Adjacent Range Cells Research Articles

On the Interpretation of Coherent Marine Radar Backscatter From Surf Zone Waves

Observations of microwave backscatter from shoaling and breaking surface waves acquired with a shore-based, coherent-on-receive X-band marine radar are presented. The radar was located at the dune cliff of a sandy beach with two breaker bars. Waves were approximately shore-normal (inclination < 10°) during the study period. Consistent with other studies, the backscatter intensity from breaking waves is significantly increased ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 10$ </tex-math></inline-formula> dB compared to nonbreaking) with Doppler velocities close to the wave phase velocity in shallow water. The strong backscatter from active breakers can cause a significant amount of signal artifacts due to the leakage of pulse energy into adjacent range cells, in particular behind the breaking crests. In the near range, the backscatter from the undisturbed surface and such pulse smearing artifacts appear as distinct peaks inside the Doppler spectra. Thus, the velocity of both sources of scatterering can be retrieved using a dedicated peak separation algorithm. In the far range ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r &gt; 500$ </tex-math></inline-formula> m), the artifacts dominate the Doppler signal behind breaking wave crests. Therefore, when investigating the spatio-temporal evolution of breaking wave-induced Doppler velocities with marine radar, the analysis should be restricted to the wave crests and the well-illuminated front faces of the waves. The evolution of Doppler spectra tracked along the crest of an exemplary individual breaking wave is extracted during the steepening, active- and post-breaking stage.

Radar High-Resolution Range Profile Ship Recognition Using Two-Channel Convolutional Neural Networks Concatenated with Bidirectional Long Short-Term Memory

Radar automatic target recognition is a critical research topic in radar signal processing. Radar high-resolution range profiles (HRRPs) describe the radar characteristics of a target, that is, the characteristics of the target that is reflected by the microwave emitted by the radar are implicit in it. In conventional radar HRRP target recognition methods, prior knowledge of the radar is necessary for target recognition. The application of deep-learning methods in HRRPs began in recent years, and most of them are convolutional neural network (CNN) and its variants, and recurrent neural network (RNN) and the combination of RNN and CNN are relatively rarely used. The continuous pulses emitted by the radar hit the ship target, and the received HRRPs of the reflected wave seem to provide the geometric characteristics of the ship target structure. When the radar pulses are transmitted to the ship, different positions on the ship have different structures, so each range cell of the echo reflected in the HRRP will be different, and adjacent structures should also have continuous relational characteristics. This inspired the authors to propose a model to concatenate the features extracted by the two-channel CNN with bidirectional long short-term memory (BiLSTM). Various filters are used in two-channel CNN to extract deep features and fed into the following BiLSTM. The BiLSTM model can effectively capture long-distance dependence, because BiLSTM can be trained to retain critical information and achieve two-way timing dependence. Therefore, the two-way spatial relationship between adjacent range cells can be used to obtain excellent recognition performance. The experimental results revealed that the proposed method is robust and effective for ship recognition.

Adaptive Detection of Dim Maneuvering Targets in Adjacent Range Cells

This letter addresses the detection problem of dim maneuvering targets in the presence of range cell migration. Specifically, it is assumed that the moving target can appear in more than one range cell within the transmitted pulse train. Then, the Bayesian information criterion and the generalized likelihood ratio test design procedure are jointly exploited to come up with six adaptive decision schemes capable of estimating the range indices related to the target migration. The computational complexity of the proposed detectors is also studied and suitably reduced. Simulation results show the effectiveness of the newly proposed solutions also for a limited set of training data and in comparison with suitable counterparts.

Space-Time Adaptive Processing by Employing Structure-Aware Two-Level Block Sparsity

Traditional radar space-time adaptive processing (STAP) cannot efficiently suppress heterogeneous clutter because of a small number of independent and identically distributed training snapshots. In the article, we propose a new STAP approach exploiting structure-aware two-level block sparsity (STBS) of radar echoes, namely STBS-STAP. It enhances the performance on clutter suppression and target detection with limited training snapshots. The clutter angle-Doppler profile always appears in a continuous diagonal clustering structure and the radar echoes at the adjacent range cells commonly share the same sparse pattern. STBS-STAP employs STBS, i.e., both the diagonal clustering structure and the common sparsity property, to acquire a precise clutter covariance matrix estimation. Thus, the new STBS-STAP achieves better performance on clutter suppression compared with existing STAP methods with a small number of training samples. Besides, STBS-STAP achieves superior target detection performance due to the precise estimation of the statistical properties of the clutter. The superiority of STBS-STAP is verified by experiments on both simulated data and measured Mountain-Top data.

Adaptive detection for passive radars using the structure of interference covariance matrix

A two-step generalized likelihood ratio test (GLRT) is proposed for adaptive detection in passive radars using the mixed-signal model. The detector is derived based on a hypothesis test in which the spill-over of target energy into several mixed signals, and the structured correlation between the mixed signals corresponding to the adjacent range cells are considered. The detection performance is evaluated by simulations. Moreover, two upper bounds on the performance are extracted analytically. The results show that the detection performance of the detector is improved with respect to the previously proposed adaptive detection algorithms for passive radars.

P.580 The impact of mental disorders on sexual health: a retrospective study

In this paper, we consider the range distributed target detection in partially homogeneous (PH) clutter which displays different statistical properties in adjacent range cells. We propose a group method that adjacent cells with slightly varying statistics are divided into a group. Given the cells group effects on deducing the generalized likelihood ratio test (GLRT), three detectors: one-step group GLRT (1S-G-GLRT), maximum a posteriori estimation group GLRT (MAP-G-GLRT) and two-step group GLRT (2S-G-GLRT) are developed. It is verified that the 1S-G-GLRT and 2S-G-GLRT are constant false alarm rate (CFAR) with respect to the scale parameter of texture and the estimated speckle covariance matrix. The experiments show that, in the simulated clutter, the three proposed detectors behave approximately similarly, all of them outperforming three existing detectors remarkably despite the effects of target models and group strategies. In the real clutter, the 1S-G-GLRT and MAP-G-GLRT have advantages over the detectors without grouping in PH real clutter.

Range-Recursive IAA for Scanning Radar Angular Super-Resolution

Recently, the iterative adaptive approach (IAA) was adopted to allow for the estimation of high-resolution scanning radar images. In this letter, we further develop this approach by introducing a range-recursive IAA (IAA-RR) formulation allowing for a computationally efficient updating of the resulting estimates along range. Besides exploiting the rich matrix structure to mitigate the computational complexity for each iteration, the correlation between adjacent range cells is exploited to accelerate the convergence of the IAA iterations. When an additional range measurement becomes available, further acceleration is available by exploiting the estimates already formed for the adjacent range cells. Compared with the existing fast IAA implementation, the proposed IAA-RR is shown to offer significant computational savings, without noticeable loss in performance. Numerical results illustrate the superior performance of the proposed IAA-RR algorithm.

Three GLRT detectors for range distributed target in grouped partially homogeneous radar environment

In this paper, we consider the range distributed target detection in partially homogeneous (PH) clutter which displays different statistical properties in adjacent range cells. We propose a group method that adjacent cells with slightly varying statistics are divided into a group. Given the cells group effects on deducing the generalized likelihood ratio test (GLRT), three detectors: one-step group GLRT (1S-G-GLRT), maximum a posteriori estimation group GLRT (MAP-G-GLRT) and two-step group GLRT (2S-G-GLRT) are developed. It is verified that the 1S-G-GLRT and 2S-G-GLRT are constant false alarm rate (CFAR) with respect to the scale parameter of texture and the estimated speckle covariance matrix. The experiments show that, in the simulated clutter, the three proposed detectors behave approximately similarly, all of them outperforming three existing detectors remarkably despite the effects of target models and group strategies. In the real clutter, the 1S-G-GLRT and MAP-G-GLRT have advantages over the detectors without grouping in PH real clutter.

Radio frequency interference suppression in high frequency surface wave radar based on range–domain correlation

High frequency surface wave radar (HFSWR) is often subject to the serious impact of external radio frequency interference (RFI), which must be suppressed efficiently in order to improve the viability of the radar. In this paper, we consider two different types of RFIs. One has a strong range–domain correlation at all range cells, while the other has a strong range–domain correlation only at the adjacent range cells. According to the difference on range–domain correlation between these two types of RFIs, we propose two different suppression methods based on the range–domain adaptive filtering. One is the far range cells method which uses the signals at the far range cells as the secondary data, and the other is the adjacent range cells method which uses the signals at the adjacent range cells as the secondary data. In the practical processing, we should select the far range cells method when the RFI has a strong range–domain correlation at all range cells. Otherwise, we should select the adjacent range cells method. Processing results of real data are presented to demonstrate the effectiveness of these two methods.

Localization Strategies for Multiple Point-Like Radar Targets

In this correspondence, we focus on the design and the analysis of schemes aimed at estimating the position of multiple point-like targets that fall among three adjacent samples of the matched filter output (typically present in the processing chain of a radar). To this end, we exploit spillover of targets' energy to adjacent range cells. The number of targets (and the corresponding Doppler frequency shifts) is assumed to be known. Moreover, we suppose that received useful signals can be modeled in terms of vectors known up to deterministic parameters and that they are embedded in correlated Gaussian noise with unknown covariance matrix. For estimation purposes we assume that a set of secondary data, free of signal components, but sharing the same covariance matrix of the noise in the cells containing signal returns, is available. The analysis, also in comparison to a possible competitor, proves the superiority of multitarget schemes with respect to single target ones, even under reasonably mismatched scenarios.

ADAPTIVE CLUTTER SUPPRESSION FOR AIRBORNE RANDOM PULSE REPETITION INTERVAL RADAR BASED ON COMPRESSED SENSING

We present an adaptive clutter suppression method for airborne random pulse repetition interval radar by using prior knowledge of clutter boundary in Doppler spectrum. In this method, by exploiting the intrinsic sparsity, compressed sensing based on iterative grid optimization (CS-IGO) is applied to directly recover the clutter spectrum with only the test range cell instead of nonhomogeneous training data from adjacent range cells. Since the sensing matrix and clutter spectrum obtained by CS-IGO are well adapted to the data, the prewhitening fllter can be efiectively obtained to cancel the mainlobe clutter. Further, the clutter residue can be suppressed by the iterative reweighted l1 minimization to enhance the target response. Simulation results show that the approach is capable of efiective suppression of clutter and precise recovery of targets' unambiguous spectrum, ofiering a high performance of output signal to clutter and noise ratio.

Small-Target Detection in High-Resolution Heterogeneous Sea-Clutter: An Empirical Analysis

This paper presents the results of an extensive experimental parametric analysis of coherent radar detectors exploiting a new Ka-band high-resolution sea-clutter database. The statistical analysis of the submeter range resolution real data shows strong fluctuations of both the spectral shape and power between adjacent range cells. This highly heterogeneous dataset is therefore ideally suited to characterize the performance degradation to be expected in real conditions, relative to the homogeneous finite-sample-support case. The impact of spectral heterogeneity within the reference window of the detectors is quantified by resorting to simulated datafiles. The analysis focuses on both the experimental constant false alarm rate (CFAR) behavior and the synthetic Swerling I target probability of detection. Finally, a comparison with a practical detection scheme is included, allowing the trade-off between increased computational complexity and improvement in performance over conventional approaches to be assessed.

Track-Before-Detect Algorithms for Targets with Kinematic Constraints

We propose and assess new algorithms for adaptive detection and tracking based on space-time data. At design stage we take into account possible spillover of target energy to adjacent range cells and assume a target kinematic model. Then, resorting to the generalized likelihood ratio test (GLRT) we derive track-before-detect (TBD) algorithms that can operate in scan-to-scan varying scenarios and, more important, that ensure the constant false track acceptance rate (CFTAR) property with respect to the covariance matrix of the disturbance. Moreover, we also propose CFTAR versions of the maximum likelihood-probabilistic data association (ML-PDA) algorithm capable of working with data from an array of sensors. The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that the proposed TBD structures outperform the ML-PDA implementations especially in terms of probability of track detection (and for low signal-to-noise ratio (SNR) values).

Clutter suppression for airborne phased radar with conformal arrays by least squares estimation

The radar clutter statistics for airborne conformal arrays varies by range, i.e., the clutter distributions are nonstationary, which causes performance degradation for the conventional space–time adaptive processing (STAP), which estimates the clutter covariance matrix (CCM) from data at adjacent range cells. In this paper, a novel clutter suppression method for airborne phased radar with conformal arrays is proposed that takes a form of corrected sample matrix inversion (SMI) through the CCM estimated by the least squares (LS) estimation. The estimated CCM can provide partial information about the real CCM in the novel method, which results in improved detection performance for targets in conformal array applications. Simulation results relative to several typical conformal arrays verify the effectivity of the presented method.

Target detection in high-resolution sea clutter via block-adaptive clutter suppression

Attentions have been focused on the moving target detection in a high-resolution sea clutter. This study commences with a proposal of median-based estimator to estimate the power spectrum of high-resolution sea clutter by the time series observed in adjacent range cells and time intervals. The estimator provides a robust estimation when just a few aberrant time series happen in observation. Based on the estimator, a block-adaptive clutter suppression filter (BACSF) is designed to suppress the clutter prior to the pulse integration. Then, the residual clutter, the output of the BACSF, is modelled as spherically invariant random vector. Upon applying an adaptive normalised matched filter (ANMF) to the residual clutter, a residual clutter's ANMF detector is derived. Moreover, in high-resolution radar background, considering that the approximately stationary intervals of sea clutter and residual clutter are much shorter than the coherent processing interval, another heuristic block-ANMF detector is proposed. It can integrate more pulses and can achieve better performance than the ANMF detector does. This study concludes with the experiments of simulated target against the real sea clutter. The experimental results demonstrate that, when target's Doppler frequency is beyond strong clutter region, the ANMF detector and block-ANMF detector perform better in the residual clutter than in the clutter.

An effect of range-heterogeneous clutter on adaptive Doppler filters

The practical implementation of adaptive Doppler filters requires estimates of clutter parameters to determine the adaptive weights. A method of deriving the estimate via the sample matrix inversion (SMI) algorithm using multiple data snapshots from adjacent range cells is presented. For homogeneous clutter environments, the results of this technique asymptotically approach the optimum (a priori known covariance matrix) as the number of snapshots approaches infinity; this asymptotic behavior does not occur for heterogeneous clutter environments. An equation for the decrease in improvement factor is derived. To promote understanding, the simplified special case of narrowband clutter is considered in detail. In almost all cases, the loss is small. >