Accurate Range Estimation Research Articles

Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system

Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.

Experimental concept validation of a proton therapy range verification system using scattered proton measurements

In recent years, the application of positron emission tomography (PET) for the dose range verification of proton therapy has been proposed. However, the positron distribution is determined by the nuclear reaction cross section; hence, PET may not accurately reflect the dose range primarily influenced by ionization. Consequently, a proton dose range verification system based on scattered proton measurements has been suggested owing to the similarity in the reaction cross section between Rutherford scattering and ionization. While previous investigations have only verified the feasibility of dose range estimation through simple simulations, the objective of this study is to demonstrate this feasibility through experimental investigation. In this paper, we established an experimental framework for capturing scattered protons and introduced an algorithm that compares measured signal patterns with a reference database to estimate the dose range. A therapeutic beam was irradiated onto the abdominal region of a human phantom, and scattered protons were measured using scintillation detectors placed on the phantom surface. Consequently, the dose range was estimated with error margins of 4.22 ± 3.68 and 0.60 ± 1.03 mm along the beam axis and perpendicular directions to the Bragg peak, respectively. While providing the same level of Bragg peak positioning accuracy as conventional methods, our system features small size, cost-effectiveness, and system simplicity. One notable limitation of our method is the challenge in achieving precise detector positioning, which is crucial for accurate dose range estimation. Future research will focus on improving detector-position accuracy and exploring advanced algorithms for signal analysis to further refine dose range estimations.

A Method for Correcting Signal Aberrations in Ultrasonic Indoor Positioning.

The increasing focus on the development of positioning techniques reflects the growing interest in applications and services based on indoor positioning. Many applications necessitate precise indoor positioning or tracking of individuals and assets, leading to rapid growth in products based on these technologies in certain market sectors. Ultrasonic systems have already proven effective in achieving the desired positioning accuracy and refresh rates. The typical signal used in ultrasonic positioning systems for estimating the range between the target and reference points is the linear chirp. Unfortunately, it can undergo shape aberration due to the effects of acoustic diffraction when the aperture exceeds a certain limit. The extent of the aberration is influenced by the shape and size of the transducer, as well as the angle at which the transducer is observed by the receiver. This aberration also affects the shape of the cross-correlation, causing it to lose its easily detectable characteristic of a single global peak, which typically corresponds to the correct lag associated with the signal's time of arrival. In such instances, cross-correlation techniques yield results with a significantly higher error than anticipated. In fact, the correct lag no longer corresponds to the peak of the cross-correlation. In this study, an alternative technique to global peak detection is proposed, leveraging the inherent symmetry observed in the shape of the aberrated cross-correlation. The numerical simulations, performed using the academic acoustic simulation software Field II, conducted using a typical ultrasonic chirp and ultrasonic emitter, compare the classical and the proposed range techniques in a standard office room. The analysis includes the effects of acoustical reflection in the room and of the acoustic noise at different levels of power. The results demonstrate that the proposed technique enables accurate range estimation even in the presence of severe cross-correlation shape aberrations and for signal-to-noise ratio levels common in office and room environments, even in presence of typical reflections. This allows the use of emitting transducers with a much larger aperture than that allowed by the classical cross-correlation technique. Consequently, it becomes possible to have greater acoustic power available, leading to improved signal-to-noise ratio (SNR).

The Importance of Representative Sampling for Home Range Estimation in Field Primatology

Understanding the amount of space required by animals to fulfill their biological needs is essential for comprehending their behavior, their ecological role within their community, and for effective conservation planning and resource management. The space-use patterns of habituated primates often are studied by using handheld GPS devices, which provide detailed movement information that can link patterns of ranging and space-use to the behavioral decisions that generate these patterns. However, these data may not accurately represent an animal’s total movements, posing challenges when the desired inference is at the home range scale. To address this problem, we used a 13-year dataset from 11 groups of white-faced capuchins (Cebus capucinus imitator) to examine the impact of sampling elements, such as sample size, regularity, and temporal coverage, on home range estimation accuracy. We found that accurate home range estimation is feasible with relatively small absolute sample sizes and irregular sampling, as long as the data are collected over extended time periods. Also, concentrated sampling can lead to bias and overconfidence due to uncaptured variations in space use and underlying movement behaviors. Sampling protocols relying on handheld GPS for home range estimation are improved by maximizing independent location data distributed across time periods much longer than the target species’ home range crossing timescale.

Phase-Based Ranging in Narrowband Systems With Missing/Interfered Tones

The growth in the number of low-cost narrow band radios such as Bluetooth low energy (BLE) enabled applications such as asset tracking, human behavior monitoring, and keyless entry. The accurate range estimation is a must in such applications. Phase-based ranging has recently gained momentum due to its high accuracy in multipath environment compared to traditional schemes such as ranging based on received signal strength. The phase-based ranging requires tone exchange on multiple frequencies on a uniformly sampled frequency grid. Such tone exchange may not be possible due to some missing tones, e.g., reserved advertisement channels. Furthermore, the IQ values at a given tone may be distorted by interference. In this paper, we proposed two phase-based ranging schemes which deal with the missing/interfered tones. We compare the performance and complexity of the proposed schemes using simulations, complexity analysis and measurement setups. In particular, we show that for small number of missing/interfered tones, the proposed system based on employing a trained neural network (NN) performs very close to a reference ranging system where there is no missing/interference tones. Interestingly, this high performance is at the cost of negligible additional computational complexity and up to 60.5 Kbytes of additional required memory compared to the reference system, making it an attractive solution for ranging using hardware-limited radios such as BLE.

Cooperative Localization for Passive RFID Backscatter Networks and Theoretical Analysis of Performance Limit

In fully-connected passive RFID backscatter networks, it is challenging to provide accurate range estimations due to complex channels. Facing this problem, we propose a differential analysis based anti-multipath technique by introducing a reference tag. Through the linear difference between target tag received power measurements with the reference tag activated and not, the power with respect to the reader-reference-target link is separated out from the mixed measurements, achieving the mitigation of multipath and accurately ranging. Under distributed localization schemes, after breaking down the full network into a series of fragments, it is vital but challenging to determine how to assemble them satisfactorily. VIABLE, virtual-actual assembling algorithm, is proposed to achieve distributed and cooperative localization. The virtual assembling phase rectifies the fragments over and over until their errors converge, which enables the mining of a satisfactory and adaptive assembling order. Subsequently, the actual phase assembles the rectified fragments together with that order and accomplishes the overall localization accurately. The theoretical analysis of performance limit is presented via the derivation of Cramér-Rao lower bound approximated by a particle approach. Extensive simulations demonstrate that our proposed framework outperforms existing algorithms for cooperative localization.

Learning based method for near field acoustic range estimation in spherical harmonics domain using intensity vectors

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.

State estimation in range coordinate using range-only measurements

In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.

Range estimation within a shallow-water waveguide via active and reactive acoustic intensity

The proposed ranging algorithm examines low-order modal variations in vertical reactive (Qz) and horizontal active (Ir) acoustic intensity and provides a gated estimate of the range from a receiver to a source. The receiver (measuring horizontal and vertical acoustic intensity) estimates range bins, for example, a source, which is confined within a range of 1 km to 2 km. Accurate initial range estimates provide key information for follow-on processors, such as tracking and depth classification algorithms. The technique relies on predictive broadband frequency characteristics between the superposition of low order trapped modes; modes which are restricted to the first few orders, for example, modes one through five. The binned range estimates can deliver, for example, initial values for higher level iterative and interpolative Detection, Classification and Localization (DCL) algorithms. Similarly, given an accurate opening estimate of a target’s range, neural networks techniques are enhanced. Existing at-sea shallow-water (<300 m), acoustic intensity data are compared to theoretical predictions to assess the feasibility and performance of the proposed algorithm.

Generalized Gaussian decomposition for full waveform LiDAR processing

Waveform decomposition techniques are commonly used to extract attributes of targets from light detection and ranging (LiDAR) waveforms. Since the shape of a real LiDAR waveform varies for different systems, the conventional models (e.g. the Gaussian function, lognormal function, and generalized normal function) cannot be universally used. In this paper, we present a generalized Gaussian decomposition (GGD) algorithm, which considers the received waveform as the convolution of an arbitrary system waveform with the target response assumed as a Gaussian mixture model. The proposed method was validated using the experimental waveforms sampled from our self-designed LiDAR system with two different system responses. Metrics, including the mean absolute error (MAE) for range retrieval and the root-mean-squared error (RMSE) for waveform fitting, were used to provide a comprehensive quantitative evaluation of the performance. Three classical models for waveform decomposition—the Gaussian, lognormal, and generalized normal functions—were introduced and studied for the comparison. As for the system waveform with a right-skewed profile, the experimental results showed that the GGD algorithm provided the lowest RMSE for waveform fitting, and the most accurate range estimates with an MAE of . The Gaussian decomposition (GD), lognormal decomposition (LND), and generalized normal decomposition (GND) algorithms produced much worse results with MAEs of 0.362, 1.091, and , respectively. As for the system waveform with a negative tail, the GGD algorithm also performed best with an MAE of , while the GD, LND and GND algorithms provided much larger MAEs of 0.457, 0.489, and , respectively. Therefore, the proposed method has the potential to extract more accurate model parameters from a variety of LiDAR waveforms regardless of the shape of the system waveform.

Motion Modeling and State Estimation in Range-Squared Coordinate

In some target tracking applications, angle measurements are absent due to sensor characteristics, which makes Cartesian states unobservable for a single stationary sensor. When multi-sensor target tracking techniques with only range or Doppler measurements are applied to produce Cartesian state estimates, their performance and efficiency can be benefited from accurate range and Doppler estimates obtained from single sensors. This paper investigates the range and Doppler estimation problem based on motion modeling in range-squared coordinate considering range-squared as a generic coordinate. A pseudostate vector consisting of range squared and its derivatives is defined to completely summarize the dynamic of the Cartesian nearly constant velocity (NCV) motion and it is found time evolution of the pseudostate is linear based on the methodology of state space representation. To estimate the pseudostate from range-Doppler or range-only measurements, a recursive filter is developed using the decorrelated unbiased converted measurement (DUCM) method. Finally, the pseudostate estimates in the range-squared coordinate are converted into the range coordinate using the scaled unscented transformation (SUT) method to produce range and Doppler estimates. Although the conversion is nonlinear, it is performed outside the filtering recursions without propagating nonlinearity approximation errors in the filtering recursions. Numerical experiments with various conditions of measurements demonstrate the superior performance benefits from the proposed accurate linear state equation and estimation method.

Self-Interference-Resistant IEEE 802.11ad-Based Joint Communication and Automotive Radar Design

The IEEE 802.11ad based joint communication and radar sensing (JCRS) has attracted great attention for vehicles in recent years. Existing studies mostly assume full-duplex radio for radar receiver based on perfect self-interference cancellation. However, the self-interference may not be fully cancelled in practice. In this paper, imperfect self-interference cancellation is considered. To combat the strong self-interference, a novel JCRS is developed based on IEEE 802.11ad orthogonal frequency division multiplex physical-layer (OFDM PHY) for multi-target sensing. More specifically, the received radar signal is designed to be synchronized with self-interference, and two radar sensing functions are developed for short-range sensing and long-range sensing separately. The synchronization with self-interference resolves the difficulty in self-interference calibration in short-range sensing and prevents the inter-subcarrier-interference (ICI) from self-interference in long-range sensing. The Golay sequences in the preamble are leveraged to combat self-interference for short-range sensing, thanks to the relatively strong reflection signal. For long-range sensing, the pilot signal and a few self-interference-free OFDM symbols at the end of the data frame are leveraged to combat self-interference. Particularly, a novel pilot signal design is first developed to achieve subcarrier-wise pulse radar for accurate velocity and coarse range estimation, and then the self-interference-free OFDM symbols are utilized to achieve accurate range estimation. A strategy of frame transmissions is finally developed to integrate these two functions while balancing the performance between communications and sensing. Simulation results show that cm/s level velocity estimation and cm level range estimation can be achieved for up to 200-meter radar sensing.

A multi-mode electric vehicle range estimator based on driving pattern recognition

Limited driving range and availability of charging infrastructures are still among the main barriers of adoption of electric vehicles (EVs) in the market. Combination of those limiting factors causes ‘range anxiety’ in EV users. While different EV battery technologies and charging infrastructures are under development, one short-term solution to reduce EV users’ range anxiety is to provide the EV user with an accurate range estimation. In this study, an EV range estimation technique is proposed that recognises the current driving pattern and then classifies it into one of the predefined clusters (driving modes). The future energy consumption per kilometre is then tuned according to the average energy consumption of each cluster. Having an updated energy consumption rate, the EV range is calculated based on the battery state-of-charge. Different features are considered for driving pattern clustering where ‘average speed’ and ‘average power’ were identified as the best choices for this application. The effectiveness of the proposed EV range estimator is validated using real driving data that gives an average error of 9% in EV energy consumption estimation ahead.

A Gaussian Process model for UAV localization using millimetre wave radar

The detection and positioning of unmanned aerial vehicles has become essential for both automation and surveillance tasks, in recent years. The design of accurate drone localization systems is challenging, especially in cluttered environments, where the target may be partially or even completely obscured. This paper proposes a precise detection and 3D localization system for drones, by means of a millimetre wave radar. Drone locations are estimated from spatial heatmaps of the received radar signals, which are obtained by applying the super-resolution MUSIC algorithm. These estimates are improved by analysis of the micro-Doppler effect, generated by the rotating propellers, which aids detection in poor visibility conditions. A novel Gaussian Process Regression model is developed, in order to compensate for systematic biases in the radar data. The complete system produces accurate estimates of the target range and direction, and is shown to outperform direct spectral analysis methods.

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

AbstractDue 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.

Extension of the ML-EM algorithm for dose estimation using PET in proton therapy: application to an inhomogeneous target

Positron emission tomography (PET) has been used for in vivo treatment verification, mainly for range verification, in proton therapy. Evaluating the direct dose from PET measurements remains challenging; however, it is highly desirable from a clinical perspective. In this study, a method for estimating the dose distribution from the positron emitter distributions was developed using the maximum likelihood expectation maximization algorithm. The 1D spatial relationship between positron emitter distributions and a dose distribution in an inhomogeneous target was inputted into the system matrix based on a filter framework. In contrast, spatial resolution of the PET system and total variation regularization (as prior knowledge for dose distribution) were considered in the 3D image-space. The dose estimation was demonstrated using Monte Carlo simulated PET activity distributions with substantial noise in a head and neck phantom. This mimicked the single field irradiation of the spread-out Bragg peak beams at clinical dose levels. Besides the simple implementation of the algorithm, this strategy achieved a high-speed calculation (30 s for a 3D dose estimation) and accurate dose and range estimations (less than 10% and 2 mm errors at 1-σ values, respectively). The proposed method could be key for using PET for in vivo dose monitoring.

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.

Residual range estimation for battery electric vehicle based on radial basis function neural network

The accurate residual range estimation of a battery electric vehicle (BEV) can alleviate the driver’s range anxiety and improve driving safety. In this study, the residual range estimation is considered a nonlinear system involving battery factors and a variety of vehicle working status factors. Given that the radial basis function neural network (RBF NN) performs well in approximating nonlinear systems, this study proposes a RBF NN method to estimate the residual range of BEV. The contribution analysis method is used to simplify the input layer of RBF NN and enhance the real-time performance of estimation. Then, the residual range is estimated by RBF NN using historical data newly collected at a fixed time in current discharge process. Some experimental data are from operational BEVs in Beijing, China. Experimental results of different types demonstrate that all errors are comparatively small and within the engineering limit.

An Injection-Locked-Based FMCW Transmitter With Synthetic Bandwidth Technique

An injection-locked-based frequency-modulated continuous-wave (FMCW) transmitter using a synthetic bandwidth technique is presented. A wideband chirp is synthesized by combining the up-converted narrowband chirp with different adjacent carrier frequencies. This is achieved by mixing a narrowband direct digital synthesis chirp with a fast-switching subharmonic injection-locked oscillator (SHILO). The use of SHILO allows fast switching at sub-10 ns and provides better phase noise performance than phase-locked loops due to its much wider loop bandwidth. The key idea is demonstrated at 4 GHz by generating two subband chirps with about 300-MHz bandwidth each, achieving a synthetic bandwidth of 600 MHz. The measured impulse response shows a resolution of 32 cm (Hamming) with a sidelobe level of −25 dBc and very accurate range estimates with accuracy of ±0.5 cm.

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

Millimeter-wave (MMW) signals in 60GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight (NLOS) propagation is a primary factor affecting the range precision. To improve range estimation, an Energy detector (ED) based normalized threshold algorithm which employs a Neural network (NN) is developed on the basis of NLOS identification. The maximum curl and standard deviation of the received energy block values are used to determine NLOS environment and the normalized thresholds for different Signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.