Range Estimation Performance Research Articles

Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics.

Performance analysis of passive acoustic ranging using a single hydrophone in shallow waters

Passive source range estimation is one of the important research fields on the sonar system. Recent research has carried out range estimation using single hydrophone. This study presents the result of analyzing the impact of environmental conditions on range estimation performance when estimating the range of a moving source with a single hydrophone. Range estimation of a moving source using a single hydrophone estimates source velocity from the cross-correlated fields of the signal measured at time intervals. This method of estimating the source range from estimated source velocity is used. Therefore, it is important to accurately estimate the source velocity for ranging, and the accuracy of estimating the source velocity is affected by the propagating mode characteristics in waveguide. This study investigates the changes in velocity estimation performance of a moving source according to various underwater environment and mode formation characteristics through simulation analysis and discusses ways to reduce velocity estimation error. [Work supported by KRIT (Contract No. 22-305-B00-001).]

Passive localization using horizontal dual-array acoustic intensity striations

The formation of interference striations is closely related to channel structure and target location, which has attracted extensive attention from scholars at home and abroad. In this paper, the passive localizing method using observed interference striations of horizontal dual-arrays will be studied. The relationship between interference phenomena and target motion is discussed firstly, then the basic principle of the passive positioning method by using horizontal dual-array acoustic intensity striations is derived mathematically. Meanwhile, it can be seen that the shape of the interference striations is related to the parameters, which reflects the motion parameters of the target. This provides mathematical theory support for extracting the the target motion state parameters carried out by Hough transformation according to these acoustic interference striations.The simulation results show that the proposed method can effectively obtain the targets’ range. Meanwhile, comparing the ranging results of different groups of parameters, we can see that the more accurate the target motion parameters are, the smaller the estimated target distance error is. Finally, the performance of the algorithm under different noise conditions is discussed. The simulation results show that the error of extracted parameters is larger under the condition of low SNR, which leads to a larger passive positioning error. However, at high SNR, the range estimation performance is good, within 10%.

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.

Evaluation of Range Estimation Performance of FLIR with Field Requirements Criteria

Thermal imaging performance depends on many variables, ranging from the properties of the imaged object to atmospheric transmittance and system parameters. After clarification of the functional needs in system design or procurement, system parameters of the design that can meet these needs should be determined. Diagnosis/recognition from a distance is one of the foremost of these needs. The following briefly introduces the Forward Looking Infrared (FLIR) systems, followed by explanations for calculating the theoretical diagnostic range. After the theoretical information, sample systems are given, and high-performance FLIR systems are presented. To accurately analyze, measure and predict the performance of FLIR systems, a model should calculate summary performance measures of the system in the form of Minimum Resolvable Temperature (MRT) and Modulation Transfer Function (MTF) between a target and its background and estimate range for a given scenario electro-optical required for the performance evaluation of the system. The accuracy of these calculations will ultimately determine the accuracy of the model by which the performance of the FLIR system is evaluated.

Channel and Radar Parameter Estimation With Fractional Delay-Doppler Using OTFS

Orthogonal time frequency space (OTFS) waveform is suited for both communication as well as radar sensing. In this paper, we propose an algorithm for efficient channel estimation at the receiver and range/velocity estimation at the transmitter using OTFS. The algorithm processes received pilot frames for channel estimation at the receiver and data frames echoed from the target/user for range and velocity estimation at the transmitter. A key component in the proposed algorithm is the cancellation of inter-path interference (IPI) in the DD domain. The algorithm works for fractional delay-Doppler which is a source of IPI. The proposed algorithm outperforms other channel estimation schemes and also achieves good root mean square error performance of range and velocity estimation.

A Convolutional Neural Network Combining Classification and Regression for Source Localization in Shallow Water

Machine learning (ML) has been widely applied to ocean acoustic source localization. The localization problem is often regarded as a classification problem or a regression problem in the previous works. This paper proposes a convolutional neural network (CNN) combining classification and regression (CR-CNN) for source localization in shallow water with vertical array data. The normalized sample covariance matrices (SCMs) of the broadband data received by a vertical line array calculated by an acoustic propagation model are used as the input features of the network in the training process. The proposed method is verified by the observation data in the shallow water area of the East China Sea. The results of simulation and real data show that the performance of proposed method is better than that of the separate classification CNN (C-CNN) and MFP. And the localization results of the experimental data are basically consistent with the geoacoustic parameters sensitivity analysis. The depth estimation of the CR-CNN is much better than MFP, while the range estimation performance of CR-CNN, C-CNN and MFP are equivalent.

5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System

The emerging joint sensing and communication (JSC) technology is expected to support new applications and services, such as autonomous driving and extended reality (XR), in the future wireless communication systems. Pilot (or reference) signals in wireless communications usually have good passive detection performance, strong anti-noise capability and good auto-correlation characteristics, hence they bear the potential for applying in radar sensing. In this paper, we investigate how to apply the positioning reference signal (PRS) of the 5th generation (5 G) mobile communications in radar sensing. This approach has the unique benefit of compatibility with the most advanced mobile communication system available so far. Thus, the PRS can be regarded as a sensing reference signal to simultaneously realize the functions of radar sensing, communication and positioning in a convenient manner. Firstly, we propose a PRS based radar sensing scheme and analyze its range and velocity estimation performance, based on which we propose a method that improves the accuracy of velocity estimation by using multiple frames. Furthermore, the Crámer-Rao lower bound (CRLB) of the range and velocity estimation for PRS based radar sensing and the CRLB of the range estimation for PRS based positioning are derived. Our analysis and simulation results demonstrate the feasibility and superiority of PRS over other pilot signals in radar sensing. Finally, some suggestions for the future 5G-Advanced and 6th generation (6 G) frame structure design containing the sensing reference signal are derived based on our study.

Image-based Range Estimation of a Moving Target using Gaussian Process Motion Models

This paper presents a novel range estimation of moving targets observed by a moving camera. The target motions are modeled using Gaussian Processes (GP). Using GP regression, target velocity models of several basic motions are learned a-priori and stored as a library. An interacting multiple model (IMM) filter is then utilized on the perspective dynamical system (PDS) model to estimate the 3D range of the feature points on the moving target. The IMM selects the most likely target motion model from the bank of motion models such that the measurement likelihood is maximized and the relative range state is estimated from the image observations. Simulation results performed using a target motion that is a combination of basic target motions show good range estimation performance in terms of the root mean square error (RMSE) metric.

DCFDA-MIMO: A dual coprime FDA-MIMO radar framework for target localization

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has a range-angle-dependent beampattern by adding the frequency offset across array elements, which can simultaneously locate the target angle and range. In this paper, a novel dual coprime FDA-MIMO (DCFDA-MIMO) radar framework is proposed. It employs a set of coprime frequency offset at the transmitter and receiver to increase the frequency degrees of freedom (DOFs) and improve range estimation performance. Meanwhile, it overcomes the single snapshot problem of conventional coprime arrays in target parameters estimation, and further improves the selectivity of the algorithm. Furthermore, the Cramér-Rao bound and the analytical solution of maximum unambiguity range under this framework are given. In order to solve the range ambiguity problem, the subarrays DCFDA-MIMO structure is designed. The applicability of the proposed framework is proved by numerical simulations using the 2D-MUSIC method, which further demonstrates the superiority of the angle-range resolution and the distinguishable number of the targets.

An Improved ESPRIT-Based Algorithm for Monostatic FDA-MIMO Radar With Linear or Nonlinear Frequency Increments

This letter proposes a search-free algorithm based on ESPRIT for angle and range estimation in monostatic multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA). In this algorithm, the angle is estimated by the ESPRIT algorithm, and the range is calculated by the least-squares principle. Thus, it is suited for FDA-MIMO radar works in linear and nonlinear frequency increments. Besides, the improved algorithm has better range estimation performance than the traditional ESPRIT algorithm. Simulation results prove the effectiveness of the proposed algorithm.

A gridless one-step method for mixed far-field and near-field sources localization

To locate mixed far-field (FF) and near-field (NF) sources, most existing methods require two or three steps to first find the direction-of-arrival (DOA) and then retrieve the range parameters based on the DOA estimates. Hence, the range estimation performance highly relies on the DOA estimation accuracy in these methods. On the other hand, the MUSIC-like or on-grid sparse methods require discretization of the angle and range spaces which may bring in high computations and grid mismatch problem. In this paper, we propose a new method named Gridless OnE-Step (GOES) to jointly retrieve the DOA and range without requiring the pairing and discretization of the angle and range. In particular, we first construct a virtual single-snapshot signal model by the fourth-order cumulants, and then formulate a semidefinite programming (SDP) based on the atomic norm technique to simultaneously estimate the angle and range information. To reduce the computational complexity, we also propose a projected Wirtinger gradient descent algorithm Wirtinger-GOES (W-GOES) for the implementation. Because of the one-step procedure, our method not only has higher resolution than many existing methods in distinguishing both the angles and the ranges of different sources, but also can locate multiple NF or mixed FF and NF sources closely-located in angle domain or even with the same DOA. Extensive simulations are carried out to verify the superiorities of our method.

Fast Range and Motion Parameters Estimation for Maneuvering Targets Using Time-Reversal Process

For maneuvering targets, their motion during long observing time will deteriorate the integration results and degrade the performance of range and motion parameters estimation. To solve this problem, a computationally efficient method based on the time-reversal (TR) process and maximum likelihood (ML) principle, i.e., TR-MLE is proposed. The proposed method decouples the joint parameter estimation problem into two simpler problems, which not only increases the efficiency but also improves the estimation performance in low signal-noise-ratio. Furthermore, a fast method using Chirp-Z transform and Newton's method is developed for a more efficient implementation. The theoretical analysis of the noise properties after the TR process is carried out. Then, the corresponding Cramer–Rao lower bound that can evaluate the performance loss introduced by the TR process is discussed in detail. Simulated data and real data are used to assess the performance of the proposed method.

Combined Wireless Ranging and Frequency Transfer for Internode Coordination in Open-Loop Coherent Distributed Antenna Arrays

Beamforming in distributed antenna arrays requires accurate coordination of the relative phasing between the separate wireless nodes. Principally, this coordination constitutes measuring the node separations and transferring a stable frequency reference, enabling beamsteering and ensuring that the local oscillators on the separate platforms are locked. A 4.5-GHz wireless system for simultaneous high-accuracy estimation of node separation and estimation of a wirelessly transferred 10-MHz frequency reference is presented in this article. Ranging is achieved using a spectrally sparse two-tone waveform with a tone separation of 200 MHz and pulsewidth of $2~\mu \text{s}$ ; a 10-MHz frequency reference is modulated onto one of the ranging tones. The use of spectrally sparse waveforms allows simple transceiver designs compared to wideband ranging systems since the tones can be individually generated and received. The theoretical bounds for range and frequency estimation are derived for a set of common modulation formats, and experimental measurements of ranging accuracy and frequency estimation accuracy are presented, demonstrating that the proposed waveform can achieve high accuracy in both domains, with range accuracy of 3.35 mm and phase errors less than 18°, supporting coherent distributed array operations up to 5.96 GHz. The results show that the ranging estimation is effectively independent of the modulation format of the reference frequency and the frequency estimation performance is independent of the sparse waveform design for ranging. Thus, the performance of range estimation and frequency transfer may be approached independently but in the same waveform.

Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements

This paper studies the processing principles, implementation challenges, and performance of OFDM-based radars, with particular focus on the fourth-generation Long-Term Evolution (LTE) and fifth-generation (5G) New Radio (NR) mobile networks' base stations and their utilization for radar/sensing purposes. First, we address the problem stemming from the unused subcarriers within the LTE and NR transmit signal passbands, and their impact on frequency-domain radar processing. Particularly, we formulate and adopt a computationally efficient interpolation approach to mitigate the effects of such empty subcarriers in the radar processing. We evaluate the target detection and the corresponding range and velocity estimation performance through computer simulations, and show that high-quality target detection as well as high-precision range and velocity estimation can be achieved. Especially 5G NR waveforms, through their impressive channel bandwidths and configurable subcarrier spacing, are shown to provide very good radar/sensing performance. Then, a fundamental implementation challenge of transmitter-receiver (TX-RX) isolation in OFDM radars is addressed, with specific emphasis on shared-antenna cases, where the TX-RX isolation challenges are the largest. It is confirmed that from the OFDM radar processing perspective, limited TX-RX isolation is primarily a concern in detection of static targets while moving targets are inherently more robust to transmitter self-interference. Properly tailored analog/RF and digital self-interference cancellation solutions for OFDM radars are also described and implemented, and shown through RF measurements to be key technical ingredients for practical deployments, particularly from static and slowly moving targets' point of view.

An Efficient Estimator for Moving Target Localization Using Multi-Station Dual-Frequency Radars.

Localization of a moving target in a dual-frequency radars system has now gained considerable attention. The noncoherent localization approach based on a least squares (LS) estimator has been addressed in the literature. Compared with the LS method, a novel localization method based on a two-step weighted least squares estimator is proposed to increase positioning accuracy for a multi-station dual-frequency radars system in this paper. The effects of signal noise ratio and the number of samples on the performance of range estimation are also analyzed in the paper. Furthermore, both the theoretical variance and Cramer–Rao lower bound (CRLB) are derived. The simulation results verified the proposed method.

Low-complexity Spectral Partitioning based MUSIC Algorithm for Automotive Radar

This paper proposes a low-complexity spectral partitioning (SP) based MUSIC algorithm for automotive radar. For short-range radar (SRR), the range accuracy and resolution of 24 GHz ISM band radar should be improved because the requirements of automotive sensing become more demanding over time. To improve the performance of range estimation, high resolution based algorithms such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) and multiple signal classification (MUSIC) have been proposed. However, in a low signal-to-noise ratio (SNR), the high resolution algorithm shows degraded performance to estimate the parameters. To solve this problem, SP based high resolution algorithms have been studied recently. However, for real-time operation, the conventional SP method cannot be applied to automotive radar due to its high complexity. Therefore, a low complexity SP based MUSIC is designed to maintain the performance of the range accuracy and resolution in a low SNR environment. While the complexity of the proposed algorithm is less than the SP-MUSIC, Monte-Carlo simulation results and experimental results show that the estimation performance of the proposed method is similar to that of SP-MUSIC in terms of various parameters.DOI: http://dx.doi.org/10.5755/j01.eie.23.4.18719

Frequency diverse array radar for target range-angle estimation

Purpose Frequency diverse array (FDA) radar with uniform frequency offset between antenna elements has been proposed and investigated, which exhibits a range-angle-dependent beampattern. Nevertheless, because of the coupling in range and angle responses, it cannot estimate directly both the range and angle information of a target.The purpose of this paper is to consider a sub-array scheme of range-angle joint estimation of a target for frequency diverse array (FDA) radar. Design/methodology/approach First, The entire array is divided into two sub-arrays, which employs two different frequency offsets. For aperture extension, each sub-array adopts difference co-array structure . Therefore, the targets range and angle can be estimated directly with the subspace-based multiple signal classification algorithms for the decoupling capability of distance and angle dimensional. The estimation performance is examined by analyzing the Cramer-Rao lower bound (CRLB) versus signal-to-noise ratio (SNR). Findings Each sub-array adopts difference co-array structure to provide degrees of freedom by only physical sensors when the second-order statistics of the received data is used. And the sub-array is equivalent to two sets of equations to solve two unknown quantities, and the closed solution of the unknown quantity can be directly determined, which cannot be gained by the phase-array radar and basic ULA FDA radar. Finally, numerical simulation results verify the validity of the proposed method. Originality/value In this paper, we devise a subarray scheme on FDA radar for range and angle estimation. In order to aperture extension, difference co-array is employed in each subarrays, and more targets can be distinguished than the physical sensors. The range and angle estimation performance is examined by analyzing the CRLB.

GPS Cycle Slip Detection Considering Satellite Geometry Based on TDCP/INS Integrated Navigation.

This paper presents a means of carrier phase cycle slip detection for an inertial-aided global positioning system (GPS), which is based on consideration of the satellite geometry. An integrated navigation solution incorporating a tightly coupled time differenced carrier phase (TDCP) and inertial navigation system (INS) is used to detect cycle slips. Cycle-slips are detected by comparing the satellite-difference (SD) and time-difference (TD) carrier phase measurements obtained from the GPS satellites with the range estimated by the integrated navigation solution. Additionally the satellite geometry information effectively improves the range estimation performance without a hardware upgrade. And the covariance obtained from the TDCP/INS filter is used to compute the threshold for determining cycle slip occurrence. A simulation and the results of a vehicle-based experiment verify the cycle slip detection performance of the proposed algorithm.

Transmit Subaperturing for Range and Angle Estimation in Frequency Diverse Array Radar

Different from conventional phased-array, which provides only angle-dependent beampattern, frequency diverse array (FDA) employs a small frequency increment across the antenna elements and results in a range-angle-dependent beampattern. This beampattern offers a potential to localize the targets in two dimensions in terms of slant ranges and azimuth angles. However, it is difficult to obtain the target location information from a standard FDA radar due to the couplings in range and angle responses. In this paper, we propose a transmit subaperturing scheme on the FDA radar for range and angle estimation of targets. The essence is to divide the FDA elements into multiple subarrays and optimize the transmit beamspace matrix with the use of convex optimization. We also discuss several practical issues for designing the FDA radar system parameters. Since the subarrays offer decoupled range and angle responses, the targets can be located using the beamspace-based multiple signal classification algorithm. The range and angle estimation performance is evaluated by comparing with the Cramer-Rao lower bound.