency transform to a higher dimensional time-frequency space, processed there, and synthesized to an output signal. We show how to approximate such methods using a quasi-Monte Carlo (QMC) approach. We consider cases where the time-frequency representation is redundant, having feature axes in addition to the time and frequency axes. The proposed QMC method allows sampling both efficiently and evenly such redundant time-frequency representations. Indeed, 1) the number of samples required for a certain accuracy is log-linear in the resolution of the signal space, and depends only weakly on the dimension of the redundant time-frequency space, and 2) the quasi-random samples have low discrepancy, so they are spread evenly in the redundant time-frequency space. One example of such redundant representation is the localizing time-frequency transform (LTFT), where the time-frequency plane is enhanced by a third axis. This higher dimensional time-frequency space improves the quality of some time-frequency signal processing tasks, like the phase vocoder (an audio signal processing effect). Since the computational complexity of the QMC is log-linear in the resolution of the signal space, this higher dimensional time-frequency space does not degrade the computation complexity of the proposed QMC method. The proposed QMC method is more efficient than standard Monte Carlo methods, since the deterministic QMC sample points are optimally spread in the time-frequency space, while random samples are not.

A Novel Approach Based on Generative Adversarial Network for Interference Detection in Wireless Communications

With the rapid growth of wireless devices, the communication environment gets complex. The detection of interference or unauthorized signals can improve spectrum efficiency, which is a key technology for limited spectrum resources. Traditional detection methods analyze the parameter characteristics of the received signal. But it is difficult to detect interference with the same time and frequency as the original signal by those feature engineering. As a classical problem in deep learning, anomaly detection is usually solved by supervised learning. But a more challenging situation is to detect unknown or invisible anomalies. It means that the number of abnormal samples is insufficient and the data is highly biased toward the normal samples. In this paper, a wireless communication interference detection algorithm based on generative adversarial network (GAN) is proposed. In the semi-supervised learning scenario, the algorithm detects the time-frequency overlapped interference by the reconstruction strategy. The generator adopts the encoder-decoder-encoder architecture. In the training process, the model jointly learns the data distribution of normal samples by minimizing the distance in both the signal space and the latent space. In the inference phase, a large distance metric implies an abnormal sample. Experiments on simulated communication datasets show the superiority of the proposed algorithm.

Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension

With the increasingly obvious trend of satellite communication and network integration, especially the emergence of inter satellite links, the interconnection between space nodes has become the development trend and inevitable requirement of future space communication. However, problems, such as long communication distance, large transmission delay and loss, limited network resources and frequent switching of transmission links, limit the ability of spatial information transmission. Firstly, based on the idea of ring planning and design, this paper uses the joint design of beamforming between nodes to design the physical layer network coding in the relay to realize the reliable transmission of information. Secondly, according to the diversity of relay signal space resources and network node information exchange, a joint design method of relay compression matrix and node precoding vector is studied, which breaks through the existing configuration constraints. In this scheme, the computational complexity is reduced by compressing and precoding the matrix to ensure reliable decoding while obtaining spatial alignment gain and degrees of freedom. Simulated and real data results demonstrate the superiority and effectiveness of the proposed method.