Range Estimation Algorithm Research Articles

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

AbstractAccurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar

Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar.

Tackling range uncertainty in proton therapy: Development and evaluation of a new multi-slit prompt-gamma camera (MSPGC) system

In theory, the sharp dose falloff at the distal end of a proton beam allows for high conformal dose to the target. However, conformity has not been fully achieved in practice, primarily due to beam range uncertainty, which is approximately 4% and varies slightly across institutions. To address this issue, we developed a new range verification system prototype: a multi-slit prompt-gamma camera (MSPGC). This system features high prompt-gamma detection sensitivity, an advanced range estimation algorithm, and a precise camera positioning system. We evaluated the range measurement precision of the prototype for single spot beams with varying energies, proton quantities, and positions, as well as for spot-scanning proton beams in a simulated SSPT treatment using a phantom. Our results demonstrated high accuracy (<0.4 mm) in range measurement for the tested beam energies and positions. Measurement precision increased significantly with the number of protons, achieving 1% precision with 5 × 108 protons. For spot-scanning proton beams, the prototype ensured more than 5 × 108 protons per spot with a 7 mm or larger spot aggregation, achieving 1% range measurement precision. Based on these findings, we anticipate that the clinical application of the new prototype will reduce range uncertainty (currently approximately 4%) to 1% or less.

Development of simplified air drag models including crosswinds for commercial heavy vehicle combinations

Accurate range prediction requires good knowledge of the prevailing wind conditions and how they affect the energy consumption of the ego vehicle. A few different simplified vehicle air drag models that explicitly include the effect from crosswinds are presented and compared through some objective criteria. The models are developed from the normal air drag equation where the effect from wind is implicit and therefore often forgotten or neglected. The purpose is to find a low-complexity model complementing CFD models and wind tunnel tests, that can be used for range estimation and predictive energy management algorithms. To simplify online estimation, a requirement is that the air drag models only contain a few tuning parameters. The models are validated against CFD calculations for a few vehicle combinations and the best models show good accuracy for air attack angles up to at least 60 degrees. It is shown that the parameters of the simplified models can loosely be connected to some basic geometrical attributes of a vehicle combination so it should be possible to give at least a rough estimate of the parameters of a simplified model based on these geometrical attributes. This is useful for making a first estimate of the aerodynamic properties of a vehicle combination after major changes in the exterior, e.g. when adding a trailer. It also highlights that the size and the shape of the vehicle side may be mainly responsible for the high longitudinal air drag sensitivity to crosswinds for large vehicle combinations.

Pulsed Laser Ranging Techniques Based on Digital Signal Processing Methods for Proximity Fuze

Laser fuze systems play an emerging part in military technology bases nowadays. In the paper, a solution is developed to improve the accuracy of the laser rangefinder system in laser fuze without changing the hardware design of the digital signal processor. The proposed algorithm will combine real-time pulse processing techniques used in target range measurement. The new algorithm interpolates returned data using a relatively low sampling rate to a higher one. It applies a cross-correlation technique using high-resolution reference waveform models to restore the position of the super-sampled start and return pulses. Based on the Monte Carlo method and simulating the waveform in the noise environments, 100 trials were judged on the standard deviation of the range estimations. With the presentation of detection performance comparison results between range estimation algorithms of laser fuzes, this paper can provide guidance and references for the application in the proximity fuze.

New algorithm to estimate proton beam range for multi-slit prompt-gamma camera

The prompt gamma imaging (PGI) technique is considered as one of the most promising approaches to estimate the range of proton beam in the patient and unlock the full potential of proton therapy. In the PGI technique, a dedicated algorithm is required to estimate the range of the proton beam from the prompt gamma (PG) distribution acquired by a PGI system. In the present study, a new range estimation algorithm was developed for a multi-slit prompt-gamma camera, one of PGI systems, to estimate the range of proton beam with high accuracy. The performance of the developed algorithm was evaluated by Monte Carlo simulations for various beam/phantom combinations. Our results generally show that the developed algorithm is very robust, showing very high accuracy and precision for all the cases considered in the present study. The range estimation accuracy of the developed algorithm was 0.5–1.7 mm, which is approximately 1% of beam range, for 1 × 109 protons. Even for the typical number of protons for a spot (1 × 108), the range estimation accuracy of the developed algorithm was 2.1–4.6 mm and smaller than the range uncertainties and typical safety margin, while that of the existing algorithm was 2.5–9.6 mm.

Application of GPS occurrence data to understand African white-backed vultures Gyps africanus spatial home range overlaps.

Understanding key overlap zones and habitats which are intensively shared by species in space and time is crucial as it provides vital information to inform spatial conservation with maximum benefits. The advent of high‐resolution GPS technologies associated with new analytical algorithms is revolutionizing studies underpinning species spatial and social interaction patterns within ecosystems. Here, using a robust home range estimation algorithm, the autocorrelated kernel density estimator (AKDE) equipped with an equally powerful home range overlap metric, the Bhattacharyya's coefficient (BC), we provide one of the first attempts to estimate and delineate spatial home range overlap zones for critically endangered African white‐backed vultures to inform conservation planning. Six vultures were captured in Hwange National Park using a modified cannon net system after which they were tagged and tracked with high‐resolution GPS backpacks. Overall, results suggested weaker average home range overlaps based on both the pooled data (0.38 ± 0.26), wet non‐breeding seasonal data (0.32 ± 0.23), and dry breeding season data (0.34 ± 0.28). Vultures 4, 5, and 6 consistently revealed higher home range overlaps across all the scales with values ranging between 0.60 and 0.99. Individual vultures showed consistence in space use patterns as suggested by high between‐season home range overlaps, an indication that they may be largely resident within the Hwange ecosystem. Importantly, we also demonstrate that home range overlapping geographic zones are all concentrated within the protected area of Hwange National Park. Our study provides some of the first results on African vulture home range overlaps and segregation patterns in the savanna ecosystem based on unbiased telemetry data and rigorous analytical algorithms. Such knowledge may provide vital insights for prioritizing conservation efforts of key geographic overlap zones to derive maximum conservation benefits especially when targeting wide‐ranging and critically endangered African white‐backed vultures. To this end, spatial overlap zones estimated here, although based on a small sample size, could provide a strong foundation upon which other downstream social and ecological questions can be explored further to expand our understanding on shared space use mechanisms among African vulture species.

Experimental Investigation of Body-Centric Indoor Localization Using Compact Wearable Antennas and Machine Learning Algorithms

A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms, and time-of-arrival (TOA)-based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS, and NLOS scenarios. Multiclass support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63%. Threshold-based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5–3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx–Rx distance, presence of another obstruction between the Tx and Rx links, and performance in different environment which shows the suitability of the proposed methodology.

Range-Angle Decoupling and Estimation for FDA Radar With Non-Uniform Frequency Offset

Frequency diverse array (FDA) radar with non-uniform frequency increasing offset is the most potent method to decouple the range-angle dependent beampattern and form a dot-shaped beampattern. However, little known empirical research has focused on the joint range-angle estimation of FDA radar with non-uniform frequency offset. The aim of this letter is to investigate a decoupling model and propose a range-angle estimation algorithm of FDA radar with uniform or non-uniform frequency offset. First, the blind source separation algorithm is performed to process the observation signal and obtain the separation matrix. Then, the array manifold matrix of observation signal is estimated by separation matrix correction. Next, a decoupling model is formulated to separate the range and angle component from the estimated array manifold matrix. Finally, an independent range and angle estimation algorithm via pulse-pair technique is developed. The numerical simulations are performed to indicate the effectiveness and superior performance.

Improving Target Detection Ability Based on Time Invariant and Dot-Shape Beamforming in TMRC-FDA-MIMO Radar

Frequency diverse array and multiple-input multiple-output (FDA-MIMO) radar is studied more to realise the joint estimation of range and angle. However, the estimation performance of target parameters for linear FDA radar with an identical frequency increment and multiple-input multiple-output (IFI-FDA-MIMO) and logarithmically increased frequency offset linear interval, and multiple-input multiple-output (LIFO-FDA-MIMO) is fundamentally limited by the periodic range-time variation and time-variant dot shape beampattern respectively. In this article, we proposed a joint range and angle estimation algorithm based on a new waveform synthesis model of time modulation and rang compensation FDA-MIMO (TMRC-FDA-MIMO). The emulation results demonstrate that the improved scheme achieves the goal of time-invariant, dot-shaped and low sidelobe beampattern, which is optimised by a new accelerated particle swarm optimisation (NAPSO) algorithm. The performance of target estimation under the Cramer Rao lower bound (CRLB), and the root means square errors (RMSE) of the radar system is analysed. Moreover, the mathematical formula derivation and numerical results verify the performance of the proposed algorithm, which shows that TMRC-FDA-MIMO radar system is superior to others mentioned above.

Bearing and range estimation with an exact source‐sensor spatial model

In sensor array processing literature, near-field bearing and range estimation algorithms generally use spherical wavefront to model only array sensors’ phase response (sometimes with Fresnel approximation) and assume equality in amplitude response. Ignoring the range dependent amplitudes, though facilitating the algorithmic development, will cause systematic estimation errors due to model mismatch. By taking the spherical wavefront amplitude into account, a new bearing and range estimation algorithm for locating multiple near-field sinusoid sources is presented. With the estimation of the near-field sensor array's response vector, closed-form formulas for bearing and range estimates are derived from its magnitude, or phase, or both. The problem of estimation ambiguity is discussed as well. Cramér-Rao bound is also derived to serve as a benchmark for performance study.

Near-Field Parameter Estimation for Polarized Source Using Spatial Amplitude Ratio

In array signal processing, source-sensor amplitude responses are in general assumed to be in equality and signal parameters are estimated by exploiting spatial phase difference caused by propagation delay. This is unrealistic when sources are located in the near-field of the array. In near field, array sensors’ amplitude responses are inversely proportional to the radial distance from the sources. This spatial amplitude ratio is exploited for near-field parameter estimation in this letter. We derive an angle, range and polarization parameters estimation algorithm for multiple polarized sources using a linear cocentered orthogonal loop and dipole (COLD) array. We define the algorithm’s name as SPatial Amplitude Ratio based Estimator (SPARE). Compared with previous phase-based algorithms, SPARE has the following advantages: (1) it is closed-form, non-iterative, and search-free, (2) it is applicable to irregular array geometry, and has no restrict on minimum sensor-spacing, since array sensors’ amplitude responses are necessarily unambiguous, and (3) it can be applied to the arrays with phase uncertainties.

Robust range estimation algorithm based on hyper‐tangent loss function

Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper-tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection.

An Indoor Navigation App using Computer Vision and Sign Recognition.

Indoor navigation is a major challenge for people with visual impairments, who often lack access to visual cues such as informational signs, landmarks and structural features that people with normal vision rely on for wayfinding. Building on our recent work on a computer vision-based localization approach that runs in real time on a smartphone, we describe an accessible wayfinding iOS app we have created that provides turn-by-turn directions to a desired destination. The localization approach combines dead reckoning obtained using visual-inertial odometry (VIO) with information about the user's location in the environment from informational sign detections and map constraints. We explain how we estimate the user's distance from Exit signs appearing in the image, describe new improvements in the sign detection and range estimation algorithms, and outline our algorithm for determining appropriate turn-by-turn directions.

An Investigation Into Key Influence Factors for the Everyday Usability of Electric Vehicles

Society relies on electric mobility to decrease the problems associated with local emissions and global climate change. One key factor for the success of electric vehicles is their everyday usability. Today's users must deal with limited driving range in a sparse charging infrastructure. In this work, we investigate central features influencing the everyday usability of electric vehicles, such as battery capacity, charging infrastructure, range prediction accuracy and vehicle concepts. Since the influence of these aspects depend on each other, they cannot be examined separately. Therefore, we created a stochastic simulation framework with vehicle models and map data to calculate a large amount of different feature variations. One of our key findings is that battery capacities beyond 100 kWh are not feasible. In addition, we stated the importance of an accurate range estimation algorithm and a dense network of high-performance charging points for everyday usability. Taking these results into consideration would help policy makers and automobile manufacturers achieve worldwide acceptance of electric vehicles.

A Stochastic Range Estimation Algorithm for Electric Vehicles Using Traffic Phase Classification

Limited range and charging infrastructure leads to range anxiety of electric vehicle drivers. Current range estimation algorithms are deemed unreliable and large safety margins are reserved to prevent the risk of stranding. Range estimation in general depends on two factors: current battery energy content and the energy consumption forecast on the route to destination. This paper aims at improving the latter by enhancing the forecast with a notion of uncertainty. The prediction algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts. Thereby, a central part of the algorithm is the explicit evaluation of the traffic situation by classifying the traffic phases. With the help of this methodology, individual forecasts can be made more precise since they are highly dependent on surrounding traffic. To demonstrate the validity of the algorithms, the performance is evaluated using real test drive data comprising multiple drivers. On the basis of the performance evaluation, both the superiority of stochastic algorithms over deterministic predictions and the improvement of predictive performance by evaluating explicit traffic phases can be shown. Implementing the proposed methodology in modern day electric vehicles could reduce range anxiety and ultimately increase acceptance of electric mobility worldwide.

Robust Shrinkage Range Estimation Algorithms Based on Hampel and Skipped Filters

Herein, we present robust shrinkage range estimation algorithms for which received signal strength measurements are used to estimate the distance between emitter and sensor. The concepts of robustness for the Hampel filter and skipped filter are combined with shrinkage for the positive blind minimax and Bayes shrinkage estimation. It is demonstrated that the estimation accuracies of the proposed methods are higher than those of the existing median-based shrinkage methods through extensive simulations.

Influence of Measurement and Prediction Uncertainties on Range Estimation for Electric Vehicles

Knowledge of the remaining driving range is crucial for drivers of battery electric vehicles (BEVs), because maximum range and charging infrastructure are limited. This and the fact that current implementations of range displays are deemed unreliable leads to range anxiety and ultimately less acceptance of BEVs. Every range estimation algorithm depends on various uncertain inputs and hence, its result is also uncertain. A more reliable range estimation could be achieved by assessing uncertainties of the underlying predictions and measurements and their influence on the calculation result. This paper presents a method for accurately calculating the probability of reaching the destination of a given route. For this, uncertainty of future velocity and battery state is analyzed using the example of an actual test vehicle. The error propagation through a physical model of the powertrain is evaluated by means of Monte-Carlo method and Taylor series expansion. Range uncertainty is investigated for different sets of input parameters, yielding a necessary safety margin between 12% and 23% battery charge. It is also shown that uncertainty more than doubles over lifetime of the vehicle and that neglecting this uncertainty can lead to unpredictable stranding for disadvantageous initial conditions. Knowledge of range uncertainty can reduce the necessary safety margin and avoid stranding of vehicles. In addition, displaying the uncertainty information to the driver alleviates range anxiety of drivers and consequently, acceptance of electric vehicles can be raised in society.

Sequential source localisation and range estimation based on shrinkage algorithm

This study presents the shrinkage-based sequential source localisation and range estimation algorithms. The shrinkage factor is found using the variance of the estimate in the existing shrinkage algorithm. However, the variance of the estimate is difficult to calculate when the form of the estimate is complex. To circumvent this problem, the authors propose a shrinkage algorithm that employs the Cramer-Rao lower bound (CRLB) instead of the variance for the maximum likelihood (ML) estimate. The variance of the ML estimate and CRLB were found to be similar in simulation results. Furthermore, Stein's unbiased risk estimator and Ledoit-Wolf methods are used to determine the shrinkage factor. The resulting estimation accuracy of the proposed shrinkage-based sequential source localisation and range estimation methods was similar with that of the existing shrinkage algorithm.

Image-Based Visibility Estimation Algorithm for Intelligent Transportation Systems

Posted road speed limits contribute to the safety of driving, yet when certain driving conditions occur, such as fog or severe darkness, they become less meaningful to the drivers. To overcome this limitation, there is a need for adaptive speed limits system to improve road safety under varying driving conditions. In that vein, a visibility range estimation algorithm for real-time adaptive speed limits control in intelligent transportation systems is proposed in this paper. The information required to specify the speed limit is captured via a road side unit that collects environmental data and captures road images, which are then analyzed locally or on the cloud. The proposed analysis is performed using two image processing algorithms, namely, the improved dark channel prior (DCP) and weighted image entropy (WIE), and the support vector machine (SVM) classifier is used to produce a visibility indicator in real-time. Results obtained from the analysis of various parts of highways in Canada, provided by the Ministry of Transportation of Ontario, show that the proposed technique can generate credible visibility indicators to motorists. The analytical results corroborated by extensive field measurements confirmed the advantage of the proposed system when compared to other visibility estimation methods such as the conventional DCP and WIE, where the proposed system results exhibit about 25% accuracy enhancement over the other considered techniques. Moreover, the proposed DCP is about 26% faster than the conventional DCP. The obtained promising results potentiate the integration of the proposed technique in real-life scenarios.