Bingqi Zhu

Optimal radar waveform design for moving target

Radar performance improvement through waveform optimization has been an ongoing topic of research recent years. In this paper, we use the optimal waveform design method to deal with the moving target in the clutter and noise. Neyman-Pearson detector criterion is used to maximize the probability of target detection. The optimal waveform is then designed theoretically corresponding to the velocity of target and clutter/noise power spectrum density. Simple CW signals can produce maximum detectability based on different noise PSD situations. Simulated results are presented based on our method and improvement in image is approached. Finally, the conclusions are drawn based on our analysis and simulations.

Synthetic aperture radar imaging algorithm customized for programmable optronic processor in the application of full-scene synthetic aperture radar image formation

Abstract. With the high programmability of a spatial light modulator (SLM), a newly developed synthetic aperture radar (SAR) optronic processor is capable of focusing SAR data with different parameters. The embedded SLM, encoding SAR data into light signal in the processor, has a limited loading resolution of 1920×1080. When the dimension of processed SAR data increases to tens of thousands in either range or azimuth direction, SAR data should be input and focused block by block. And then, part of the imaging results is mosaicked to offer a full-scene SAR image. In squint mode, however, Doppler centroid will shift signal spectrum in the azimuth direction and make phase filters, loaded by another SLM, unable to cover the entire signal spectrum. It brings about a poor imaging result. Meanwhile, the imaging result, shifted away from the center of light output, will cause difficulties in subsequent image mosaic. We present an SAR image formation algorithm designed to solve these problems when processing SAR data of a large volume in low-squint case. It could not only obtain high-quality imaging results, but also optimize the subsequent process of image mosaic with optimal system cost and efficiency. Experimental results validate the performance of this proposed algorithm in optical full-scene SAR imaging.

Waveform design based multi-target hypothesis testing under unknown clutter parameters

A method to solve multi-target classification problems with unknown clutter parameters is proposed in this paper. The unknown parameter is estimated and synthesized at each observation, and probability of each hypothesis is updated. Subsequently, the optimal waveform for the next illumination is designed based on NP criteria, and the final decision is made based on the sequential probability ratio testing. Simulated results are presented based on our method and show that the optimal waveform-based sequential testing can be decided through reduction of the average illumination number. Furthermore, results indicate a significant improvement over the non-optimal waveforms.

Optimal waveform-based clutter suppression algorithm for recursive synthetic aperture radar imaging systems

Abstract. A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.

SAR clutter suppression using recursive waveforms

In this paper, we combine the waveform design method with the synthetic aperture algorithm to suppress clutter and generate clear microwave images of targets. The linear recursive model is introduced into the SAR operation principle and Kalman filter algorithm is used to estimate target and clutter responses in each azimuth direction based on their states before, which both are assumed to be Gaussian distributions. Optimal waveforms based on NP criteria are designed repeatedly and used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. The simulations show that our algorithm can significantly reduce the clutter response while targets are imaged.

A SAR imaging technique for the target of complex azimuth envelope based on information extraction

This paper talks about an innovative method for constructing SAR image from raw data when range migration phenomenon is obvious. Here, SAR raw data works as input. A set of range migration curves (RMC) will be extracted from its range compression result. After that, parameters gained from curve fitting provide physical location and normalized backscattering coefficient of targets, and thus a SAR image is constructed. In this algorithm, Random Sample Consensus (RANSAC) offers idea about pixels classification of different point targets. Moreover, the least-squares distance gives measure to classify pixels belonging to different point targets. It is shown that range migration curve can be regarded as a parabola when estimating the parameters by curve fitting. These estimated parameters construct SAR image well when the conventional SAR image formation algorithm fails in obtaining a good one. In this paper, experimental results are conducted to illustrate the superiority of the algorithm.

Waveform design for deterministic binary targets

A method for optimal waveform-based test to solve the binary targets classification problem is proposed in this paper. The two targets responses are all assumed to be deterministic vectors, and Neyman-Pearson criteria-based optimal waveform design method is used to determine which of these two targets is in the illumination scene. By this method, we can make final decision by using designed waveform. Simulated results are presented not only on the comparison of the linear frequency modulation (LFM) signals with the designed waveforms, but also the performance by using optimal waveforms. Finally, the conclusions are drawn based on our analysis and simulations.

Improved clutter suppression for SAR imaging based on optimal waveform design method

In this paper, we proposed a clutter suppression algorithm for SAR imaging due to different target power spectrum density (PSD) and clutter PSD in azimuth direction. The optimal waveform of the SAR system is designed according to the prior-knowledge of the target and clutter, an amplitude limiter set in frequency domain is used to suppress the clutter response and 2-dimentional pulse compression is then used to the SAR imaging. Simulated results are presented based on our method which is shown great improvement in target image over clutter image. Then conclusions are drawn based on our analysis and simulations.

Complex target-induced azimuth envelope reconstruction from SAR RAW data

An innovative algorithm to reconstruct complex target-induced azimuth envelope in synthetic aperture radar (SAR) system is proposed. Unlike the assumption in conventional SAR imaging algorithms, targets backscattering coefficient can hardly remain constant when synthetic aperture time is long enough. In order to fully understand the target feature, we extract both amplitude and phase information of target-induced azimuth envelope from SAR raw data. In this paper, we formulate range migration curve (RMC) with a parametric model and implement curve fitting to estimate these parameters. The input pixels of curve fitting process is extracted from range compression result of raw data. Applying the idea of random sample consensus (RANSAC), this algorithm classifies pixels according to the targets RMC they belongs to, and extracts targets feature information at the same time. Experimental results are conducted to validate this algorithm.

A clutter suppression algorithm based on optimal waveform design for sar imaging

A clutter suppression algorithm for SAR imaging is proposed in this paper. The optimal waveform of the SAR system is designed according to the prior-knowledge of the target and clutter, an amplitude limiter set in frequency domain is used to suppress the clutter response and 2-dimensional pulse compression is then used to the SAR imaging. With the help of this algorithm, the SAR sensor has a strong adaptive capacity to the environment. Simulated results are presented based on our method and improvement in image is approached. Finally, the conclusions are drawn based on our analysis and simulations.