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We demonstrate a method for constructing spacetime diagrams for special relativity on graph paper that has been rotated by 45o. The diagonal grid lines represent light-flash worldlines in Minkowski spacetime, and the boxes in the grid (called "clock diamonds") represent units of measurement corresponding to the ticks of an inertial observer's light clock. We show that many quantitative results can be read off a spacetime diagram simply by counting boxes, with very little algebra. In particular, we show that the squared interval between two events is equal to the signed area of the parallelogram on the grid (called the "causal diamond") with opposite vertices corresponding to those events. We use the Doppler effect-without explicit use of the Doppler formula-to motivate the method. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet's end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems. [ABSTRACT FROM AUTHOR]

In high-mobility scenarios, a user's media experience is severely constrained by the difficulty of network channel prediction, the instability of network quality, and other problems caused by the user's fast movement, frequent base station handovers, the Doppler effect, etc. To this end, this paper proposes a video adaptive transmission architecture based on three-dimensional caching. In the temporal dimension, video data are cached to different base stations, and in the spatial dimension video data are cached to base stations, high-speed trains, and clients, thus constructing a multilevel caching architecture based on spatio-temporal attributes. Then, this paper mathematically models the media stream transmission process and summarizes the optimization problems that need to be solved. To solve the optimization problem, this paper proposes three optimization algorithms, namely, the placement algorithm based on three-dimensional caching, the video content selection algorithm for caching, and the bitrate selection algorithm. Finally, this paper builds a simulation system, which shows that the scheme proposed in this paper is more suitable for high-speed mobile networks, with better and more stable performance. [ABSTRACT FROM AUTHOR]

Underwater unmanned vehicles are complementary with human presence and manned vehicles for deeper and more complex environments. An autonomous underwater vechicle (AUV) has automation and long-range capacity compared to a cable-guided remotely operated vehicle (ROV). Navigation of AUVs is challenging due to the high absorption of radio-frequency signals underwater and the absence of a global navigation satellite system (GNSS). As a result, most navigation algorithms rely on inertial and acoustic signals; precise localization is then costly in addition to being independent from acoustic data communication. The purpose of this paper is to propose and analyze the performance of a novel low-cost simultaneous communication and localization algorithm. The considered scenario consists of an AUV that acoustically sends sensor or status data to a single fixed beacon. By estimating the Doppler shift and the range from this data exchange, the algorithm can provide a location estimate of the AUV. Using a robust state estimator, we analyze the algorithm over a survey path used for AUV mission planning both in numerical simulations and at-sea experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a specific algebraic structure and demonstrates its nature as an extension field, enabling the construction of Golomb Costas (GC) arrays. It provides detailed instructions and examples for constructing GC arrays using this extension field, along with a corresponding flowchart. Additionally, the paper conducts a thorough analysis, incorporating calculations and comparisons, to evaluate the autocorrelation of a GC array derived from the extension field compared to that of a diagonal frequency hopping array. The analysis reveals the superior autocorrelation properties of GC arrays based on the extension field. Furthermore, the paper establishes a mathematical model for the signal coded by the frequency hopping array and subsequently simulates and compares the ambiguity function of the signal coded by a GC array with that of a signal coded by a diagonal frequency hopping array. This comparison underscores the thumbtack ambiguity function of frequency hopping signal coded by a GC array. Moreover, the paper thoroughly investigates the relationship between the correlation function of GC arrays and the roots of an algebraic equation in a finite field, and strictly proves the ideal autocorrelation properties of Golomb Costas arrays. [ABSTRACT FROM AUTHOR]

-- This paper presents a localization solution exploiting the Doppler effect, digital maps and the Global Positioning System (GPS). To deploy such a system, the following steps must be completed: selecting a suitable GPS receiver, developing operating software, creating an app for displaying digital maps offline, choosing a software-defined RF receiver with a stable frequency reference, integrating the GPS receiver with the map and a radio within a software environment, and setting up a transmitter-receiver link. The second part of the research involves comprehensive tests of the integrated localization system and analyzing the empirical results obtained. The novel approach described in this article consists in the use of digital maps and GNSS data for dynamic visualization of transmitter location using the SDF method. The research was carried out in an NLOS environment. [ABSTRACT FROM AUTHOR]

The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25% of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coeffcient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echofeatures, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than one minute, allowing its use on future field campaigns. [ABSTRACT FROM AUTHOR]

Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5–15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type. [ABSTRACT FROM AUTHOR]

In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range–velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources. [ABSTRACT FROM AUTHOR]

The article presents a study which describes the wind radiometer WIRA, a new ground-based microwave Doppler-spectro-radiometer for middle-atmospheric horizontal wind measurement. The study made use of montecarlo simulations to test its reliability. The result of the study shows that WIRA was less sensitive to higher altitudes than the mirror method as evident in the slightly higher wind speeds retrieved buy the centroid method.

Unmanned aerial vehicle (UAV) empowered vehicular networks are capable of providing flexible radio access, efficient data transmission, and secure driving operation. For these promising applications, the authors focus on channel modelling for UAV‐to‐vehicle (U2V) communications in this paper, which is the very first step for communication system design. Based on geometry and multi‐dimensional mobility patterns, a geometry‐based stochastic model framework for U2V communication is studied. Considering the practical scenario, multipath components (MPCs) are composed of a line‐of‐sight (LoS) path, a ground reflection (GR) path, and several random scattering (SC) paths. Specifically, the movement of a UAV in both three‐dimensional (3D) translational and 3D rotational directions is considered as well as a ground vehicle capable of moving in arbitrary directions with varying velocities. Subsequently, the Doppler shift expressions for various paths and movements are presented individually, incorporating spatial angles. Comprehensive simulation results are examined, illustrating that six‐dimensional (6D) motion exhibits varying effects with linear relative velocity, local angular velocity, and two types of spatial angles. Moreover, channel statistical properties such as temporal auto‐correlation function (TCF) and Doppler power spectral density (PSD) are studied to show the consideration of 6D mobility is indispensable. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles' sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors' data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window ( L w ), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range–velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation. This results in a novel joint range–velocity estimation algorithm termed as the CEEMD-SVD-FFT-Root-MUSIC (CEEMD-SVD-FRM) algorithm. First, the beat signal contaminated with additive white Gaussian noise is decomposed using CEEMD, and an appropriate autocorrelation coefficient threshold is determined to select the highly correlated intrinsic mode functions (IMFs). Then, the SVD is applied to the selected highly correlated IMFs for denoising the beat signal. Subsequently, the denoised IMFs and signal residuals are combined to reconstruct the beat signal. Finally, the FFT-Root-MUSIC algorithm is applied to the reconstructed beat signal to estimate both the range and Doppler frequencies, which are then used to calculate the range and velocity estimates of the targets. The proposed CEEMD-SVD-FRM algorithm is validated though simulations and experiments, demonstrating significant improvement in the robustness and accuracy of range and velocity estimates for the FMCW radar due to the effective denoising of the reconstructed beat signal. Moreover, it substantially outperforms the traditional methods in low SNR environments. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system's average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles' data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system's AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system. [ABSTRACT FROM AUTHOR]

In case of mitigate the reliance of aviation users on the Global Navigation Satellite System (GNSS) in an increasingly interference-prone environment, utilizing opportunistic signals from Low-Earth Orbit (LEO) for navigation and positioning is an alternative approach. However, LEO satellite SOPs are not intended for navigation. Therefore, it is necessary to design methods to extract navigation observables from these signals. In this paper, we proposed a lightweight deep learning model with a two-branch structure called CLOCFC, designed to extract navigation observables. Furthermore, we have established a low Earth orbit satellite signal dataset by using ORBCOMM constellation signals as the input to the model and Doppler frequency as the label for the model. The results show that CLOCFC, as a lightweight model, demonstrates a significantly faster convergence rate and higher accuracy in navigation observables extraction compared to other models (ResNet, Swin Transformer, and Clo Transformer). In CLOCFC, we introduce the CFC module, a kind of Liquid Neural Network, to enhance the information acquisition capability through the spatiotemporal information in the data sequence. Finally, we have also conducted extensive experiments with the Doppler shift extraction of LEO satellites as an example, under various noise and resolution conditions, demonstrating the superiority of the CLOCFC. [ABSTRACT FROM AUTHOR]

The discussion about the origin of the zebra pattern has been going on for more than 50 years. In many papers it is usually postulated that the double plasma resonance mechanism always works in the presence of fast particles in the magnetic trap. Due to a number of difficulties encountered by this mechanism, works on its improvement began to appear, mainly in a dozen papers by Karlický and Yasnov, where the whole discussion is based on variability of the ratio of the magnetic field and density height scales and the assumption of some plasma turbulence in the source. Here we show possibilities of an alternative model of the interaction between plasma waves and whistlers. Several phenomena were selected in which it is clear that the ratio of height scales does not change in the magnetic loop as the source of the zebra pattern. It is shown that all the main details of the sporadic zebra pattern in the phenomenon of August 1, 2010 (and in many other phenomena), can be explained within the framework of a unified model of zebra patterns and radio fibers (fiber bursts) in the interaction of plasma waves with whistlers. The main changes in the zebra pattern stripes are caused by scattering of fast particles by whistlers leading to switching of the whistler instability from the normal Doppler effect to the anomalous one. In the end, possibilities of laboratory experiments are considered and the solar zebra pattern is compared with similar stripes in the decameter radio emission of Jupiter. [ABSTRACT FROM AUTHOR]

This paper introduced a network throughput optimization method for interference and joint power control to address channel interference management and power control challenges within constrained unmanned systems. The method aimed to achieve maximum efficiency in overall energy utilization, frequency efficiency, and system performance. By examining the different duplex operation modes of the primary node in unmanned systems, the paper analyzed three types of interference: interlayer, intralayer, and mixed-type interference. It conducted a comprehensive assessment of Doppler frequency shifts, external interference effects, and signal-to-noise ratio estimations for cross-layer, intralayer, and mixed-layer interferences, deriving corresponding signal-to-noise ratio relationships. In addition, to mitigate interference and reduce energy consumption, this paper proposed a network throughput model based on power control and interference. It developed two centralized iterative methods and three distributed intelligent strategies to address the complex non-convex optimization problem. Simulation results demonstrate that the method maintains a balanced relationship between the number of unmanned systems, node antennas, and network capacity, leading to improved spectrum utilization and enhanced anti-interference transmission capabilities within unmanned systems. [ABSTRACT FROM AUTHOR]

Velocity information is important for describing the motion state of an object. Modern underwater navigation systems use the Doppler effect to extract the absolute velocity to the bottom or relative velocity to the water layer from the acoustic backscattering signal. Suppressing interference from sidelobe coupling is very important to improve the accuracy of phased-array acoustic Doppler Velocity Log (DVL) and to realize autonomous navigation in complex ocean environments. This paper proposes an interference suppression method based on optimizing the correlation delay of the pulse-pair algorithm to reduce the sidelobe coupling interference. The results of simulations and experiments show that the proposed method reduces the effect of sidelobe coupling on velocity measurements by 70% on average, which is expected to be useful for improving the autonomous navigation performance of unmanned vehicles in complex underwater environments. [ABSTRACT FROM AUTHOR]

Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received signal that severely impact decoding accuracy. This study aims to address the issue of rapid carrier phase shifts caused by significant time-varying Doppler shifts during mobile underwater M-SS communication. This paper innovatively proposes a method for updating matched filters based on correlation cost factors. By calculating the correlation cost factors for each received symbol, the method guides the direction of Doppler estimation and updates the matched filters. After identifying the optimal match, the received symbols are shifted, correlated, and decoded. Simulation and sea trial results indicate that this method demonstrates higher computational efficiency and improved decoding accuracy compared to traditional Doppler estimation matched filters under low signal-to-noise ratio conditions, and exhibits greater robustness under complex motion conditions. [ABSTRACT FROM AUTHOR]

Aiming at the problem of estimating the speed of M-ary Phase Shift Keying (MPSK) communication radiated sources and their carrying platform targets, this paper proposes a data-assisted and inter-symbol spectrum analysis-based speed estimation method for MPSK communication radiated sources. The method first demodulates a signal-carrying message symbol from the received MPSK signal; then segments the signal according to the symbol synchronization information and the symbol period; and then compensates the phase of the symbol waveform corresponding to the message data according to the demodulated message symbol; finally combines the phase-compensated symbol waveform data into a two-dimensional matrix and finds the Doppler frequency of the data at the same sampling moment of different symbols using the vertical Fourier transform to obtain the moving target speed. The speed measurement accuracy and anti-noise performance of the method are analyzed through simulation experiments, and the simulation results show that the speed measurement accuracy of the method is 98.5%. [ABSTRACT FROM AUTHOR]

In order to reduce the frequency of high-frequency Doppler signal light, the electronic bandwidth of a data acquisition system is reduced. This paper mainly describes the principle and experimental verification results of optical multistage cascade frequency reduction technology. The bandwidth requirement of the detector and the oscilloscope is reduced by the method of "relaying" the measured beat frequency signal between multiple electronic channels. Aiming to achieve the requirement of ultra-high speed measurement of 22 km/s, the requirement of the original signal frequency as high as 28 GHz electrical bandwidth is reduced to the acquisition and recording system with only 8 GHz bandwidth. A complete velocity profile of up to 11.47 km/s is measured on a three-stage light gas gun with velocity measurement accuracy of 1%. [ABSTRACT FROM AUTHOR]

To reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high‐speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time‐varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high‐precision DFO estimator based on the convolutional neural network with anti‐noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time‐varying channel. Moreover, to improve the practicality of the algorithm, the non‐ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high‐speed mobile scenarios. [ABSTRACT FROM AUTHOR]

The article presents a study which looks into an approach for profiling atmospheric temperature through Doppler-Radio Acoustic Sounding System (RASS). The study describes the application of RASS and antenna configurations and introduces the Kon's approximation-based relationship between Doppler shift and phase velocity. It also proposes the empirical correction of the Kon's approximation and presents results from several measurement campaigns.

Summary: Underwater acoustic channel is a challenging medium for communication due to the presence of significant multipath, high noise, frequency‐dependent propagation loss, and high and non‐uniform Doppler spread. Doppler shift is non‐negligible in underwater communication due to the low velocity of underwater signals. Synchronization and Doppler estimation are important requirements for achieving good performance in this channel. Synchronization algorithms that give good performance in radio communication do not work well in underwater communication. Hence, this area has received a lot of attention from researchers. This paper surveys important works in the area. The techniques proposed in the literature for frame synchronization, frequency and phase synchronization, and timing synchronization in single carrier communications are reviewed here. The synchronization techniques proposed for OFDM, MIMO OFDM, and spread spectrum communication are also surveyed. Doppler estimation methods proposed in the literature are also reviewed. It is found that most of the recent works in underwater acoustic communication focus on OFDM synchronization. Deep learning‐based methods proposed in the literature are also reviewed. Key open problems and areas that require future research attention in the field of synchronization and Doppler estimation in underwater communications are highlighted in this paper. The area needing most attention of underwater communication researchers was found to be MIMO OFDM due to the difficulty in synchronization in such systems while used in underwater communication. Reducing the computational complexity of the algorithms used is also important for future work. Schemes that work with Doppler due to relative velocity over 10 m/s also need to be developed. [ABSTRACT FROM AUTHOR]

The aim of this study is to experimentally demonstrate that the time-average Doppler spectrum of a continuous-wave lidar is proportional to the probability density function of the line-of-sight velocities. This would open the possibility of using cw lidars for a precise determination of the second-order atmospheric turbulence statistics. An atmospheric field campaign and a wind tunnel experiment are carried out to show that the use of an average Doppler spectrum instead of a time series of velocities determined from individual Doppler spectra significantly reduces the differences with the standard deviation measured using ordinary anemometers, such as ultra-sonic anemometers or hotwires. The proposed method essentially removes the spatial averaging effect intrinsic to the cw lidar systems. [ABSTRACT FROM AUTHOR]

Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler Beam Swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably-stratified flow past a wind turbine. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow are generally small, less than 0.5 ms-1. Errors in the cross-stream and vertical velocity components are much larger: cross-stream component errors are on the order of 1.0 m s-1 and errors in the vertical velocity exceed the actual measurements of the vertical velocity. DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 0.5 ms-1, but cross-stream and vertical velocity estimates in the near wake are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty. [ABSTRACT FROM AUTHOR]