Multipath Signals Research Articles

Integrated Sensing and Communication With Delay Alignment Modulation: Performance Analysis and Beamforming Optimization

Delay alignment modulation (DAM) has been recently proposed to enable manipulable channel delay spread for efficient single- or multi-carrier communications. In particular, with perfect delay alignment, inter-symbol interference (ISI) can be eliminated even with single-carrier (SC) transmission, without relying on sophisticated channel equalization. The key ideas of DAM are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delay pre-compensation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path-based beamforming</i> , so that all multi-path signal components may arrive at the receiver simultaneously and be superimposed constructively, rather than causing the detrimental ISI. Compared to the classic orthogonal frequency division multiplexing (OFDM) transmission, DAM-enabled SC communication has several appealing advantages, including low peak-to-average-power ratio (PAPR) and high tolerance for Doppler frequency shift, which renders DAM also appealing for radar sensing. Therefore, in this paper, DAM is investigated for integrated sensing and communication (ISAC) systems. We first study the output signal-to-noise ratios (SNRs) for ISI-free SC communication and radar sensing, respectively, and then derive the closed-form expressions for DAM-based sensing in terms of the ambiguity function (AF) and integrated sidelobe ratio (ISR). Furthermore, we study the beamforming design problem for DAM-based ISAC to maximize the communication SNR while guaranteeing the sensing performance in terms of the sensing SNR and ISR. Finally, we provide performance comparison between DAM and OFDM for ISAC, and it is revealed that DAM signal may achieve better communication and sensing performance, thanks to its low PAPR, reduced guard interval overhead, as well as higher tolerance for Doppler frequency shift. Simulation results are provided to show the great potential of DAM for ISAC.

Интервально-импульсная модуляция сверхширокополосных хаотических радиоимпульсов: теория и эксперимент

An approach to reduce the negative impact of multipath signal propagation (interpulse interference) in a wireless communication channel has been proposed. Pulse-interval modulation of chaotic radio pulses allows to increase the time interval between pulses without reducing the average bitrate. The possibility of using this method in ultra-wideband microwave channels with multipath propagation is demonstrated. The noise immunity of the AWGN channel has been studied. An experimental implementation of the method is described.

Time-frequency analysis of differences between coordinates of three permanent GNSS stations in Krakow

Abstract This paper applies time-frequency analysis to a 3-day time series with a sampling interval of 1 second of the changes in E, N and H coordinates of three permanent GNSS stations: WRON, KR10, and KRUR in Krakow, as well as differences between them. Time-frequency analysis was conducted using a Fourier transform band-pass filter, which separates time series into frequency components. By analyzing the differences between these coordinates, it was observed that the WRON station shows a systematic error in the form of a regular wideband oscillation with a period of 75 minutes, whose amplitude varies from approximately 1 to 3 mm with a period of about 1 day. In the horizontal plane, this oscillation takes the shape of a ˚attened ellipse with a semi-major axis oriented in the northwest direction. The most probable cause of this regular oscillation is the day-to-day variability of the multipath signal environment.

Analysis of Water Level Monitoring using GNSS Interferometric Reflectometry in River Waters

GNSS Interferometric Reflectometry (GNSS-IR) is one of the newest explorations of Global Navigation Satellite System (GNSS) signals which utilizes multipath signals to calculate the vertical distance from the reflecting surface to the geodetic antenna/receiver. However, scientific-grade or geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor. Hence, in this study, low-cost GNSS receiver devices were used to monitor the surface level of PT Garam River in Pamekasan Regency using the GNSS-IR method. The study location of PT Garam River which is quite close to the sea causes the water level to vary following the tides at the sea. This is because there are needs for advanced methodologies to limit GNSS observations in a much narrower area. The river in this case is a more challenging study location in terms of monitoring the water level. Calculation of river water level height with GNSS-IR observations is based on determining the peak frequency on the periodogram resulting from signal-to-noise ratio (SNR) data extraction. The two-weeks GNSS-IR observation in this study were applied by installing sideway orientation of GNSS antenna towards the river. This will be done to see how effective the strength of the reflected signal received by the low-cost GNSS devices are in narrow footprint area. Conventional tide gauge near the GNSS-IR site was used to evaluate the accuracy of GNSS-IR in river water level monitoring. Tide analysis is carried out to obtain tidal constituents and tide predictions using least square harmonics estimation (LSE). The resulting accuracy of the validation data is at an RMSE of 15.35 cm, with the correlation value of 0.94. The type of river tides in the study location based on the calculation results is mixed tide prevailing semidiurnal. Tide prediction for 20 days gives promising results with an RMSE of 16 cm. These results indicate that low-cost GNSS device has a promising capability for water level monitoring using GNSS-IR method in a narrow reflector area.

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

A vehicle’s position can be estimated with array receiving signal data without the help of satellite navigation. However, traditional array self-position determination methods are faced with the risk of failure under multipath environments. To deal with this problem, an array signal subspace fitting method is proposed for suppressing the multipath effect. Firstly, all signal incidence angles are estimated with enhanced spatial smoothing and root multiple signal classification (Root-MUSIC). Then, non-line-of-sight (NLOS) components are distinguished from multipath signals using a K-means clustering algorithm. Finally, the signal subspace fitting (SSF) function with a P matrix is established to reduce the NLOS components in multipath signals. Meanwhile, based on the initial clustering estimation, the search area can be significantly reduced, which can lead to less computational complexity. Compared with the C-matrix, oblique projection, initial signal fitting (ISF), multiple signal classification (MUSIC) and signal subspace fitting (SSF), the simulated experiments indicate that the proposed method has better NLOS component suppression performance, less computational complexity and more accurate positioning precision. A numerical analysis shows that the complexity of the proposed method has been reduced by at least 7.64dB. A cumulative distribution function (CDF) analysis demonstrates that the estimation accuracy of the proposed method is increased by 3.10dB compared with the clustering algorithm and 11.77dB compared with MUSIC, ISF and SSF under multipath environments.

Phase space reconstruction and self-attention convolutional neural network for sound source ranging with trial data

To improve the feature extraction ability of multipath signals and reduce the dependence on environmental information, this paper studies a method that combines multipath signal feature information mining and neural network in high-dimensional phase space instead of using the received complex acoustic pressure as the input of the neural network directly. Based on the phase space reconstruction (PSR) technology, we propose a hybrid model of the self-attention-based Transformer and convolutional neural network (CNN), named PSR-TransCNN. The covariance matrix of the reconstructed data is constructed as the input of the sparse auto-encoder (SAE). A transfer learning strategy is employed to obtain the optimal weight and offset value of SAE, which serve as the initial values of the PSR-TransCNN. Real-world experimental results demonstrate that the PSR-TransCNN significantly reduces the relative root mean square error (RMSE) by 0.006 compared with the matched field processing (MFP) method and exhibits outstanding generalization and practical application potential compared to other state-of-the-art (SOTA) methods. The PSR-TransCNN achieves an RMSE of 0.046 and an accuracy of 88.75%, successfully classifying sound sources with unknown labels. The proposed method effectively reduces the multipath effects and addresses the mismatch issue in sound source ranging (SSR) within an unexplored ocean environment.

Low complexity joint angle and delay estimation for underwater multipath acoustic channel based on sparse Bayesian learning

In the field of underwater acoustic communication, multipath signals arrive at the receiving end with different angles and path delays, which can cause degradation in communication quality. When performing angle-delay joint estimation, sparse Bayesian learning (SBL) has many advantages over traditional methods, such as high accuracy, resolution and global minimum results, but at the cost of high computational complexity. Regarding this, in this study, we propose a low-complexity SBL based framework to jointly estimate the angle and delay of the received signal. We first propose a low-complexity approximation SBL algorithm applied to joint estimation, which can keep the super-resolution and estimation accuracy but significantly reduce the computation time. To further tackle the problem of performance degradation of the approximate algorithm in underwater low signal-to-noise ratio environments, we utilize above result as a starting point for the space-alternating generalized Expectation Maximization (SAGE) algorithm to achieve a more precise estimation. Simulation and experimental results demonstrate that the proposed algorithm has an excellent performance in various underwater multipath scenarios.

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

Design of spread spectrum communication system based on multipath signal synthesis

AbstractA low‐complexity anti‐multipath interference spread spectrum communication system is proposed to address shortcomings in traditional anti‐multipath technology, such as complex structures and high resource consumption. The system utilizes a fast Fourier transform to capture pseudo‐noise code signals and simultaneously detect multipath signals. Moreover, the signal‐to‐noise ratio (SNR) of each path signal is calculated, and the multipath signal is synthesized through the designed optimal SNR synthesis method to improve the SNR of the demodulated signal to reduce the bit error rate. MATLAB is used to perform functional simulations of the system to verify its feasibility. The simulation results show that the system can successfully detect and synthesize multipath signals and achieve lower communication error rates than traditional code division multiple access (CDMA) systems.

Multipath time delay estimation based on the improved Wigner-ville distribution

The accuracy of underwater multipath channel time delay is crucial in underwater acoustic signal processing. The temporal resolution of the traditional matched filter is restricted by the transmitted signal bandwidth. Wigner-Ville distribution (WVD) which has excellent energy concentration characteristics and effectively avoids the interaction between temporal and frequency resolutions, and is widely used in non-stationary signal processing. However, considering the multipath signal processing, the cross-term in echo WVD would cause inaccurate time delay estimation. Therefore, in this paper, a novel multipath signal time delay estimation method based on the improved WVD is proposed. By utilizing the difference in the continuity of energy changes between auto-term and cross-term in the time-frequency domain, the Seeded Region Growing algorithm is applied and improved to extract auto-term in the WVD. The simulation results show that the method removes cross-term and maintains high concentration characteristics of auto-term, compared with pseudo-WVD and smooth pseudo-WVD, etc. In addition, it improves the accuracy of multipath signal delay estimation and has some anti-noise ability.

Using coherent processing algorithms for direction finding of UAV acoustic signals

Small UAVs are often used in a group, since their signals are highly correlated, their resolution is reduced when using non-coherent processing.&#x0D; The article analyzes the well-known methods of coherent processing of acoustic signals in order to increase the resolution in the direction finding of signals with a high degree of correlation. Obtaining qualitative indicators of the analyzed algorithms was carried out by the method of statistical computer modeling in the Matlab environment.&#x0D; Based on the simulation results, it is shown that coherent signal processing methods are the most stable in conditions of low signal-to-noise ratios, while non-coherent ones show the best results in the region of high signal-to-noise ratio, while coherent algorithms can potentially distinguish more targets. the WAVES coherent algorithm performs better in the high signal-to-noise ratio region, but loses to the CSSM algorithm in the low signal-to-noise region.&#x0D; To increase the efficiency of coherent processing of multipath signals, it applies spatial filtering of the inputsignal.

Time Reversal Detection for Moving Targets in Clutter Environments

Moving target detection plays a pivotal role in many applications, especially in radar systems. In addition, time-reversal (TR) technology can improve radar detection probability by effectively solving the problem of multipath signals. However, detection performance can be affected when clutter exists and target Doppler shifts are unknown. For this reason, we propose a novel moving target detection algorithm that leverages TR technology and a novel waveform optimization method in clutter and multipath signal environments. In this paper, we establish a TR signal model for a moving target in such environments and derive the TR average likelihood ratio test (TR-ALRT) detector. Additionally, we introduce a waveform optimization method that adapts to clutter and unknown Doppler information further to enhance the performance of the TR-ALRT detector. Consequently, a TR-ALRT waveform optimization (TR-ALRT-WO) detector is derived to adapt to the unknown Doppler and environment information, thereby improving the detection probability. Comparative analysis with other detectors under the same conditions validates the effectiveness of our proposed TR-ALRT-WO detector. Furthermore, numerical experiments demonstrate the superior performance of the proposed detection algorithm.

Pseudolite Multipath Estimation Adaptive Mitigation of Vector Tracking Based on Ref-MEDLL

Among many indoor positioning technologies, pseudolite positioning technology has become an important supplement to GNSS. In indoor open environments, pseudolite positioning technology can usually perform high-precision positioning. However, in a complex environment, the pseudolite receiver is seriously interfered by multipath and other interference signals, which will lead to a serious decline in the observation accuracy, signal lock loss or even no positioning results. Therefore, this work proposes a pseudolite indoor anti-multipath receiver based on reference multipath estimating delay lock loop (Ref-MEDLL). It adds the Ref-MEDLL multipath estimator module and the multipath mitigation module to the traditional receiver signal processing architecture. In the multipath mitigation module, the multipath estimation adaptive mitigation of vector tracking (MEAM-VT) method and the multipath estimation direct mitigation (MEDM) method for multipath mitigation is proposed. Experimental results show that the Ref-MEDLL multipath estimation method has good adaptability to multipath signals that have different time delays and different amplitudes; both the MEDM receiver and the MEAM-VT receiver have good multipath mitigation performance. The MEAM-VT method performs better than the MEDM method in multipath mitigation and tracking, but the stability of the pseudorange observations of the MEAM-VT method is not as good as that of the MEDM method. The positioning accuracy of the MEDM receiver and the MEAM-VT receiver has been improved to different degrees in static positioning experiments and dynamic positioning experiments.

Mechanism and Characteristic of Nonlinear Clutter in Harmonic Radar With Frequency-Modulated Waveform

Harmonic radar is a special radar system that has been widely used in insect tracking, search and rescue, and electronic equipment detection in recent years due to its excellent anti-clutter capability. However, in complex close-range scenarios, harmonic radar still suffers from nonlinear clutter interferences, which will cause a large number of clutter false alarms and seriously affect the performance of target detection. This paper analyzes the mechanism and the characteristic of nonlinear clutter through experimental results in complex close-range scenarios. It is found that the nonlinear clutter is generated by the harmonics and intermodulation components of the multipath signals. More importantly, it is the first time for the multipath intermodulation phenomenon to be revealed in harmonic radar with frequency-modulated waveform. A nonlinear clutter signal model is developed to explain this special phenomenon in this paper. The proposed theory is verified by the simulation and measurement results. The work in this paper lays a theoretical foundation for the subsequent work on nonlinear clutter suppression.

Adaptive Fusion Positioning Based on Gaussian Mixture Model for GNSS-RTK and Stereo Camera in Arboretum Environments

The integration of Global Navigation Satellite System (GNSS) Real-Time Kinematics (RTK) can provide high-precision, real-time, and global coverage of location information in open areas. But in arboretum environment, the ability to achieve continuous high-precision positioning using global positioning technology is limited due to various sources of interference, such as multi-path effects, signal obstruction, and environmental noise. In order to achieve precise navigation in challenging GNSS signal environments, visual SLAM systems are widely used due to their ability to adapt to different environmental features. Therefore, this paper proposes an optimized solution that integrates the measurements from GNSS-RTK and stereo cameras. The presented approach aligns the coordinates between the two sensors, and then employs an adaptive sliding window approach, which dynamically adjusts the window size and optimizes the pose within the sliding window. At the same time, to address the variations and uncertainties of GNSS signals in non-ideal environments, this paper proposes a solution that utilizes a Gaussian Mixture Model (GMM) to model the potential noise in GNSS signals. Furthermore, it employs a Variational Bayesian Inference-based (VBI) method to estimate the parameters of the GMM model online. The integration of this model with an optimization-based approach enhances the positioning accuracy and robustness even further. The evaluation results of real vehicle tests show that in challenging GNSS arboretum environments, GMM applied to GNSS/VO integration has higher accuracy and better robustness.

Low-Complexity Joint Angle of Arrival and Time of Arrival Estimation of Multipath Signal in UWB System.

In an ultra-wideband (UWB) system, the two-dimensional (2D) multiple signal classification (MUSIC) algorithms based on high-precision 2D spectral peak search can jointly estimate the time of arrival (TOA) and angle of arrival (AOA). However, the computational complexity of 2D-MUSIC is very high, and the corresponding data model is only based on the dual antennas. To solve these problems, a low-complexity algorithm for joint AOA and TOA estimation of the multipath ultra-wideband signal is proposed. Firstly, the dual antenna sensing data model is extended to the antenna array case. Then, based on the array-sensing data model, the proposed algorithm transforms the 2D spectral peak search of 2D-MUSIC into a secondary optimization problem to extract the estimation of AOA via only 1D search. Finally, the acquired AOA estimations are brought back, and the TOA estimations are also obtained through a 1D search. Moreover, in the case of an unknown transmitted signal waveform, the proposed method can still distinguish the main path signal based on the time difference of arrival of different paths, which shows wider applications. The simulation results show that the proposed algorithm outperforms the Root-MUSIC algorithm and the estimation of signal parameters using the rotational invariance techniques (ESPRIT) algorithm, and keeps the same estimation accuracy but with greatly reduced computational complexity compared to the 2D-MUSIC algorithm.

Analytical model OFDM-MIMO signal in a radio channel with frequency-time selective fading

One of the main problems in modern wireless electronic communication networks is the transmission and reception of signals in multipath propagation. Due to the imperfect impulse response of the communication channel, frequency-selective fading of the transmitted signal occurs. This can cause waveform distortion and inter-symbol interference, which can seriously degrade the performance of the communication system. Thus, increasing the noise immunity of wireless electronic communication networks in real-world conditions is one of the most important tasks of modern communication technologies. Orthogonal frequency division multiplexing is known for its resistance to multipath fading and is used to reduce the impact of inter-symbol interference. Multi-antenna systems are a key technology for increasing bandwidth and improving communication reliability. The combination of these two technologies in wireless electronic communication networks can reduce the impact of negative phenomena on the transmitted signal in difficult interference conditions. An analytical model of the signal transmitted by a nonstationary frequency-selective radio channel in a wireless electronic communication network with orthogonal frequency multiplexing and a multi-antenna system is proposed. Analytical expressions are presented that allow us to jointly take into account the influence of interchannel and inter-symbol interference (the effect of multipath signal propagation and Doppler shift on the transmitted signal) against the background of fluctuating noise. Taking into account the factors affecting the transmitted signal makes it possible to bring the model closer to the real processes that occur in the vast majority of radio channels of wireless electronic communication networks.

Wideband TDoA Positioning Exploiting RSS-Based Clustering.

The accuracy of radio-based positioning is heavily influenced by a dense multipath (DM) channel, leading to poor position accuracy. The DM affects both time of flight (ToF) measurements extracted from wideband (WB) signals-specifically, if the bandwidth is below 100 MHz-as well as received signal strength (RSS) measurements, due to the interference of multipath signal components onto the information-bearing line-of-sight (LoS) component. This work proposes an approach for combining these two different measurement technologies, leading to a robust position estimation in the presence of DM. We assume that a large ensemble of densely-spaced devices is to be positioned. We use RSS measurements to determine "clusters" of devices in the vicinity of each other. Joint processing of the WB measurements from all devices in a cluster efficiently suppresses the influence of the DM. We formulate an algorithmic approach for the information fusion of the two technologies and derive the corresponding Cramér-Rao lower bound (CRLB) to gain insight into the performance trade-offs at hand. We evaluate our results by simulations and validate the approach with real-world measurement data. The results show that the clustering approach can halve the root-mean-square error (RMSE) from about 2 m to below 1 m, using WB signal transmissions in the 2.4 GHz ISM band at a bandwidth of about 80 MHz.

Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.

Online monitoring instantaneous 2D temperature distributions in a furnace using acoustic tomography based on frequency division multiplexing

The online and accurate capture of dynamic changes in furnace temperature distribution is crucial for production efficiency improvement and international environmental policy compliance in power plants. To achieve this, a measurement system with a reliable online reconstruction capability and high temporal resolution is necessary. This paper presents a novel technique that can improve the temporal resolution of the currently existing acoustic tomography (AT) system using frequency division multiplexing (FDM). This method allows for concurrent transmissions of acoustic signals in several different frequency bands instead of a sequential manner, which leads to more efficient channel utilization and allows all acoustic signals to be acquired at the same time, so that a better temporal uniformity of multipath acoustic signals can be realized. Theoretical analysis and experiments have been conducted to verify the effectiveness of this technique. The results prove that the proposed method can significantly improve the temporal resolution of the AT system while maintaining the accuracy and robustness of the reconstruction.