Sun Jinping

Motion compensation for Compressive Sensing SAR imaging with autofocus

The high resolution required by various modes of Synthetic Aperture Radar (SAR) results in a huge amount of sampling data, which brings a demand for bigger storage. Recently, a novel SAR concept based on Compressive Sensing (CS) theory asserts that an unknown sparse signal can be recovered exactly with an overwhelming probability even from what appear to be highly sub-Nyquist-rate samples. In this paper, the phase errors caused by radar platform motion are discussed for CS based SAR imaging, and autofocus processing is employed to implement motion compensation. The experiment results show that unfocused SAR images through CS recovery are different from conventional unfocused SAR images for the same motion caused phase error, and usually used Phase Gradient Autofocus (PGA) algorithm is still effective to CS based SAR motion compensation.

The effects of input signal-to-noise ratio on compressive sensing SAR imaging

Synthetic Aperture Radar (SAR) is an active and coherent microwave high resolution imaging system, which has the capability to image in all weather and day-or-night conditions. The high resolution required by various modes of SAR results in a huge amount of sampling data, which brings a demand for bigger storage. Recently, a novel concept based on Compressive Sensing (CS) theory asserts that an unknown sparse signal can be recovered exactly with an overwhelming probability even with highly sub-Nyquist-rate samples. In this paper, a new scheme for the test bed of CS based SAR imaging is proposed. Experimental results on some real raw SAR data reveal that there are some practical limitations on the use of CS based SAR imaging, especially for complex imaging scenes and systems with low Signal-to-Noise Ratio (SNR).

Tracking ground targets with road constraint using multiple hypotheses tracking

Data association in tracking multiple targets is of vital importance for airborne surveillance radar. Multiple hypotheses tracking (MHT) is generally accepted as the preferred method for solving the data association problem in modern multiple-target tracking (MTT) systems. However, there are usually too many hypotheses to process slowly. In this paper, map information has been used to prevent unnecessary MHT track branches and improve the state estimator. The simulation results show that this new algorithm improves tracking performance in realistic environment and effectively brings down the number of track branches, while reducing largely calculating time with better accuracy.

Sensitivity of MIMO STAP Radar with Waveform Diversity

Abstract Space-time adaptive processing (STAP) is an effective method adopted in airborne radar to suppress ground clutter. Multiple-input multiple-output (MIMO) radar is a new radar concept and has superiority over conventional radars. Recent proposals have been applying STAP in MIMO configuration to the improvement of the performance of conventional radars. As waveforms transmitted by MIMO radar can be correlated or uncorrelated with each other, this article develops a unified signal model incorporating waveforms for STAP in MIMO radar with waveform diversity. Through this framework, STAP performances are expressed as functions of the waveform covariance matrix (WCM). Then, effects of waveforms can be investigated. The sensitivity, i.e., the maximum range detectable, is shown to be proportional to the maximum eigenvalue of WCM. Both theoretical studies and numerical simulation examples illustrate the waveform effects on the sensitivity of MIMO STAP radar, based on which we can make better trade-off between waveforms to achieve optimal system performance.

River detection from SAR images

River detection from SAR images plays an important role in civilian applications. A new method for river detection is proposed in this paper, which includes fuzzy clustering, wavelet transform and using snake model. River area can be extracted by clustering and morphological processing. Then the edge of river is extracted with the wavelet modulus maximum method (WTMM), and is smoothed by the snake model. A Radarsat-1 image is used for the experiment. The experimental result shows that the method proposed in this paper is efficient for river detection and edge location.

Research of Small Target Detection within Sea Clutter Based on Chaos

In this paper, based on the prior knowledge of chaotic character of sea clutter, we discuss a method for detection of small target in sea clutter using neural network as a predictor. Neural network can capture the dynamics of strange attractor generating sea clutter. After the reconstruction of real-life Radar data, BP and RBF networks are regularized, and then a set of sample data from real-life data is inputted into the networks to train the neural networks respectively. As a sequence, according the nature of neural networks, these trained networks could approximate the model of dynamical system responsible for sea clutter. These networks can be used to detect small target within sea clutter as a predictor.

Time-frequency analysis based on BLDC motor fault detection using Hermite S-method

Fault signals of brushless DC (BLDC) motors typically are non-stationary. Conventional Fourier transform method cannot matching the demand of extraction of such fault signals. Time-frequency analysis (TFA) based motor fault diagnostics, which can identify effectively rotor faults by detecting time-variant frequency components of stator current signal, such as the dynamic eccentricity and the unbalanced rotor fault, have been important signal processing methods. This paper proposes a TFA based BLDC motor fault detection approach using Hermite S-method. Compared with commonly used short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD), Hermite S-method owns better time-frequency concentration and better cross-term suppression abilities, thereby improving the accuracy of BLDC motor fault detection. Taking rotor dynamic eccentricity fault as an example, the validity of method is demonstrated.

Space-time adaptive processing for sea clutter and jamming suppression in radar seekers

When radar seeker searches and tracks moving target on sea surface, sea clutter spectra severely spread out due to the strong effect of sea conditions. Conventional methods for suppressing sea clutter have achieved poor performance. This paper mainly analyzes the sea clutter characteristics in azimuth-Doppler domain and clutter spectra with different scanning angles. Then space-time adaptive processing (STAP) technique is applied to suppress sea clutter. Research results indicate that sea clutter spectra have some special characteristics in space-time distribution, and the proposed STAP algorithm can detect slow moving targets effectively as it can suppress sea clutter and strong jamming.

A mover detection method based on 2-D fuzzy entropy in SAR images

In this paper, a new technique for the moving targets detection combining incoherent and coherent information of the Synthetic Aperture Radar (SAR) images is proposed based on the measure of a two-dimensional (2-D) fuzzy entropy principle. Firstly, it extracts incoherent and coherent information from images in pixel and builds a 2-D histogram. Secondly, according to 2-D fuzzy function, image pixels are fuzzy classified into two groups: changed and unchanged then 2-D fuzzy entropy is calculated. Finally, Differential Evolution (DE) algorithm is used to maximize the entropy and the optimal parameters of the fuzzy function are obtained, which procures effective moving target detection results.

Estimation of UWB radar scattering center with GTD-based 2D state-space method

A GTD-based 2D state-space method is presented in this paper for estimating the parameters of the scattering centers on a moving target under the ultra-wideband conditions. Firstly, this method constructs the GTD-based impulse response from the moving target in the state space equation, and then arranges the impulse response of different frequency at different time into a Hankel matrix. Next, the range and the range rate are estimated by using the Hankel matrix singular value decomposition (SVD), which effectively solves the range aliasing problem when the ranges of different scattering centers are overlapped. It occurs in the ID state space when the range is estimated only. At last, the type parameter and intensity of the scattering center are solved out by this method. The effectiveness of the method is verified by comparing the result of processing the echo data from the rotating conical target with the CRB on parameters estimation.