Range Bins Research Articles

A new approach to atmospheric turbulence measurements using HOSE on radar backscattered echoes

A Novel approach has been proposed for the measurement of turbulent energy dissipation rate (ε) from Nonlinear index (NLI) based measurements using the Higher Order Spectral Estimation (HOSE) technique. The higher order spectral analysis, also known as Bispectrum, is useful for identifying nonlinearities present in the signals. Earlier studies used Bispectrum measurements to experimentally study the spectral energy transfer due to wavenumber interactions in a turbulent flow, which is briefly discussed in the manuscript. This is one of the main motivations for considering HOSE on Atmospheric signals. The Bicoherence obtained by this method has been applied to the backscattered signals received from the Mesosphere, Stratosphere, and Troposphere (MST) radar, Gadanki. Here we considered both convective and clear air atmospheric observations for the analysis and calculated the Nonlinear Index (which represents the amount of nonlinearity in signals) obtained from the Bicoherence for each range bin. We observed that the nonlinearity index indirectly provides information about turbulent intensity. An empirical relationship between the nonlinear index and turbulent energy dissipation rate has been established through regression models. The results of the turbulence energy dissipation rate (ε) obtained from the nonlinear index based method have been compared with the turbulence energy dissipation rate (ε) obtained from the spectrum width based method.

High‐Resolution Sporadic E Layer Observation Based on Ionosonde Using a Cross‐Spectrum Analysis Imaging Technique

AbstractThe appearance of the sporadic E (Es) layer is an occasional phenomenon in the ionosphere. Limited by range resolution, it is difficult to observe the fine structures and internal evolution processes using existing ionosonde detection techniques. In this study, by analyzing the cross‐spectrum of the echo and the modulation sequence of the transmitted signal, the coherence between frequency components in the signal bandwidth is utilized. Using the Capon spectrum estimation method in the range dimension, the range resolution and use efficiency of the ionosonde are improved. This method does not rely on the joint processing of multifrequency detection data, unlike the frequency domain interferometry technique, maintaining the resulting time resolution. Using this imaging method, observations of the Es layer show that multiple peaks within an intrinsic range bin (3.84 km) can be resolved at a range that is 10‐fold higher (384 m); the multilayer phenomenon inside the Es layer can be clearly imaged in a continuous echo. Semishading between the layers is present. In some cases, the mode of the echo traces can also be directly identified through high‐resolution imaging. Concurrently, observations show that dramatic short‐term evolution processes on the order of seconds inside the Es layer can be captured.

Sparse Target Batch-Processing Framework for Scanning Radar Superresolution Imaging

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for two-dimensional echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents batch-processing potential for two-dimensional echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is much suitable for two-dimensional echo data superresolution.

Innovative Cognitive Approaches for Joint Radar Clutter Classification and Multiple Target Detection in Heterogeneous Environments

The joint adaptive detection of multiple point-like targets in scenarios characterized by different clutter types is still an open problem in the radar community. In this paper, we provide a solution to this problem by devising detection architectures capable of classifying the range bins according to their clutter properties and detecting possible multiple targets whose positions and number are unknown. Remarkably, the information provided by the proposed architectures makes the system aware of the surrounding environment and can be exploited to enhance the entire detection and estimation performance of the system. At the design stage, we assume three different signal models and apply the latent variable model in conjunction with estimation procedures based upon the expectation-maximization algorithm. In addition, for some models, the maximization step cannot be computed in closed-form (at least to the best of authors' knowledge) and, hence, suitable approximations are pursued, whereas, in other cases, the maximization is exact. The performance of the proposed architectures is assessed over synthetic data and shows that they can be effective in heterogeneous scenarios providing an initial snapshot of the radar operating scenario.

Body Motion Artifact Cancellation Technique for Cough Detection Using FMCW Radar

A body motion artifact cancellation (BMAC) technique is proposed to detect human cough signals using a frequency-modulated continuous-wave (FMCW) radar. Human coughs are spontaneously accompanied by large-scale body motions, which overwhelm small-scale vibration signals induced by coughing. To mitigate these effects, motion-induced phase variations are estimated and compensated at respective frequencies of an FMCW signal. The cough signals are extracted from range profiles of the phase-compensated FMCW signals. This allows collecting cough signals at a fixed range bin, which are not so much distorted by range migrations due to the large-scale body motions. It is verified through both simulations and experiments. The experimental result shows that it can clearly detect human cough signals by removing body motion artifacts.

Classification Schemes for the Radar Reference Window: Design and Comparisons

In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of the range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors towards more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters.

Range-resolved detection of boundary layer stable water vapor isotopologues using a ground-based 1.98 µm differential absorption LIDAR.

This paper presents a first demonstration of range-resolved differential absorption LIDAR (DIAL) measurements of the water vapor main isotopologue H2 16O and the less abundant semi-heavy water isotopologue HD16O with the aim of determining the isotopic ratio. The presented Water Vapor and Isotope Lidar (WaVIL) instrument is based on a parametric laser source emitting nanosecond pulses at 1.98 µm and a direct-detection receiver utilizing a commercial InGaAs PIN photodiode. Vertical profiles of H2 16O and HD16O were acquired in the planetary boundary layer in the suburban Paris region up to a range of 1.5 km. For time averaging over 25 min, the achieved precision in the retrieved water vapor mixing ratio is 0.1 g kg-1 (2.5% relative error) at 0.4 km above ground level (a.g.l.) and 0.6 g kg-1 (20%) at 1 km a.g.l. for 150 m range bins along the LIDAR line of sight. For HD16O, weaker absorption has to be balanced with coarser vertical resolution (600 m range bins) in order to achieve similar relative precision. From the DIAL measurements of H2 16O and HD16O, the isotopic abundance δD was estimated as -51‰ at 0.4 km above the ground and -119‰ in the upper part of the boundary layer at 1.3 km a.g.l. Random and systematic errors are discussed in the form of an error budget, which shows that further instrumental improvements are required on the challenging path towards DIAL-profiling of the isotopic abundance with range resolution and precision suitable for water cycle studies.

Mainlobe Deceptive Jammer Suppression in SF-RDA Radar: Joint Transmit-Receive Processing

The problem of mainlobe deceptive jammer suppression with a novel stepped frequency (SF)-receive delay array (RDA) radar is addressed in this paper. In our system design, the linear frequency modulation (LFM) waveform with a SF across the antenna elements is transmitted in a uniform linear array, and the received echo is delayed with a time shift between adjacent antenna elements, introducing extra range-dependent degrees-of-freedom (DOFs) in both transmit and receive domains. Consider a typical form of mainlobe jammers, where multiple false targets are generated in the same transmit pulse or same range bin behind the true target after short time modulation. In this respect, the jammers are discriminated from the true target in the joint transmit-receive spatial frequency domain, and suppressed via a data-dependent beamformer based on the minimum variance distortionless response (MVDR) criterion. To further solve the problem of sample selection due to the pseudo-random distribution of the false targets, two kinds of robust beamformers based on the enhanced Capon power spectrum (ECPS) and the eigen-projection elimination (EPE), are developed to reconstruct the jammer-plus-noise covariance matrix and estimate the steering vector of the true target. The performance of the proposed methods in suppression of mainlobe deceptive jammers is evaluated via numerical simulations and compared with the existing systems and robust beamforming methods.

Accurate sensing of multiple humans buried under rubble using IR-UWB SISO radar during search and rescue

Upon the collapse of man-made structures like buildings, bridges, etc., the search and rescue personnel must locate and rescue the buried people. Considering the radar’s beam width, victims may be buried under rubble at the same longitudinal range from the radar and fall into blind spots due to missed detections. Here, we will address this problem efficiently. Experimental work has been undertaken by placing subjects under randomized rubble with an impulse-radio ultra-wideband (IR-UWB) radar placed above the debris. Pre-processing the raw data removes stationary and non-stationary clutter, while the novel variance threshold method proposed herein identified the potential targets. The inter-target dynamic range (ITDR) concept is presented to extract the exact number of targets per range bin by considering the difference in the power level of consecutive dominant target breathing frequencies. Consequently, variable mode decomposition (VMD) is used to extract the breathing signal. An average correlation coefficient of 87.9 % was achieved between the target vital signs from the radar and the respiration belt. Finally, a range compensation algorithm is developed to increase detection efficiency to an average of 7.23 % range error. Thus, multiple targets are successfully extracted with reasonably accurate range and vital signs under varying target postures.

Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO

Frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar makes it transmit beam range–angle two-dimensional freedom by attaching a small frequency offset increment between the array elements. With the widespread use of digital radio frequency memory (DRFM) technology, it is able to delay the false target to any range bin and doppler unit by delaying and forwarding the radar transmits signals, resulting in the deceptive jamming effect of range dimension. To this end, based on the research of the existing main-lobe deceptive interference methods, this paper proposes a new improved MUSIC method based on the FDA-MIMO radar against deceptive jamming in the range dimension. Firstly, the range information of target and jamming is determined through spatial spectrum search by using the mismatch of jamming in the airspace position and range bin position, and then the process of range dimension deceptive jamming suppression is given. The simulation analysis results show that the proposed anti-jamming method can effectively mitigate against range dimension deceptive jamming.

Frame Selection Method for ISAR Imaging of 3-D Rotating Target Based on Time–Frequency Analysis and Radon Transform

When a target has 3-D rotation accompanying roll–pitch–yaw movement, it yields an additional phase component, which cannot be compensated for by any of the existing inverse synthetic aperture radar (ISAR) imaging methods. Under this condition, the resulting ISAR image will be severely distorted. Accordingly, selecting a proper imaging frame, where the target has only 2-D rotation, becomes the only solution, especially for range-Doppler (RD) algorithm. To do this, the conventional approach is to directly use the phase of the received signal, and however, this approach is vulnerable to noise and the cross-term interference of multiple scattering centers (SCs) in the range bin. To address these issues, we propose a new frame selection method using the range-instantaneous Doppler (RID) technique and Radon transform to detect the presence of 3-D rotation of the target within imaging frame. The proposed method comprises three steps: 1) obtain joint time–frequency distribution (JTFD) via the smoothed pseudo Wigner–Ville distribution (SPWVD); 2) estimate the orientation angle of the Radon image of the JTFD spectrum; and 3) select the suitable imaging frame using the estimated angle. Furthermore, to demonstrate the effectiveness of the proposed method, experimental results based on simulated and real measured data are provided.

Research on the Compound Jamming Method against FDA-MIMO Beamforming

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar solves the problem of angle pointing and range pointing during beam transmission due to its unique range-angle dimension dependent, which gives it good characteristics against main lobe deceptive jamming. Moreover, the current single range dimension deceptive jamming is easy to be identified and cannot form an effective jamming effect on FDA-MIMO radar. To address this problem, this paper proposes a compound jamming method of suppression and range deceptive against FDA-MIMO beamforming. Compared with the traditional deceptive jamming method, this method can reasonably utilize the beam gain, and minimum variance distortionless response (MVDR) adaptive beamforming cannot significantly reduce the power of the jamming, thus avoiding the jamming being weakened at the beamforming level, and the jamming may appear in different range bins, causing significant suppression and range deceptive compound jamming effects on FDA-MIMO radar in the range dimension. The simulation verified that the compound jamming method can effectively interfere with the FDA-MIMO radar at the beam-forming level, and the jamming effect is good.

A CFI algorithm for estimating the Doppler peak of wind-profiling radar

ABSTRACT Wind profiling radars are very useful instruments in meteorological forecasting for evaluating the three dimensional wind velocities. The challenging part of this job is to identify the true Doppler peaks of atmospheric origin from a noisy Doppler profile. This paper presents a novel algorithm for the Doppler peak estimation in windprofilers. The normalized Doppler spectrum of each range bin is divided into multiple clusters and low signal to noise ratio (SNR) clusters are discarded. Then, clusters having no continuity along height axis are filtered out and finally the remaining clusters are interpolated to get the wind profile. The algorithm performs very well even when the signal SNR is consistently below −3 dB over successive range bins. The proposed algorithm is found to have improved performance compared to existing algorithms with a reduced computational time.

Interferometric Doppler Velocity Sonar for Low Bias Long Range Estimation of Speed Over Seabed

The long term accuracy (i.e., scale factor) of a Doppler velocity log (DVL) for speed over seabed estimation is studied theoretically, using a simplified 2-D physical model which accurately describes the absorption and terrain biases, for the narrow beamwidths of existing DVLs. The model is used to assess performance limits of existing DVL designs and motivate new designs. A classical piston DVL, which averages the Doppler phase over the entire beam footprint, is compared with the piston of same diameter and operating frequency operated in a new range-gated mode, to reduce the spread in echo strength which biases the speed estimation. The range gating is shown to lead to a very significant reduction of both biases. As an example, the bias resulting from both effects is lower than 0.01% for a range-gated 150-kHz piston of 14-cm diameter operated at 500-m altitude, whereas the corresponding value is 0.8% for the same piston operated conventionally, and 0.15% for a conventional 600-kHz piston of 7-cm diameter. Thus the 150-kHz range-gated DVL has better accuracy than the conventional 600-kHz DVL but much greater operating range. To exploit this benefit of range gating, the direction of arrival of the seabed echoes from the gate must be accurately estimated in the piston frame, e.g., the gate must be centered at boresight to the piston with low bias. It is shown how this can be achieved by an interferometric DVL (InDVL) design which splits the piston into at least two phase centers, and centers the gate using the zero crossing of the differential Doppler phase. It is also shown that, over locally planar seabeds, the residual spread in Doppler phase over the range gate can be removed by replacing the autocorrelation currently used in wideband Doppler processing by the correlation of the seabed echo with a suitably time scaled copy of itself. Finally, a new source of DVL bias, known as diffraction bias, is shown to be the main bias limiting InDVL performance. This is due to a second-order 3-D effect related to the curvature of the range bins not accounted for by the simplified 2-D physical model. For the 150-kHz InDVL, this bias is close to 0.1%, making it the main factor limiting theoretical performance.

A Sea-Surface-Zoning Method Based on Fractal Characteristics

In this paper, a criterion for sea surface zoning based on the fractal characteristics of disturbances is demonstrated. To improve radar detection performance in heavy-tailed sea clutter, a multitude of sea clutter models and corresponding optimum and suboptimum detectors have been designed in recent years. However, there are cases where these models and detectors become insufficient to describe the coexistence of noise and sea clutter. The commonly used signal-to-clutter ratio (SCR) can hardly serve as an indicator revealing which kind of disturbances dominate in a certain area since it is difficult to decide the level of SCR at which sea clutter or noise exceeds the other. Therefore, it is necessary that a set of rules reflecting essential differences between sea clutter and noise are proposed to tell areas where sea clutter dominates, areas where sea clutter and noise coexist and areas where noise dominates. Analyzing fractal characteristics of disturbances, we consider the Hurst exponent H as a feature distinguishing sea clutter and noise from each other. A modified Sigmoid function is employed to model the variation in H with range bins, and the derivative of the function helps to formulate a set of rules for sea surface zoning.

Optimum target range bin selection method for time-frequency analysis to detect falls using wideband radar and a lightweight network

ObjectiveWideband bio-radars have been applied to fall behaviour recognition research in the last few years. The optimum target range bin is that the time Doppler spectrogram image of signals of the range time matrix in this range bin is best for the deep learning network to distinguish between falling and non-falling. Thus, selecting an appropriate target range bin for time-frequency analysis is very important for effective fall detection of wideband radar. MethodsA K-band wideband frequency modulated continuous wave radar is applied to build a fall detection database of 36 subjects in two scenes. The radar data are pre-processed to obtain range-Doppler spectrograms where the optimum target range bin is selected for the subsequent time-frequency analysis. The original, threshold, and binary time Doppler spectrograms are compared using different target range bin optimum target range bin selection methods. To determine the effectiveness of the proposed algorithm in fall recognition, a MobileNetV3-Small architecture is implemented for fall detection by automatically extracting features and classifying them. ResultsThe proposed method distinguishes falls from non-falls with a 98.93% classification accuracy with 5-fold cross validation, which is superior to the existing maximum variance method that is applied to the dataset using three kinds of time Doppler spectrograms. The test results in another scene show that our proposed method is robust enough to detect falls successfully for unseen subjects in unseen situations. ConclusionsThe proposed optimum target range bin selection method can effectively and robustly detect falls when using wideband radar and a lightweight network.

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.

A Parametric Approach to Space-Time Adaptive Processing in Bistatic Radar Systems

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in environments of clutter and jammers. Effective STAP implementations are dependent on an accurate estimate of the space-time covariance matrix, which characterizes noise and interference in the radar signal. Inside-looking monostatic radar systems, the estimate based on secondary radar observations is rather straight forward as all the samples in secondary data can be argued to be from a single distribution, and the sample covariance can be used as an estimate of the space-time covariance matrix. However, in many other radar configurations, the vital underlying STAP training assumption that secondary data are identically distributed is violated, which implies that detection performance can be significantly degraded. This article develops a new method that can be used when secondary data do not share a common distribution due to geometry-induced range dependencies. This phenomenon is of particular concern in bistatic radar systems. We propose a model-based approach, where the distribution of noise and clutter for each range bin is parameterized by a set of scenario parameters. Using secondary data, the scenario parameters are estimated by maximizing the likelihood function. Based on the estimated scenario parameters, the STAP covariance estimate is formed for the cell under test. The presented method is compared with other state-of-the-art methods for bistatic radar STAP via numerical simulations. The simulations indicate that the presented method, with a proper initialization, yields an estimate of the STAP covariance matrix that significantly increases the signal-to-interference-plus-noise ratio compared to the other investigated methods.

Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation

Abstract. The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the Atmospheric LAser Doppler INstrument (ALADIN) Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of ALADIN, the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer's high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented, which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent DWL that was deployed in parallel on board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 to about 1.5 m s−1, demonstrating the capability of such a direct detection DWL. Furthermore, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument's near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m s−1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) winds for ascending and descending orbits, as well as random errors of about 3 m s−1 (HLOS) for the Mie and close to 6 m s−1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality.

An efficient target detection algorithm via Karhunen‐Loève transform for frequency modulated continuous wave (FMCW) radar applications

This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high-performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen - Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input-dependent signals orthogonal to each other under new eigen-basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal-to-noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection (Pd).