range-Doppler Spectrum Research Articles

Clutter Suppression and Target Tracking by the Low-Rank Representation for Airborne Maritime Surveillance Radar

Moving target detection is of vital importance to maritime security and maritime resource protection. However, the detection of slow or weak targets is difficult based on traditional methods. A new detection method is proposed by using the different motion variations of radar moving target and sea clutter in the range-Doppler spectrum sequence. The first step in implementing this method is the separation of moving target and sea clutter by the low-rank representation, in which the target and clutter are modeled as foreground and background components. Subsequently, a sea clutter discriminator is constructed within the sea clutter bandwidth to further remove the sea clutter (false alarms) that exists in the foreground. The proposed method can reduce the sea clutter power while maintaining the target power and improve the detection rate of moving targets, especially slow or weak targets. Data collected with airborne maritime surveillance radar in maritime moving target indication (MMTI) mode are used to validate the performance of the proposed method. The experimental results demonstrate that the improvement in the signal-to-clutter ratio (SCR) obtained with the proposed method is better than that obtained with space-time adaptive processing (STAP, including 1DT-STAP, 3DT-STAP and sparse-STAP) and principal component pursuit (PCP) methods; additionally, the figure of merit (FOM) of the proposed method is higher than that of the constant false alarm rate (CFAR) and PCP method. Furthermore, the tracks of ships are obtained by applying a location constraint to the foreground sequence.

A High-Resolution Framework for Range-Doppler Frequency Estimation in Automotive Radar Systems

In this paper, a signal processing framework using high-resolution algorithms is applied to automotive radar systems for target separation purposes. In many critical use cases, targets sharing similar parameters, i.e., range and relative velocity, can hardly be discriminated in a 2-dimensional (2-D) range-Doppler spectrum via conventional radar signal processing techniques. The proposed framework by taking advantage of a 2-D model-based algorithm with high-resolution capability, i.e., modified matrix enhancement and matrix pencil (MMEMP), is able to separate the overlapping targets by jointly estimating their aforementioned target parameters in a precise manner. Additionally, to reduce computational cost, the presented framework could preselect the peaks (local maxima) of interest via a 2-D model order selection algorithm, i.e., 2-D subspace-based automatic model order selection (2-D SAMOS), and apply the MMEMP only in multi-target situations. Finally, simulation and experiments are carried out to evaluate the performance of the proposed high-resolution target separation framework.

Moving target detection based on OFDM radar

The study combines the general hardware platform of the radar system and wireless communication system, as well as applies it in a real detecting scenario. To detect target, the periodogram-based estimation algorithms based on the signal of orthogonal frequency-division multiplexing (OFDM) is introduced. According to range-Doppler (R-D) spectrum, as an enhanced detection algorithm of constant false alarm rate (CFAR), a radar-target detection method, also referred to as binary successive target cancellation, is presented. This research has important theoretical significance for the communication radar-integrated detection technology, and the research results have essential application value for the technology of radar target detection as well as the fusion and development of wireless communication technology.

Comparative Study on Chaos Identification of Ionospheric Clutter From HFSWR

In addition to target echoes, high frequency surface wave radar (HFSWR) receives sea and ionospheric clutter. Among these clutters, the ionospheric clutter is dominant and significantly affects the detection performance of HFSWR, particularly when the targets are located 100 kilometers away from the radar, rendering it an unsolved problem for HFSWR. Existing studies concerning HFSWR ionospheric clutter lack empirical research on the nonlinear dynamical characteristics of the ionosphere of HFSWR as existing ionospheric suppression methods are still insufficient to adapt the project application. Therefore, the present study utilized the threshold segmentation method to eliminate the sea clutter in HFSWR Range-Doppler spectrum and extracted ionospheric signals from this spectrum by edge feature extraction. Subsequently, the chaotic invariants, such as correlation dimension and the largest Lyapunov exponent of HFSWR ionospheric clutter, were calculated by phase space reconstruction, whilst the chaotic dynamical characteristics of HFSWR ionospheric clutter were determined by the 0–1 test for chaos and other algorithms. Furthermore, the present study demonstrated, for the first time, the chaotic dynamics of the ionospheric clutter of HFSWR with a low-dimensional attractor by processing and analyzing the experimental data from the Weihai High Frequency Radar Station. The conclusion redefines HFSWR ionospheric clutter based on chaotic dynamics rather than regarding it as a stochastic process, which is conducive to efforts to explore the formation mechanism of ionospheric clutter in essence, which can ultimately improve the detection capability of HFSWR, particularly for long-distance targets.

Clutter Suppression Approach for End-Fire Array Airborne Radar Based on Adaptive Segmentation

End-fire array antenna is a kind of special radar antenna with beam pointing perpendicular to the normal direction of the array. Because of its low profile and directional radiation characteristics, it is especially suitable for blind zone compensation of airborne radar. However, there are few publicly available pieces of literature on clutter suppression for end-fire array radar. In this paper, we firstly simulate and analyze the clutter spectrum characteristics for end-fire array airborne radar (EAAR), and then propose a clutter suppression method for EAAR based on adaptive segmentation. In the proposed method, the concept of Riemann mean distance of temporal covariance matrix is introduced to quantify the clutter nonstationarity, and in this way we realize adaptive segmentation of the clutter range-Doppler spectrum. Then the different clutter suppression methods including pulse Doppler (PD) processing, two-dimensional space-time adaptive processing (2D STAP), and three-dimensional STAP (3D STAP) are used to suppress clutter in different regions. The theoretical analysis and simulation results demonstrate that compared with 3D STAP, the proposed method not only guarantees clutter suppression performance, but also reduces the computational complexity to an extent.

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.

Range-Doppler Spectrum Estimation via Sparse variational Bayesian approach

ABSTRACTIn this paper, the structural sparsity of the range profile and the range-Doppler (RD) image is utilised and an effective sparse variational Bayesian approach with modified automatic relevance determination (ARD) is proposed for the RD spectrum estimation. Specifically, a probabilistic model is derived where hierarchical sparse promoting prior is imposed over the scatter coefficients. Since the real and the imaginary parts of the same complex coefficients are strongly correlated, a symmetric construction hyper-parameter is designed. Furthermore, a new hyper-parameter learning rule is induced by optimising the marginal likelihood based on Majorization-Minimization (MM) principle to further improve the accuracy and the efficiency of the proposed algorithm. Finally, the simulation results demonstrate that the proposed algorithm can achieve superior performance and high efficiency in the scenarios of low signal-to-noise ratio (SNR) and limited pulse echoes.

Building Layout Reconstruction in Concealed Human Target Sensing via UWB MIMO Through-Wall Imaging Radar

This letter is devoted to the layout reconstruction via the ultra-wideband (UWB) through-wall imaging radar under one single observation and simultaneously takes account of the real-time human indication. In the proposed framework, layout reconstruction is taken as the preprocessing, where a coherent processing interval consisting of several successive received echoes in the initial stage is first employed to construct a range-Doppler (RD) spectrum. Then, in the RD spectrum, a series of selected discrete Doppler frequency signals is used to form Doppler back projection (BP) images. Finally, in the Doppler BP image stack, we design a 3-D constant false alarm rate detector to extract the building layout. Once completed, the achieved layout as auxiliary information is fused with the simultaneous human indication. Through-wall experiments show that the proposed method can effectively extract the covered layout of multiple walls under one single view and accordingly provide strong support for the concealed human sensing.

Enhanced Target Detection for HFSWR by 2-D MUSIC Based on Sparse Recovery

This letter proposes using the 2-D multiple-signal classification (MUSIC) based on sparse recovery (SR) to improve the target-detection capability of high-frequency surface wave radar (HFSWR). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum, and bearings are then estimated by superresolution methods such as MUSIC. Unfortunately, the conventional cascaded method can easily result in unfavorable deterioration of multitarget detection when different target signals tend to become mixed in the Doppler spectrum. Moreover, sea clutter is an unwanted signal that frequently masks target signals. To enhance the detection of multiple targets and targets embedded in sea clutter, spatial–temporal joint estimation has been proposed. However, because of the lack of spatial–temporal snapshots caused by the nonstationarity of target signals, the efficiency of the estimator cannot be guaranteed. To overcome this shortcoming, multiple-measurement-vector-based SR, which has been used to solve many under-sampling problems in the past ten years, is adopted. Our approach can effectively detect a target embedded in sea clutter as well as multiple adjacent targets and distinguish them from each other. Results obtained using real data with opportunistic targets validate our approach. Therefore, the proposed 2-D SR-MUSIC approach improves target detection and outperforms conventional cascaded methods.

The Vertical Ionosphere Parameters Inversion for High Frequency Surface Wave Radar

High Frequency Surface Wave Radar (HFSWR), which is currently applied in over-the-horizon detection of targets and sea states remote sensing, can receive a huge mass of ionospheric echoes, making it possible for the ionospheric clutter suppression to become a hot spot in research area. In this paper, from another perspective, we take the ionospheric echoes as the signal source rather than clutters, which provides the possibility of extracting information regarding the ionosphere region and explores a new application field for HFSWR. Primarily, pretreatment of threshold segmentation as well as connected region generation is used in the Range-Doppler (R-D) Spectrum to extract the ionospheric echoes. Then, electron density and plasma frequency of field aligned irregularities (FAIs) caused by plasma instabilities in the F region are obtained by the coherent backscattered radar equation. The plasma drift velocity of FAIs can also be estimated from Doppler shift. Ultimately, the effectiveness of inversion is verified by comparing with IRI2012.

Focusing forward‐looking bistatic SAR data with chirp scaling

A bistatic chirp scaling algorithm (CSA) is presented to process the forward-looking bistatic synthetic aperture radar (FL-BiSAR) data. First, the double-square-root equation in the general bistatic range model can be simplified to a square-root equation, when the size of the scene is small enough. Based on the model, the range Doppler spectrum is derived. Then, a CSA is obtained. The range model is proved to be an effective proxy by a simulation.

Computationally Efficient RF Interference Suppression Method With Closed-Form Maximum Likelihood Estimator for HF Surface Wave Over-The-Horizon Radars

Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called RFI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS</sub> ) in RDS is also frequently observed. This work concentrates on the new RFI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS</sub> and establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University.

On the mitigation of wind turbine clutter for weather radars using range‐Doppler spectral processing

The unwanted return signals from wind turbines can contaminate the weather-radar data that are used by forecasters and automatic algorithms to issue forecast and warnings for severe weather. Since wind turbines have moving components that generate return signals with non-zero Doppler velocity, traditional ground clutter filters are ineffective at removing wind turbine clutter (WTC). In this study, a WTC mitigation algorithm using the range-Doppler spectrum is developed and tested with simulated weather and WTC signals. Once the general locations of the WTC contamination are known, the proposed range-Doppler regression (RDR) algorithm exploits the spatial continuity of weather signals in the range domain to mitigate the WTC contamination while retaining as much weather signal as possible. In contrast to other proposed mitigation algorithms, the RDR algorithm is suited for real-time implementation on typical operational weather radars. Simulated data are used to optimise the parameters of the algorithm and evaluate its performance for stratiform- and convective-precipitation cases with different degrees of WTC contamination. Finally, a real data case is processed to illustrate the RDR algorithm's effectiveness. The results show that the RDR algorithm has the potential to effectively reduce the bias in spectral-moment estimates caused by WTC contamination in an operational environment.

HF radio measurements of surface currents

HF radio waves backscattered from the ocean surface can be used to measure ocean surface currents. Measurements of the range-Doppler spectrum of these signals yield the wavenumber k and the frequency ω of an ocean surface wave and its phase velocity ν = ω/ κ relative to the radar. Subtraction of the phase velocity of the wave in still water, c = √( g/ k), yields a measure of the average current from the surface to a depth of the order of (2 k) −1. A measure of the current shear is obtained by observing at more than one radio frequency. To test these ideas, surface currents were measured using both a conventional and the HF technique, and reasonable agreement was found.