Rongqing Xu

Progress in HFSWR research at Harbin Institute of Technology

The Experimental HF Surface Over-The-Horizon Radar set up in the late of 80s has been updated by installation of multi-channel receivers with digitization at the second IF stage, new designed transmitting and receiving antennas, wideband solid-state power amplifiers, frequency synthesizer with low phase noise and flexible signal generator, a new radar controller added to allow the automatic and manual control of the radar, a new designed signal and data processor built mainly with 16bit A/D converters and several multiprocessor boards with Share chip processors added in, and a electromagnetic spectrum monitor equipped in the updated radar system to be able to automatically survey HF spectrum occupancy for providing so called clear frequency channel suitable for radar operation. The purpose of the setting up of the updated radar system is to demonstrate some of new functions of the radar system, and to prove some new techniques to be possibly used in a near future. In the paper, some latest developments of countering measures with radio frequency interferences are presented, too.

Ionosphere clutter suppression based on sparse space spectrum rebuild beamforming

Conventional beamforming method is sensitive to the error of the array system, and easy to cause the desired signal self-cancellation phenomenon, especially when SNR is high. However, the strong ionosphere clutter often appears in the high frequency surface wave radar. These clutter will make the traditional beamforming algorithm ineffective. In this paper, we have investigated a new sparse space spectrum rebuild beamforming (SSSRB) approach to suppress the ionosphere clutter. This method is robustness, and have a high performance. The results of the ionosphere measured data processing show that SSSRB method can suppress the ionosphere clutter effectively, and have a better performance than the Diagonal loading sample matrix inversion(LSMI) method.

ISAR Imaging Based on Sparse Signal Representation with Multiple Measurement Vectors

A new imaging method is proposed for ISAR with multiple measurement vectors (MMV) based on sparse signal representation. As an extension of single measurement sparse signal representation, MMV can enhance the ability of suboptimal procedure to find the proper sparse solution and also offers potential robustness to noise, supposing the measurement vectors having a same sparsity structure. Simulations have shown its superior performance. Here it is first presented for ISAR imaging. Imaging result of real data shows it is a promising method for ISAR

Ionospheric clutter suppression using Wavelet Oblique Projecting Filter

The detection performance of High Frequency Surface Wave Radar (HFSWR) is influenced by the nonstationary ionospheric clutter. For decades, ionospheric clutter suppression is always a challenging problem. Targets and ionospheric clutter have different 2D scale features in Range-Doppler map (RD map) of radar because of different stationarity and range correlation. 2D Discrete Wavelet Transform (2D-DWT) have advantages of separating such nonstationary signals with different 2D scales, so Wavelet Feature Vector (WFV) constructed by 2D-DWT is used to describe this feature in this paper. Based on the nonorthogonality and disjointness of target and clutter subspaces spanned by the WFVs, Wavelet Oblique Projecting Filter (WOPF) method is proposed to suppress the clutter. Experimental results show that this method can suppress clutter and extract targets effectively.

Sea clutter simulation for skywave radar considering ionospheric diffuse scattering

Ionospheric diffuse scattering is an important factor which leads to spread-Doppler clutter (SDC) in skywave radar. However, it is often ignored in a sea clutter model. In this paper, a sea clutter model for skywave radar considering ionospheric diffuse scattering was established. A general assumption about the independence of ionospheric time modulation functions in different spatial positions of the ionosphere is presented. Simulation results show the independence of time functions increases as the distance of their positions increases. It is verified by real data.

Spread-Doppler clutter mitigation based on ionospheric irregularity learning for skywave radar

The detection performance of skywave radar on slow moving targets is essentially limited by the energy of spread-Doppler clutter (SDC). Various factors may lead to SDC. It is hard to adopt a single mitigation strategy for all situations. The idea of different signal processing strategies for different regions is needed to solve such a complex problem. The regions need to be divided first to be processed using the knowledge-aided (KA) approach. The knowledge of different regions can be learned either from auxiliary devices or from data in the radar itself. Adaptive beamforming is rarely used to mitigate sea clutter due to its omnidirectional nature. However, the existence of ionospheric irregularity may give rise to spatial structures in sea clutter. In this work, we demonstrated the use of adaptive beamforming for SDC mitigation for skywave radar when ionospheric irregularity region was learned using correlation analysis with a KA approach. The performance of this approach was further validated by real data processing results.

APES based STAP for target detection in spread-Doppler clutter

Nonstationary spread-Doppler clutter (SDC) within a coherent processing interval often precludes target detection. Across range bins, nonstationarity severely limits the amount of training data available to estimate the clutter covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in High Frequency (HF) hybrid sky-surface wave radar which utilizes the ionosphere as propagation media. This paper applies amplitude and phase estimator (APES) based space-time adaptive processing (STAP), which operates only on the primary data, to reject SDC and to make detection decision. A subspace signal model is assumed to describe the potential target perturbed by ionospheric phase path contamination. According to the simulation and experimental results, the proposed method can achieve better detection performance with single dataset.

TOA-based localization error modeling of distributed MIMO radar for positioning accuracy enhancement

To reduce the influence on localization accuracy resulting from low azimuth angle accuracy, localization with time of arrival (TOA) is proposed for Multiple-input Multiple-output (MIMO) Radar systems. In this paper, the localization error model based on the intersection of Same Delay Contours (SDC) defined by TOA is proposed. Analytic expression for localization Root-Mean-Square error is proposed to study localization performance, and SDC variance zone is proposed as a new approach to evaluate localization performance. Numerical simulations are included to support and corroborate the theoretical developments and analytic results.

Spread-Doppler clutter cancellation in high frequency hybrid sky-surface wave radar

In High Frequency (HF) hybrid sky-surface wave radars, the spread-Doppler clutter (SDC) severely deteriorates the detection performance of low-velocity surface vessels. To suppress SDC, a large number of training samples are usually needed which can be hardly satisfied due to the heterogeneous clutter environment. In this paper, a space-frequency cascaded approach is proposed to suppress SDC using training samples from only one range cell. The proposed method involves two components. First, a frequency domain minimum variance distortionless response (MVDR) weight is designed to cancel the SDC. Second, the spatial orthogonal projection matrix is constructed to estimate clutter covariance matrix, where the signal of interest has been excluded from the training data. According to the experimental results, the proposed method can obtain a more accurate estimation of the clutter characteristics with limited training data and thus achieve better clutter suppression performance.

A novel ionospheric clutter mitigation method through time-frequency image processing based on ridgelet analysis

For Ionospheric clutter is non-stationary and very strong, targets submerged in it is too difficult to extract. The detection performance of High Frequency Surface Wave Radar (HFSWR) is influenced. Ridgelet analysis is a tool for analyzing non-stationary signals and can separate lines with different directions and scales in a 2D image. Therefore, this paper presents a new method which applies ridgelet analysis to time-frequency image of radar signal to mitigate the clutter. The results show that this method can mitigate the clutter effectively, and the target submerged in clutter can be extracted well. When the target submerged in clutter and have low signal-to-clutter ratio, about 15∼20dB improvement can be get after the processing.