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"Smart" devices, such as contemporary smartphones and PDAs (Personal Digital Assistance), play a significant role in our daily live, be it for navigation or location-based services (LBSs). In this paper, the use of Ultra-Wide Band (UWB) and Wireless Fidelity (Wi-Fi) based on RTT (Round-Trip Time) measurements is investigated for pedestrian user localization. For this purpose, several scenarios are designed either using real observation or simulated data. In addition, the localization of user groups within a neighborhood based on collaborative navigation (CP) is investigated and analyzed. An analysis of the performance of these techniques for ranging the positioning estimation using different fusion algorithms is assessed. The methodology applied for CP leverages the hybrid nature of the range measurements obtained by UWB and Wi-Fi RTT systems. The proposed approach stands out due to its originality in two main aspects: (1) it focuses on developing and evaluating suitable models for correcting range errors in RF-based TWR (Two-Way Ranging) technologies, and (2) it emphasizes the development of a robust CP engine for groups of pedestrians. The results obtained demonstrate that a performance improvement with respect to position trueness for UWB and Wi-Fi RTT cases of the order of 74% and 54%, respectively, is achieved due to the integration of these techniques. The proposed localization algorithm based on a P2I/P2P (Peer-to-Infrastructure/Peer-to-Peer) configuration provides a potential improvement in position trueness up to 10% for continuous anchor availability, i.e., UWB known nodes or Wi-Fi access points (APs). Its full potential is evident for short-duration events of complete anchor loss (P2P-only), where an improvement of up to 53% in position trueness is achieved. Overall, the performance metrics estimated based on the extensive evaluation campaigns demonstrate the effectiveness of the proposed methodologies. [ABSTRACT FROM AUTHOR]

Background: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton‐induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning‐based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. Purpose: This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U‐Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. Methods: Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one‐dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. Results: The U‐Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. Conclusions: Both the U‐Net and the Transformer have certain advantages in the proton dose estimation task. The U‐Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose‐guided proton therapy. [ABSTRACT FROM AUTHOR]

The measurement uncertainty (MU) in environmental analyses is usually considered as a combination of the additive contributions from sampling, sample preparation and analytical measurement (usually instrumental). The target MU for such studies is usually relatively high due to the natural sample heterogeneity and/or the complicated pretreatment procedure required. In the analytical practice, the contribution of the sample preparation to MU is rarely evaluated. Thus, it remains a hidden part of the one related to the analysis. However, the knowledge for the contribution of sample preparation might provide important information and further possibility for optimization of the entire analytical procedure and reduction of the expanded MU of the analytical result. From statistical point of view, the separation of uncertainty contributions from the different steps is, generally, not a trivial task. Selection of the proper statistical approach depends on the data structure and quality, variables distribution, etc. In the present study, three different statistical methods for evaluation of the uncertainty contribution of the sample preparation were applied, compared, and discussed. The considered approaches are based on, both, classical and robust analysis of variances (ANOVA) applied to data from instrumental analysis of marine algae and bee honey samples, undergoing microwave digestion. Some general recommendations on the statistical approach selection are revealed based on real experimental data set. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4–2.5 GHz frequency range for the forward channel and 4.8–5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging. [ABSTRACT FROM AUTHOR]

As part of the intergovernmental and public interventions to reduce carbon dioxide emissions, there are no existing regulations to ban the sale of petrol motorcycles (PM), but it is expected that motorcycle regulations will follow car regulations with several years of delay. There is an emerging trend in motorcycle uptake, which will lead to new development projects with existing brands, and new brands, and will clearly increase the need for development tools that satisfies design challenges specific to electric motorcycles (EM) and electric powertrains. There is significant importance in motorcycle design to quantify the vehicle-level performance indicators and specifications, which are not limited to total vehicle mass, range, acceleration performance, and top speed. Those performance indicators should be quantified for different powertrain configurations and component selections to identify the most suitable configuration for the specific motorcycle development. In this paper, an innovative powertrain sizing approach is proposed to provide solutions for EMs against the design challenges specific to electric motorcycles. The innovative approach is to apply the practice of design space exploration (DSE) in resilient system design (RSD) to EM development. As a proof of concept, a case study of battery sizing is presented, in which a powertrain sizing tool is used to identify battery pack sizing requirements using requirement-based design (RBD), sensitivity analysis and DSE. The case study shows that the RBD approach allows EM product developers to identify a single solution, while DSE clearly demonstrates the trade-off between different configurations, taking multiple design variables into account. The tool prioritises high accessibility and high confidence with limited information at the early phases of electric motorcycle powertrain component sizing and selection. [ABSTRACT FROM AUTHOR]

To address the problem that the frequency diverse array MIMO (FDA-MIMO) radar requires a priori information on the target number when locating multiple targets and the multidimensional parameter estimation consumes a large amount of computation, a joint estimation method was proposed.The angle and distance dependence of the FDA-MIMO were utilized to transform the estimation of target number and orientation parameters into a multimodal optimization problem on the spatial spectrum, and a density-based multimodal differential evolution algorithm was designed to solve it, which reduced the computational load and did not require a separate target number estimation algorithm.Simulation results show that the proposed method outperformed the existing methods in estimating the target number and orientation parameters.It overcomes the application limitations of the existing orientation parameter estimation methods and can obtain optimal estimation results without quantization error.

In this work, we solve the distributed formation control problem for heterogeneous spatial underactuated vehicle networks subject to switching topologies. We consider the spatial rigid body model of underwater and aerial vehicles with one translational actuator for propulsion and three rotational actuators, without any simplification. A distributed finite‐time sliding mode observer is designed to estimate the ranges between vehicles based on the bearing angles, and thus, the control design does not require relative position or distance measurements. A coordinate transformation is proposed to define continuous reference velocity trajectories under switching topologies. Then, based on the cascade structure, a distributed formation protocol is presented which guarantees the global asymptotic convergence for the closed‐loop system. The formation controller has a simple structure, requires only neighbor‐to‐neighbor information exchange, and all the measurements may be obtained using simple onboard sensors. Numerical simulations for a group of heterogeneous spatial underactuated vehicles including autonomous underwater vehicles and quadrotors are presented. [ABSTRACT FROM AUTHOR]

The stopping-power ratio (SPR) of body tissues relative to water depends on the particle energy and mean excitation energy (I value) of the tissues. Effective energies to minimize the range error in proton therapy and ion beam therapy with helium, carbon, oxygen, and neon ions and elemental I values have been updated in recent studies. We investigated the effects of these updates on SPR estimation for computed tomography-based treatment planning. The updates led to an increase of up to 0.5% in the SPRs of soft tissues, whereas they led to a decrease of up to 1.9% in the SPRs of bone tissues compared with the current clinical settings. For 44 proton beams planned for 15 randomly sampled patients, the mean water-equivalent target depth change was − 0.2 mm with a standard deviation of 0.2 mm. The maximum change was − 0.6 mm, which we consider to be insignificant in clinical practice. [ABSTRACT FROM AUTHOR]

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.

To address the problem that the frequency diverse array MIMO (FDA-MIMO) radar requires a priori information on the target number when locating multiple targets and the multidimensional parameter estimation consumes a large amount of computation, a joint estimation method was proposed. The angle and distance dependence of the FDA-MIMO were utilized to transform the estimation of target number and orientation parameters into a multimodal optimization problem on the spatial spectrum, and a density-based multimodal differential evolution algorithm was designed to solve it, which reduced the computational load and did not require a separate target number estimation algorithm. Simulation results show that the proposed method outperformed the existing methods in estimating the target number and orientation parameters. It overcomes the application limitations of the existing orientation parameter estimation methods and can obtain optimal estimation results without quantization error. [ABSTRACT FROM AUTHOR]

The exact data of combustion efficiency are rare in the open literature near the ignition and at sub-idle, especially at the state close to the lean blowout limit, due to the complexity of combustion characteristics and inconvenience of measurement in these situations. At low working condition, in order to pursue low emission, the well-premixed combustion is expected to be used as much as possible even when it is close to the low limit combustion state. Consequently, this makes the devices easier to suffer from the sharp increase of pressure fluctuation, which results in pressure oscillation and even resonance. In this paper, a model combustor with an adjustable premix of fuel and air for low working condition is used. The problem of pressure oscillation under well-premixed and low working condition is pointed out through a comparison test. A simplified estimation method of combustion efficiency range is proposed and compared with the experiment. The experimental data confirm the applicability of the simplification algorithm of the combustion efficiency range. On the other hand, it is also pointed out that the reduction in premixing degree may be able to make the combustion efficiency less than 30%, and the CO2 content is a more important criterion for the efficiency range judgment near lean blowout state. This paper will provide a reference for the further study of combustion efficiency and pressure fluctuation under low limit combustion conditions.

Introduction: The lack of prior knowledge of the marine environment increases the difficulty of passive ranging of underwater sound sources by using a single hydrophone. The dispersion curve of the normal mode contains extensive marine environmental information, which can be extracted without prior knowledge, but the characteristics of dispersion curves of different modes vary, and the mode order cannot be determined from the received data.Methods: Herein, a method based on a single hydrophone that can jointly identify the mode order and estimate the propagation range in unknown marine environment is proposed. The method uses Bayesian theory as the main methodology and is applicable to broadband pulse sound sources in shallow seas with long-range propagation. The dispersion curves extracted from the data and those calculated by the dispersion formula are the input signal and the replica of the methods, respectively. Accurate identification of the normal mode order and estimation of the propagation range can be achieved by establishing the joint cost function.Results: In the case of unknown a priori knowledge of the marine environment, the method enables rapid inversion, is tolerant to environmental parameter mismatch, and is low cost and practical.Discussion: The simulation and measured data analysis results demonstrate the accuracy and validity of the method. The measured data contains linear frequency modulation impulse source signal and explosion sound source signals, and the mean relative error of range estimation is less than 5%.

Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework. [ABSTRACT FROM AUTHOR]

• Near-field acoustic range estimation. • Spherical harmonics decomposition of acoustic signal. • Spherical harmonics intensity vectors for rigid sphere. • Spherical harmonics intensity vector based range estimation. • Convolution neural network based classification. Near-field acoustic range estimation is considered one of the least explored research problems in digital signal processing under noise and reverberant conditions. This letter develops a new learning-based range estimation technique utilizing the spherical harmonics intensity (SH-INT) coefficients. The conventional range estimation in the spherical harmonics (SH) domain relies on the pressure coefficients. However, at high frequencies, these coefficients of different order and range overlap and hinder the accuracy of range estimation. On the contrary, the SH-INT coefficients are well distinguished at high frequencies for various orders and ranges, making these features favorable for accurate range estimation using learning algorithms. Since the SH-INT coefficients in the radial direction are independent of the source signal and vary with range, a convolutional neural network (CNN) model has been adopted to map the SH-INT coefficients with the range classes. The performance of the proposed spherical harmonic intensity (SH-INT) features in the context of near-field range estimation is validated by conducting exhaustive experiments on simulated and real data. Further, the error in near-field source range estimates is characterized using root mean square error (RMSE) criteria. The results are impactful and encourage the use of this method for practical near-field source range estimation applications. [ABSTRACT FROM AUTHOR]

One of the most fundamental yet challenging tasks for aquatic ecologists is to precisely delineate the range of species, particularly those that are broadly distributed, require specialized sampling methods, and may be simultaneously declining and increasing in different portions of their range. An exemplar is the Pacific lamprey Entosphenus tridentatus, a jawless anadromous fish of conservation concern that is actively managed in many coastal basins in western North America. To efficiently determine its distribution across the accessible 56,168 km of the upper Snake River basin in the north‐western United States, we first delimited potential habitat by using predictions from a species distribution model based on conventionally collected historical data and from the distribution of a potential surrogate, Chinook salmon Oncorhynchus tshawytscha, which yielded a potential habitat network of 10,615 km. Within this area, we conducted a two‐stage environmental DNA survey involving 394 new samples and 187 archived samples collected by professional biologists and citizen scientists using a single, standardized method from 2015 to 2021. We estimated that Pacific lamprey occupied 1875 km of lotic habitat in this basin, of which 1444 km may have been influenced by recent translocation efforts. Pacific lamprey DNA was consistently present throughout most river main stems, although detections became weaker or less frequent in the largest and warmest downstream channels and near their headwater extent. Pacific lamprey were detected in nearly all stocked tributaries, but there was no evidence of indigenous populations in such habitats. There was evidence of post‐stocking movement because detections were 1.8–36.0 km upstream from release sites. By crafting a model‐driven spatial sampling template and executing an eDNA‐based sampling campaign led by professionals and volunteers, supplemented by previously collected samples, we established a benchmark for understanding the current range of Pacific lamprey across a large portion of its range in the interior Columbia River basin. This approach could be tailored to refine range estimates for other wide‐ranging aquatic species of conservation concern. [ABSTRACT FROM AUTHOR]

Today's drivers of battery electric vehicles must deal with limited driving range in a sparse charging infrastructure. An accurate prediction of energy demand and driving range is therefore important and enables reliable routing and charge planning applications. Predictions of energy demand entail uncertainty, which can be considered directly with the use of probabilistic prediction algorithms. Machine learning algorithms are frequently applied in this context, but data used to train these algorithms are often distributed over a fleet of connected vehicles. Federated learning can be applied in this setting, but predictive uncertainty is typically not considered. We apply an extension of the federated averaging algorithm to learn probabilistic neural networks and linear regression models in a communication-efficient and privacy-preserving manner. We demonstrate the performance advantage of probabilistic prediction models over deterministic prediction models using proper scoring rules. Furthermore, we show that federated learning can improve the standard, driver-individual learning. Using probabilistic predictions, variable safety margins based on destination attainability can be applied, leading to increased effective driving range and reduced travel time.

The waveguide invariant is classically considered as constant in shallow water and can be utilized to estimate the source range. When the environment is variable, the error between the estimated range and the true range fluctuates and becomes unstable. The fluctuation of the range estimation error is large when we use the polar coordinate transform to obtain the slope of the two-dimensional Fourier transform ridge (2DFT-PT). The lines which are parallel in the ridge are not considered and the ridge information is not utilized sufficiently. For this problem, we propose a passive ranging method by using the Radon transform to obtain the slope (2DFT-RT) and achieve a more robust result. Its capability is investigated and compared with the 2DFT-PT method via numerical simulation and the SWellEx-96 experiment.

Multifrequency continuous-wave–continuous-wave (MFCW–CW) radars simultaneously transmit multifrequency continuous-wave (MFCW) signals sequentially at a constant time step and a continuous-wave (CW) signal constantly. As MFCW signals are transmitted sequentially, only one phase difference exists at each time. All phase differences between the MFCW and CW signals should be exploited to resolve ambiguous ranges. In this article, we propose a method to compensate the phase difference between the sequential MFCW signals and CW signal using the Doppler frequency of the CW signal. To verify the phase difference compensation result, we estimated the range of multiple targets. The simulation results showed that the proposed phase difference compensation method performed well regardless of compensation direction and target motion. Moreover, we proposed applying the maximum likelihood estimation (MLE) and reduced-complexity off-grid sparse Bayesian learning (RC–OGSBL) to estimate ranges and analyzed their performance. The range accuracy of the MLE using a 0.1- m grid search showed superior performance and the running time of the MLE using the simple method is the fastest among the simulated methods. The RC–OGSBL using a 5- m grid showed good results in terms of range accuracy and running time.

The localization accuracy demand is ever growing in UAV communication networks. We propose a joint coarse and fine range estimation method using single carrier burst signals with two samples per symbol for UAV networks. The coarse estimation of our joint estimation method exploits multiple preamble symbols for flexible single-carrier frequency-domain equalization (SC-FDE) frame structures to calculate correlation metrics, which are insensitive to frequency offset due to the differential correlation operation. Then, we propose a fine range estimation method using only two samples per symbol with expectation relying on shaping or matched filter. Furthermore, we derive the performance bounds for the ranging system using both raised cosine (RC) and better than raised-cosine (BTRC) pulses. Finally, extensive simulations are conducted to validate the proposed method in terms of estimate bias and variance for different modulations, shaping filters, and fading channels. Our simulation results show that, the root mean square errors of proposed ranging method can reach the order of centimeter at medium-to-high signal-to-noise ratio (SNR) region, whereas the case using BTRC filter is capable of enhancing the ranging performance at low SNRs.

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10 % lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96 % less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments. [ABSTRACT FROM AUTHOR]

Near-field (NF) localization is a key research topic in various applications, including autonomous vehicles, real-time tracking, target monitoring, etc. However, the traditional NF localization methods mostly rely on the line-of-sight (LOS) condition, and the performance may suffer degradation when there are lacking LOS paths. In this paper, we introduce a promising technology named reconfigurable intelligent surface (RIS) to resolve the problem and investigate the NF RSS-based localization algorithms. To be specific, we apply RIS to construct virtual line-of-sight (VLOS) paths between the anchor node (AN) and the unknown node (UN) for the sake of addressing the LOS absence problem, and propose the RIS phase adjustment schemes by maximizing the received signal strength (RSS) of the UN. On this basis, we derive the relationship between the azimuth and the phase parameters, and the accurate estimation of the UN’s position is derived via weighted least square (WLS) and alternate iteration methods. Next, we further resolve the coexisting problem in terms of LOS and VLOS paths by adjusting the reflection factors. Lastly, we put forward a method for discriminating whether the UN lies in the far-field (FF) or NF of the RIS subparts to minimize the localization error. Several simulations demonstrate the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

This article presents a new method for 3-D localization of an acoustic source signal by estimating its azimuth-angle, elevation-angle, and radial range. The proposed method exploits a spatially spread acoustic vector sensor, which is composed of a tri-axial velocity vector sensor and an isotropic pressure sensor. Unlike the spatially collocated case, the self-normalization of spatially spread acoustic vector sensor’s response is no longer independent of the source location, resulting in the inapplicability of the widely used “self-normalization” estimators (A. Nehorai et al., 1994), (V. N. Hari et al., 2012), (Y. Song et al., 2013). The present article considers the near-field propagation’s path attenuation and phase difference among the sensor components and derives a source location estimation method without resorting to the “self-normalization” operation. Also, the proposed method is applicable to any nonfree-space propagation models at arbitrarily unknown path-loss exponent. In comparison with the existing methods, where the velocity vector sensor is spatially collocated, the proposed method can offer improved performance estimation due to the inherent extension of the vector sensor’s spatial aperture in the spread structure. [ABSTRACT FROM AUTHOR]

In a Counter Insurgency/Counter Terrorism environment, where the life of a soldier is of paramount importance at times of uncertainty while carrying out house clearing drills. It is imperative that they would come across situations wherein there is a presence of a hostile target inside a closed room/house. This research paper encompasses “Through the wall target detection, classification, and range estimation”. Using a two port Vector Network Analyzer (VNA) as a power source, which was initially in the frequency domain was converted to time domain format for better assimilation of target detection and classification. One port of VNA utilized as transmitter and other as receiver, where two dual ridged horn antennas were placed. Also, by carrying out screen mirroring of the VNA with the laptop, the display as well as control of VNA, could be done using a laptop. The experimental results achieved were detection of stationary object/ stationary human being, moving human being, differentiation between stationary human & moving human and also differentiation between armed and unarmed personnel. These results achieved were based on the peak detection and display on the VNA screen/laptop. To achieve algorithm-based detection and classification, signal processing is required to be done depending on the peak detected and also an algorithm for real-time data transfer from VNA to the laptop. [ABSTRACT FROM AUTHOR]

As the launch frequency of reusable launch vehicle (RLV) increases, the requirement of landing site management is increasing. Especially, the return flight time of RLV should be constrained to improve the utilization of landing site. In this paper, a multi-process integrated (MPI) planning and guidance algorithm for deorbit phase and reentry phase is proposed to meet the mission requirement of RLV. Firstly, the states of RLV at reentry point and deorbit point are given by MPI planning algorithm through inverse calculation. To be specific, by taking the terminal state as the initial value, the reentry point is obtained by the backward calculation based on a cosine-quadratic bank angle profile. According to the relationship among the reentry point, the deorbit time and the deorbit range, the parameters of deorbit phase can be calculated by efficient interpolation based on deorbit ability analysis. The bank angle profile is adjusted to make estimated total flight range approximate the predefined total range by particle swarm optimization algorithm. After that, guidance algorithm considering the terminal position and time constraints of the whole flight phase is given, and the trajectory optimization problem related to brake angle and start-up time is transformed into a series of convex optimization problems in deorbit phase, which can be solved by primal-dual interior-point method with better computational performance and satisfactory accuracy. In the reentry phase, the bank angle profile is designed and updated with improved predictor-corrector algorithm to correct the error during the flight. Finally, the feasibility and robustness of the propose algorithm are verified by multi-mission planning guidance simulation and Monte Carlo simulation.

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.

For the multi-target detection,a multi-objective range estimation algorithm was proposed based on iterative interpolation to solve the problem of the range accuracy of frequency modulation continuous wave (FMCW) radar being easy to be interfered by spectrum.Firstly,the number of targets and the rough location of the target were obtained through the fast Fourier transform (FFT) and the constant false alarm (CFAR) detection algorithm.Then spectrum was iteratively interpolate,and the inter-spectral interference was eliminated during each iteration to obtain more accurate distance.Simulation results show that the proposed algorithm has higher estimation accuracy in multi-target environment and low Signal-to-noise ratio(SNR).The proposed algorithm is implemented by DSP,which proved that the algorithm meets the real-time requirements and is easy to be implemented in engineering.

In this paper, the line of sight (LOS) propagation model in visible light communication for a receiver with tilting plane is precisely derived. In addition, a relatively simple and reasonably accurate model of LOS propagation is proposed to employ received signal strength algorithm. Then, the maximum likelihood estimator is applied in range estimation where a new shot noise (RSN) source as a function of received signal power, is considered. Moreover, the Cramer-Rao lower bound is derived in closed form by considering RSN and tilting plane. Next, the influence of the Lambertian emission order on the channel model is investigated, theoretically and the optimum value of the Lambertian emission order is presented. Finally, performance is evaluated numerically. [ABSTRACT FROM AUTHOR]

In this article, we present a novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar. The localization and activity classification for aerial vehicle enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications. In the proposed method, Radar’s antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. The height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival. The aerial vehicle’s activity is classified using machine learning methods on micro-Doppler signatures extracted from Radar measurements taken in an outdoor environment. To evaluate performance, various light weight classification models such as logistic regression, support vector machine (SVM), Light gradient boosting machine (GBM), and a custom lightweight convolutional neural network (CNN) are investigated. Based on the results, the logistic regression, SVM, and Light GBM achieve an accuracy of 93%. Furthermore, the custom lightweight CNN can achieve activity classification accuracy of 95%. The performance of the proposed lightweight CNN is also compared with the pre-trained models (VGG16, VGG19, ResNet50, ResNet101, and InceptionResNet). The proposed lightweight CNN suits best for embedded and/or edge computing devices. [ABSTRACT FROM AUTHOR]

A method for improving the ranging performance for localization using orthogonal frequency- multiplexing-based communication system is proposed. The most difficult aspect of performing localization using the time-of-arrival information of a communication system is distinguishing indirect paths and noise from the direct path (DP), when the DP is blocked by obstacles. In this paper, the combination of interference cancellation and an enhanced path detector is proposed to remove interference from nearby paths and detect low-power DP. The proposed method is verified in 802.11ac environments, and it shows an improved performance compared with conventional methods.

To accurately identify the range of each target, traditional Multiple-Input Multiple-Output (MIMO) radar techniques not only require designing a shift matrix to describe different range bins but also a large number of snapshots. To alleviate this problem, a multidimensional parameter estimation method based on sparse iteration is proposed for a MIMO radar with Frequency Diverse Array (FDA). The FDA-MIMO radar uses small frequency increments across the array elements, and its transmit steering vector is a function of both range and angle. On the basis of the feature of the FDA-MIMO radar, we consider a weighted lq (0

Conventionally, mixed near-field (NF) and far-field (FF) source localization is performed using a symmetric uniform linear array (ULA). Recently, two symmetric nonuniform linear arrays (NLAs), symmetric nested array (SNA) and compressed SNA (CSNA), have been developed to enhance the positioning performance by their advantages in the degrees of freedom and the array aperture over symmetric ULAs. In this article, we devise a new symmetric NLA, termed symmetric displaced coprime array (SDCA), to locate the NF and FF sources simultaneously. The SDCA configuration consists of three sparse ULAs with a certain displacement, implying there are two displaced coprime arrays with one common subarray. For a given number of sensors, the SDCA configuration is solely determined by a closed-form expression and its consecutive coarray ranges can also be analytically computed. In addition, we derive two optimum SDCA configurations by maximizing the number of the unique and consecutive lags in the difference coarray. Compared with the SNA and the CSNA, the SDCA provides more unique and consecutive virtual sensors, as well as a larger physical array aperture. Numerical experiments are presented to verify the superiorities of the SDCA configuration over the existing symmetric NLAs. [ABSTRACT FROM AUTHOR]

Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor(PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the problem of detecting point-like targets in the presence of Gaussian disturbance with unknown covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the disturbances in both the cell under test (CUT) and the secondary data share the same covariance matrix up to an unknown power scaling factor. Specifically, we model the disturbance as a multichannel autoregressive Gaussian process and exploit the spillover of target energy to consecutive range samples, in order to improve the performances of detection and range estimation. In this context, we come up with three adaptive architectures relying on the ad hoc modifications of the generalized likelihood ratio test and Wald test. The performance assessment, conducted resorting to both simulated data and recorded live data, highlights that the proposed decision schemes can provide accurate estimates of the target position within the CUT and ensure enhanced detection performance compared with their natural competitors. [ABSTRACT FROM AUTHOR]

In this paper, we consider a simple, yet accurate approach for angle and range estimation of multiple targets in the frequency-modulated continuous-wave (FMCW) radar systems. Our new algorithm starts with estimation of time delays and ranges by the estimation of signal parameter via rotational invariance techniques (ESPRIT) algorithm. According to the estimated time delays, a group of filters is then devised to divide the received signals into a number of clusters. Finally, ESPRIT is invoked again to estimate the angles of the targets in each cluster. The new algorithm possesses some advantageous features. First, the number of the receive antennas could be less than that of the targets. Second, it is still applicable even when some targets are located at the same ranges or angles. Third, the estimated ranges and angles are automatically paired together. Both numerical simulations and practical experiments are conducted to demonstrate the superiority of our algorithm over the state-of-the-art works in terms of accuracy in various scenarios. [ABSTRACT FROM AUTHOR]

Despite the increased interest in battery electric vehicles (BEV), limited range abilities unsettle customers, which is often related to range anxiety. A better understanding of energy consumption and the possibility to accurately predict the remaining battery energy along an upcoming route may help to reduce this stress perception by means of advanced in-vehicle information systems. Addressing the trend towards vehicles with on-board cloud communication and information systems, the present research focuses on electric powertrain consumption and speed profile forecasts. A meaningful prediction of a speed profile for a given route is a basic prerequisite for an accurate consumption forecast. This study proposes a methodology to derive such a speed profile from real-time traffic data obtained from HERE Technologies while considering individual driving style characteristics. Given the predicted speed profile, a detailed BEV consumption model which accounts for BEV specific energy management strategies and environmental factors is used to obtain a consumption forecast. Prediction uncertainties are analyzed and parameter sensitivities with respect to energy consumption are derived as a function of the route dependent mean vehicle speed. Within a field study with Mercedes Benz EQC experimental vehicles, covering thirty-two test cycles, it is shown that the proposed methodology can accurately predict energy consumption for long look-ahead horizons and significantly reduces the variance in prediction compared to a typical baseline strategy. [ABSTRACT FROM AUTHOR]