Presence Of Multipath Research Articles

Detection of Ghost Targets for Automotive Radar in the Presence of Multipath

Detection of Ghost Targets for Automotive Radar in the Presence of Multipath

An Efficient Spectral Approach for JCR Narrow Band Signals in Presence of Multipath and Noise

An Efficient Spectral Approach for JCR Narrow Band Signals in Presence of Multipath and Noise

Частотно-поляризационная обработка сигналов для борьбы с многолучевостью в локальных системах местоопределения

The properties of a local positioning system operating in the 2.4 GHz ISM band, which is capable of supplementing the constellation with local system signals to achieve high positioning accuracy in a complex interference environment, are investigated. In this case, global navigation satellite systems (GNSS) signal receivers often also implement other methods for estimating the position or parameters associated with it: inertial systems, ultra-wideband systems, radars, etc. In this paper, the study of the properties of a local positioning system operating in the 2.4 GHz ISM band , capable of supplementing the constellation with local system signals to achieve high positioning accuracy in difficult conditions. The emphasis here is on indoor operation of the system when GNSS signals are unavailable. In addition, there are a large number of reflected signals in the room. The paper presents a method for describing reflected signals using the Jones vector, as well as the synthesis of optimal algorithms for estimating signal parameters under complex conditions for simplified scenarios. With the help of modeling, it is shown that the optimal methods are poorly applicable to real situations, however, they allow a better understanding of the physics of processes. Based on this understanding, a signal shaping technique is proposed to create a large redundancy of measurements in the receiver, as well as an empirical method for rejecting false measurements. With the help of this method, it has been experimentally shown that indoors in difficult conditions and in the presence of multipath, it is possible to achieve the RMS positioning error of less than 10 centimeters.

MCRB on DOA Estimation for Automotive MIMO Radar in the Presence of Multipath

Autonomous driving and advanced active safety features require accurate high-resolution sensing capabilities. Automotive radars are the key component of the vehicle sensing suit. However, when these radars operate in proximity to flat surfaces, such as roads and guardrails, they experience a multipath phenomenon that can degrade the accuracy of the direction-of-arrival (DOA) estimation. Presence of multipath leads to misspecification in the radar data model, resulting in estimation performance degradation, which cannot be reliably predicted by conventional performance bounds. In this paper, the misspecified Cramér-Rao bound (MCRB), which accounts for model misspecification, is derived for the problem of DOA estimation in the presence of multipath which is ignored by the estimator. Analytical relations between the MCRB and the Cramér-Rao bound are established, and the DOA estimation performance degradation due to multipath is investigated. The results show that the MCRB reliably predicts the asymptotic performance of the misspecified maximum-likelihood estimator and therefore, can serve as an efficient tool for automotive radar performance evaluation and system design.

FEATURES OF EVALUATION OF ACCURACY OF GEODESIC GPS MEASUREMENTS

It is known that the results of satellite measurements, which are obtained by the differential method, are usually dependent values and are characterized by the presence of a physical correlation with a close linear relationship. A model of errors of differential measurements is constructed, in which in order to separate the influence of interference on the signal coming to the satellite receiver, two components are identified, one of which is caused by interference from the main sources of error, are tropospheric and ionospheric refraction of navigation signals. individual measurement, in particular additional noise and multipath of satellite observation signals. The influence of the ratio of these components in the structure of measurement errors on the value of the correlation coefficient of signals received by satellite receivers operating in the differential mode is studied. According to the experience of satellite measurements, the share of the first component in the error of two synchronous measurements is more than 97 % of the total measurement error in the absence of additional external interference and multipath, and the correlation coefficient in practice is usually more than 0.999. This is confirmed by the fact that a pair of synchronous measurements are the result of measuring the same quantities, so their dependence is almost functional, it becomes probabilistic due to the influence of additional interference and multipath, which corresponds to the second component. Obviously, the effect of additional interference on the satellite signal path reduces the share of the first component in the measurement error. At the same time there is a decrease in the correlation coefficient. Analysis of the obtained data shows that in particular in the interval where the effect of additional interference, in particular the second component, does not exceed 25%, at 25% exposure the correlation coefficient takes 0.9, at 10% exposure it is 0.99, 3% exposure corresponds to a correlation coefficient of 0.9968. Therefore, the correlation coefficient is very sensitive to the influence of additional interference and multipath in the path of the radio signal, which is expressed by the second component in the error structure. This makes it possible to characterize each series of differential measurements by the presence or absence of multipath and justifies the use of reducing the correlation coefficient when performing coordinate determinations to indicate the presence of multipath on the useful signal of satellites.

Terrestrial Microwave Power Beaming

This paper establishes the practicality of terrestrial microwave power beaming at distances >1 km. To beam microwave power along the surface of the earth, it is necessary to consider and, where possible, exploit the effects of microwave propagation across terrain. Key design considerations include the effect of scattering from surface topography, approaches for characterizing the beam in the presence of multipath, safety, and efficiency. A rapid demonstration at the US Army Research Field in Blossom Point, MD, delivers 1.6 kW of electrical power at a 1046 m standoff from a 5.4-m-diameter X band transmitter. The transmitter is a reflector antenna with a linearly actuated feed horn that can focus the power density at specific standoff distances. Experimental results over cluttered, irregular terrain achieve a 2.3 dB (70%) enhancement in power density at the target site by deliberate exploitation of a ground bounce. A 4 m² rectenna receiver produces the 1.6 kW of output power at a 73% RF-to-DC conversion efficiency, which exceeds the current state of the art at X band. In addition, a test to destruction of 4 large rectenna arrays demonstrates how overvoltage protection circuits can improve the RF power handling of rectenna arrays by >1 dB. A final experiment demonstrates >1 kW delivered at >1 km distance to a light display. Since these results should readily scale to higher levels of power and performance, recommendations are provided to mature the technology for operational use.

DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced AoA solution. We propose a Deep Learning approach for deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2°.

Robust RFID Localization in Multipath With Phase-Based Particle Filtering and a Mobile Robot

This work revisits particle filtering RFID localization methods, solely based on phase measurements. The reader is installed on a low-cost robotic platform, which performs autonomously (and independently from the RFID reader) open source simultaneous localization and mapping (SLAM). In contrast to prior art, the proposed methods introduce a weight metric for each particle-measurement pair, based on geometry arguments, robust to phase measurement noise (e.g., due to multipath). In addition, the methods include the unknown constant phase offset as a parameter to be estimated. No reference tags are employed, no assumption on the tags' topology is assumed and special attention is paid for reduced execution time. It is found that the proposed phase-based localization methods offer robust performance in the presence of multipath, even when the tag phase measurements are variable in number and sporadic. The methods can easily accommodate a variable number of reader antennas. Mean absolute 3D localization error in the order of 24 cm for RFID tags in a library was shown, even though the system did not exploit excessive bandwidth or any reference tags. As a collateral dividend, the proposed methods also offer a concrete way to classify the environment as multipath-rich or not.

Receiver Design and Time of Arrival Estimation for Opportunistic Localization With 5G Signals

A comprehensive approach for opportunistic navigation with fifth-generation (5G) cellular signals that exploits the downlink channel is developed. The structure of possible 5G reference signals that can be exploited is presented. Then, a software-defined receiver (SDR) to extract navigation observables from cellular 5G signals is proposed. The statistics of the code phase error in a multipath-free environment and in the presence of multipath are derived, which are subsequently used to analyze the statistics of the position estimation error with different simulated channel models. Finally, experimental results are conducted to evaluate the ranging performance of the proposed SDR with real 5G signals. After removing the effect of the clock bias and drift from the estimated pseudorange, the ranging error standard deviation is shown to be 1.19 m.

Joint Delay and Phase Discriminator Based on ESPRIT for 5G NR Positioning

A joint delay and phase discriminator based on the ESPRIT algorithm is proposed for positioning with the fifth generation (5G) New Radio (NR) downlink cellular signals. Since ESPRIT requires knowledge of the number of paths of the radio channel to operate, the proposed discriminator is coupled and studied with two channel order estimators: minimum descriptive length (MDL) and efficient channel order determination (ECOD). To mitigate the delay and phase outliers appearing when the MDL overestimates the channel order, a path selection criterion based on the signal-to-noise ratio (SNR) of the path is proposed. The ESPRIT discriminator is evaluated in terms of the distribution of the estimated channel order, and the delay and phase errors in the absence and presence of multipath. It is observed that the path selection criterion makes the MDL suitable for positioning as no delay and phase outliers are produced. In the presence of multipath, the ESPRIT discriminator achieves lower mean and root-mean-square delay and phase errors than the commonly used early-minus-late power (EMLP) and 2-argument arctangent (ATAN2) discriminators.

Multi-BS Spatial Spectrum Fusion for 2-D DOA Estimation and Localization Using UCA in Massive MIMO System

In the indoor environment, where global positioning system (GPS) fails, the presence of multipath, non-line-of-sight (NLOS), and dense scatterer yields unreliable position estimation. In the massive multiple-input multiple-output (massive MIMO) system, the sparse channel property is exploited here for user localization as an integral part of the network to reduce the power of user equipment (UE). In this study, a novel spatial spectrum fusion estimation and localization (SSFEAL) algorithm using the uniform circular array (UCA) structure is proposed, which is based on the 2-D direction of arrival (DOA) estimation. The spatial spectrum of the incoherent narrowband uplink pilot signal on each frequency bin received at the distributed massive MIMO base stations (BSs) is used for spectrum fusion. The main contributions of this work are as follows: 1) the channel's sparse characteristics at individual BSs are used to synthesize the covariance matrix of the received signal at the central fusion center and 2) the proposed method has 2-D spatial information measurement fusion to generate informative spatial spectrum followed by grid refinement processing to reduce the localization error to submeter level. It is demonstrated that the proposed method using UCA gives higher resolution estimation performance compared with the rectangular array in the localization accuracy. Comprehensive Monte Carlo simulation and real application test results show the validity and submeter precision in the indoor environment.

Spectral transformation-based technique for reducing effect of limited pre-correlation bandwidth in the GNSS receiver filter in presence of noise and multipath

This paper proposes an efficient scheme to reduce the pre-correlation bandwidth effect in the global navigation satellite system (GNSS) receiver filtering process. It is mainly based on the application of a spectral transformation to the satellite-emitted signal that effectively reduces its band. At the receiver's end, this operation causes the spreading of noise over a much wider band than that used by the radio frequency stage. Consequently, the resulting auto-correlation function in the acquisition process acquires properties that enhance considerably the performance of the receiver in the presence of the multipath and noise disturbing phenomena. The simulation results demonstrate that the proposed method is a plausible solution for both multipath and noise problems in the GNSS applications for any limited value of the pre-correlation bandwidth in the receiver filter.

Tightly Coupled Array Design Based on Phase Center Contour for Indoor Direction Findings in Harsh Environments

A new model for accurate direction findings is presented, and the superiority of the proposed model is demonstrated over the existing models for real system requirements. The proposed model is constructed based on the phase center contour of each element in the array. It is also shown that the accuracy of the proposed model will remain for tightly coupled arrays while other models fail. A novel algorithm according to this new model is also proposed. The proposed algorithm is proved by both simulations and measurements to be a viable solution for indoor applications where strong multipath interferences exist in the environment. Furthermore, a detailed explanation of accurate direction finding in harsh environments is presented using ultra-miniaturized tightly coupled arrays. In general, the direction-of-arrival estimation accuracy deteriorates using such miniaturized arrays, whereas we have shown that an excellent estimation accuracy is achieved using the proposed model and the detection algorithm. This statement is substantiated for any environments regardless of the strength of interference. To validate the developed model and theory, several practical prototypes of tightly coupled arrays are fabricated using the typical FR4 printed-circuit-board (PCB) fabrication process. The prototypes are measured in several indoor scenarios at the presence of multipath. The results are studied in detail.

Experimental results of multipath behavior for GPS L1-L2 and Galileo E1-E5b in static and kinematic scenarios

Abstract Current trends like Autonomous Driving (AD) increase the need for a precise, reliable, and continuous position at high velocities. In both natural and man-made environments, Global Navigation Satellite System (GNSS) signals suffer challenges such as multipath, attenuation, or loss-of-lock. As Highway Assist and Highway Pilot are AD next steps, multipath knowledge is necessary for this typical user-case and kinematic situations. This paper presents a multipath performance analysis for GPS and Galileo satellites in static, slow, and high kinematic scenarios. The data is provided from car test-drives in both controlled and unrestricted, near-natural environments. The Code-Minus-Carrier (CMC) and cycle-slip implementations are validated with measurement data from consecutive days. Multipath statistical models based on satellite elevation are evaluated for the three investigated scenarios. Static models derived from the car setup measurements for GPS L1, L2 and Galileo E1 and E5b show a good agreement with a state-of-the-art model as well as the enhanced Galileo signals performance. Slow kinematic multipath results in a controlled environment showed an improvement for both navigation systems compared to the static measurements at the same place. This result is confirmed by static and slow kinematic multipath simulations with the same GNSS receiver. Post-processing analysis on highway measurements revealed a bigger multipath bias, compared to the open-sky static and slow kinematic measurement campaigns. Although less critical as urban or rural, this indicates the presence of multipath in this kind of environment as well. The impact of different parameters, including receiver architecture and Signal-to-noise ratio (SNR) are analyzed and discussed. Differential position (DGNSS) based on code is computed for each epoch and compared against GNSS/INS integrated position for all three measurement campaigns. The most significant 3D absolute error occurs where the greatest multipath envelope is found.

Robustified Estimators of Radar Elevation Angle Using a Specular Multipath Model

We consider the problem of estimating the elevation angle in the presence of multipath. The proposed method belongs to the class of maximum-likelihood-like estimators and employs a modified specular reflection model that accounts for the uncertainty of the steering vector by assuming that they are subject to unknown deterministic perturbations with bounded norms. The analysis, performed using convex optimization methods, allows us to obtain computationally efficient implementations of the approach. Real-world results and computer simulations confirm the improved behavior of the proposed robustified estimators.

A New Method for GNSS Multipath Mitigation with an Adaptive Frequency Domain Filter.

Multipath is the dominant error source for most fixed Global Navigation Satellite Systems (GNSS) sites and stations. The presence of multipath, particularly the multipath effect on pseudorange measurement, seriously affects positioning accuracy. Unfortunately, multipath effect reduction is still a challenging issue in high-accuracy GNSS positioning applications due to its special properties. To minimize the impact of the multipath effect, this paper focused on pseudorange multipath mitigation. First, the frequency spectrum of the code-minus-carrier divergence (CMCD) for Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and BeiDou Navigation Satellite System (BDS) satellites observed in different environments were analyzed, where we found that periodic fluctuations appeared in GPS and GLONASS as well as medium earth orbit (MEO) and inclined geosynchronous satellite orbit (IGSO) of BDS satellites in some situations, which manifested as peaks in the frequency domain. The results showed that the location of the frequency peaks in the frequency domain, width, and basic frequency spectrum intensity were different between different satellites and environments, causing difficulty in reducing the error impact. To eliminate such period fluctuations mainly caused by the multipath effect, a novel method based on a frequency domain filter was proposed in this paper. One of the keys of the proposed method was the use of short-time Fourier transformation (STFT) in the GNSS signal data processing to calculate the accurate local frequency spectrum when code-minus-carrier divergence (CMCD) was assumed to be time varying. Once the frequency spectrum was obtained, a new spectrum peak extraction method was used to locate the peak frequency position. By interpolation and inverse Fourier transformation, the influence of the spectrum peaks could be effectively eliminated, thus improving pseudorange precision. The experimental results showed that the periodic multipath effect could be greatly reduced by the proposed method.

Multipath mitigation method based on Gaussian mixture model in RF relative measurement

Radio Frequency (RF) technology represents a high-precision relative navigation solution that has significant potential for application to earth-orbiting satellites. In precision applications, multipath errors dominate the total error because observables, which are used to estimate carrier-phase integer ambiguity, are not always subject to a Gaussian distribution when dual-frequency ambiguity estimation methods are used in the presence of multipath. As it has been shown that ranging observables obey a Gaussian mixture distribution, this study proposes improvements to the accuracy of estimation based on multipath mitigation founded on the Gaussian mixture model. To this end, such a model is created for integer ambiguity resolution in the presence of multipath, using which the theoretical error in dual-frequency ambiguity estimation is derived. Expectation Maximization (EM), which aids dual-frequency ambiguity estimation, is subsequently proposed to reduce the effect of multipath errors. Finally, two experimental scenarios are implemented to test the performance of the proposed method. The results show that EM-aided dual-frequency ambiguity estimation reduces the range error to approximately 20% in comparison with simple dual-frequency ambiguity estimation. Therefore the proposed technique is effective for multipath mitigation in RF relative measurement.

Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

The modulation classification of the non-cooperation underwater acoustic (UWA) communication signals is extremely challenging due to the adverse UWA channel transmission characteristics and low signal to noise ratio (SNR), which lead to considerable impairments of the signal features. In this paper, the principal component analysis (PCA) is proposed for efficient extraction of the power spectra and square spectrum features of UWA signals at the presence of multipath, Doppler, and noise induced in UWA channels. The employment of PCA enables extraction of the principal components associated with different modulation mode as the input vector of classier, thus reducing the feature dimension and suppressing the influence of UWA channels and environmental noise. With the features obtained by PCA, an artificial neural network (ANN) classifier is adopted for modulation recognition of UWA communication signals. The experimental modulation classification results obtained with field signals in 4 different underwater acoustic channels show that the proposed PCA based modulation recognition method outperforms the classifier using classic features in terms of classification performance and noise tolerance.

Quasi Optical Multi-Ray Model For Wireless Communication Link in Millimeter Wavelengths

The spectrum of millimeter waves lay above 30GHz. The band between 30GHz up to 300GHz is called Extremely High Frequencies (EHF). This wide spectrum is relatively free of users and have recently became relevant for realizations of wireless communications an radars, including the fifth generation (5G) of cellular communications. Due to the short wavelengths, the propagation of millimeter waves can be analyzed using quasi-optical ray techniques. We present a multi-ray analysis of millimeter wave wireless link in the presence of multipath. The analysis is applicable for indoor and outdoor links and considers reflections from walls and buildings. It is shown that line-of-sight is not necessarily required in scenarios where multiple reflections exist as in long corridors, canyons and tunnels. The theoretical results are verified experimentally with a link in the W-band (94GHz).

Improvements to GPS Airborne Radio Occultation in the Lower Troposphere Through Implementation of the Phase Matching Method

AbstractAirborne radio occultation (ARO) is a remote sensing technique for atmospheric sounding using Global Positioning System signals received by an airborne instrument. The atmospheric refractivity profile, which depends on pressure, temperature, and water vapor, can be retrieved by measuring the signal delay due to the refractive medium through which the signal traverses. The ARO system was developed to make repeated observations within an individual meteorological event such as a tropical storm, regardless of the presence of clouds and precipitation, and complements existing observation techniques such as dropsondes and satellite remote sensing. RO systems can suffer multipath ray propagation in the lower troposphere if there are strong refractivity gradients, for example, due to a highly variable moisture distribution or a sharp boundary layer, interfering with continuous carrier phase tracking as well as complicating retrievals. The phase matching method has now been adapted for ARO and is shown to reduce negative biases in the refractivity retrieval by providing robust retrievals of bending angle in the presence of multipath. The retrieval results are presented for a flight campaign in September 2010 for Hurricane Karl in the Caribbean Sea. The accuracy is assessed through comparison with the European Centre for Medium Range Weather Forecasts Interim Reanalysis. The fractional difference in refractivity can be maintained at a standard deviation of 2% from flight level down to a height of 2 km. The phase matching method decreases the negative refractivity bias by as much as 4% over the classical geometrical optics retrieval method.