Cochannel Interference Suppression for Ship-Based Passive Synthetic Impulse and Aperture Radar

We propose Deep Learning (DL) as a framework for simultaneous waveform estimation and image reconstruction in passive synthetic aperture radar (SAR). We consider a passive imaging scenario where the scene is illuminated by source of opportunity with known position, but transmitting an unknown waveform. Image reconstruction is then posed as a machine learning task for which a recurrent neural network (RNN) can be constructed by unfolding the iterations of a proximal gradient descent algorithm. We separate the unknown waveform from the known components of the forward model and parameterize the network operator by the waveform coefficients. The image estimate generated by the RNN is then mapped back to the data space by a linear decoder stage, so that the waveform coefficients are refined in an unsupervised manner using passive SAR measurements as training data. As a result, network becomes a recurrent auto-encoder. In this architecture, forward propagation recovers scene reflectivity, and backpropagation solves for waveform coefficients. The non-convex training routine is initialized using the partially known SAR forward model, which helps ensure that the optimization problem converges to a satisfactory optimal point.

Most of the existing through-the-wall imaging (TWI) methods using synthetic aperture radar (SAR) tend to apply an active system. In this paper, a novel, passive SAR (PSAR), termed TWI-PSAR, is proposed, to focus the image of multi targets behind a single-/multilayered wall. TWI-PSAR would work in a bistatic configuration using wideband sources of opportunity and a single moving platform or a stationary linear array receiver. Incident angle and frequency are considered the parameters that influence TWI image directly. A stepped frequency transmitter with single incident angle is applied to investigate the incident angle effect. It could show the capability of small angle to suppress wall effects. Zero incident angle PSAR (Z-PSAR) is exploited in TWI for enhanced target identification and feature extraction as well as wall effect mitigation. In scenarios where background measurement might not be available or wall parameters are unknown for compensation, Z-PSAR could be adopted. Compared to other conventional imaging methods such as SAR and time reversal, numerical results show the superiority of the proposed TWI system in urgent situations with unknown wall parameters, employing free-space Green’s function. Moreover, to demonstrate the effectiveness of the proposed PSAR method in a real situation, sources of opportunity that are relatively wideband and aligned in several directions, such as analog TV, Digital Video Broadcasting—Terrestrial, GSM, and WiMAX, are used to image targets behind the wall. Also, Monte Carlo method is used to show the effectiveness of TWI-PSAR in different frequency and incident angle scenarios.

This chapter discusses the passive inverse synthetic aperture radar (SAR) (ISAR) (P-ISAR) technique. Radar imaging, and in particular P-ISAR, is a new feature for passive radars (PRs) made possible by the recent technological advances. This chapter presents the theory of the P-ISAR algorithm. It also discusses the issues related to the application of the ISAR technique to passive coherent location (PCL) systems and in particular the obtainable spatial resolutions. How to deal with this problem is also discussed in this chapter. Finally, results on real data from two different PCL systems are presented to demonstrate the feasibility and effectiveness of P-ISAR. This chapter is organized as follows. Section 4.1 introduces the P-ISAR concepts and its main 'ingredients'. Section 4.2 deals with the data preprocessing that includes all the steps necessary to obtain data suitable for the application of ISAR technique. Section 4.3 presents the ISAR image processing algorithm. It also proposes a method for handling incomplete data, particularly data with frequency ranges, that occurs when a full bandwidth signal is not available. Section 4.4 presents the results obtained by processing two datasets gathered with two different PCL systems. Main conclusions are finally drawn in Section 4.5.

Passive synthetic aperture radar (SAR) using Global Navigation Satellite System (GNSS) signals as the illuminators of opportunity has a relative coarse range resolution due to the narrow bandwidth of the navigation signals and the bi-static acquisition geometry, limiting its application for high-resolution requirement. In this paper, an image formation algorithm for range resolution improvement is investigated. A modified second order differentiation based method is applied on the range compressed signal of reference channel and surveillance channel, improving range resolution and keeping side-lobes at a lower level simultaneously, and the phase of processed signal is preserved by phase multiplication compensation for the following azimuth compression processing. To validate the effectiveness and benefits of the proposed algorithm in terms of range resolution and targets side-lobes, point targets simulation under different situations and real field experiment with strong scatters are conducted and discussed. Compared with the traditional GNSS-based passive SAR imaging algorithm, the results of the proposed method show that the imaging performance can be improved for better range resolution and lower side-lobes, benefitting for better distinguishing nearby targets and recognizing weak targets.

In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors. Keywords: sea ice, data assimilation, synthetic aperture radar, binary, Bayesian, probability, forward model (Published: 18 September 2015) Citation: Tellus A 2015, 67, 27218, http://dx.doi.org/10.3402/tellusa.v67.27218

This letter demonstrates the feasibility of improving range resolution in passive synthetic aperture radar with the Galileo navigation satellites as transmitters of opportunity. This can be done by an appropriate coherent combination of the Galileo E5a and E5b signals. The proposed approach is analytically derived and is confirmed both by simulation and experiments.

In this paper, a new method is presented for image formation in a Multistatic Passive Synthetic Aperture Radar (SAR), whose receiver is moving and transmitters are stationary in a multistatic structure, and the signal used to create the image is DVB-T. The proposed method uses multiple transmitters of opportunity to improve the range resolution of a Passive SAR. First, a linear observation model is developed for multistatic structure of the Passive SAR system. Then, the image of the underlying scene is obtained through a new Kalman-based estimation algorithm. In this algorithm, range migration error is completely corrected, and the use of Kalman recursive filter reduces the computational complexity of the reflection coefficients estimation algorithm. Experimental results indicate that the proposed algorithm is effective.

The phase gradient autofocus (PGA) is a state of art technique for phase error correction of synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR). It exploits the redundancy of phase error information among range bins by selecting the strongest scatter for each range bin and then synthesizes them. The motivation of this paper is based on the observation that the redundancy of phase error information is also among the cross-range (azimuth) direction in the image domain. A new scatter selection scheme is proposed to better utilize the phase error information contained in the image. The validity of our new prominent scatters selection method for PGA algorithm is theoretically analyzed and tested with some numeral simulation. It shows that a faster convergence speed can be obtained by our new method.

Transform coding based on the Karhunen-Loeve transform (KLT), the discrete cosine transform (DCT), and the discrete wavelet transform (DWT) is well-understood for optical images. Transform coding applied to synthetic aperture radar (SAR) data, however, has not been well-studied. This paper compares the results of compressing SAR images using KLT, DCT, and DWT coders. We compare the compression results based on six performance criteria-mean-squared error, mean absolute error, peak signal-to-noise ratio, energy compaction, transform gain, and compression ratio.

Doppler centroid is an important parameter in synthetic aperture radar (SAR) image processing. Owing to measurement uncertainties, many clutter-lock algorithms are proposed to estimate the Doppler centroid. One of the factors that affect the estimation is ‘chirp coupling’ [linear FM signal is used as transmitting signal in SAR, and this makes echoes of targets from different range bins overlap with each other at the same range bin, which could be called ‘chirp coupling (CHCO)’]. In broadside or lowly squint mode SAR, CHCO is often ignored. However, with large squint angle, its influence on Doppler centroid estimation will increase and even affect the final image. The azimuth-frequency shift of echoes caused by CHCO is analysed and a method proposed to calculate the threshold angle which is used to judge its influence on Doppler centroid estimation. With the help of the threshold angle, a novel strategy of Doppler centroid estimation is proposed. After theoretic analyses, simulations and experimental results are provided to demonstrate the proposed strategy of Doppler centroid estimation.

Synthetic Aperture Radar (SAR) echo simulation is an effective tool of the radar system design, which enables us to validate imaging algorithms and evaluate the performance of SAR systems before actual use. The increasing demand of higher SAR resolution makes the SAR simulation more and more complicated. The traditional simulation methods are not precise enough so that a new simulation method applicable to most complicated targets is needed. This paper demonstrates a novel simulation method based on the theory of electromagnetic scattering which simulates the received echo data through taking use of the backscattered electric field. Our simulation method can be used to simulate a complicated target in SAR simulation. This method opens a door to conducting SAR echo simulation with theory of signal and system and the electromagnetic scattering taken into consideration, though a few improvements are needed to increase the performance of the SAR simulation.

Near-space, defined as the region between 20 km and 100 km, offers many new capabilities that are not accessible to low earth orbit (LEO) satellites and airplanes, because it is above storm and not constrained by either the orbital mechanics of satellites or the high fuel consumption of airplanes. By placing radar transmitter/receiver in near-space platforms, many functions that are currently performed with satellites or airplanes could be performed in a cheaper way. Inspired by these advantages, this paper introduces several near-space vehicle-based radar configurations, such as near-space passive bistatic radar and high-resolution wide-swath (HRWS) synthetic aperture radar (SAR). Their potential applications, technical challenges and possible solutions are investigated. It is shown that near-space is a satisfactory solution to some specific remote sensing applications. Firstly, near-space passive bistatic radar using opportunistic illuminators offers a solution to persistent regional remote sensing, which is particularly interest for protecting homeland security or monitoring regional environment. Secondly, near-space provides an optimal solution to relative HRWS SAR imaging. Moreover, as motion compensation is a common technical challenge for the described radars, an active transponder-based motion compensation is also described.

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation.

Synthetic Aperture Radar (SAR) is a remote sensing technique that allows the generation of microwave images of the Earth surface, independently of weather condition and of sun illumination. SAR sensors can be either mounted on spacecrafts (spaceborne systems) or placed on airplanes (airborne systems). In this thesis the study of problems related to the use of airborne SAR is addressed. The thesis is organized in five chapters and Chapter 1 presents the basic rationale of SAR technique. The assumption that the sensor flight path is a straight line is adopted in the first chapter, which is preliminary to the subsequent analysis, and allows introducing the formalism adopted in Chapters 2-5, where the assumption of straight flight path, not realistic for airborne systems, is relaxed, and the original results of this thesis are presented. Airborne SAR acquisition model is deeply analyzed, and realization of an efficient airborne raw data simulator, which represents a useful tool for mission planning, SAR system design and inversion algorithm testing, is presented (Chapter 2). The analysis of the airborne acquisition model allows also highlighting problems related to airborne SAR focusing. In particular, a quantitative analysis of the accuracy achievable by airborne SAR images is performed; extension to the repeat pass airborne interferometry is also included (Chapter 3). Such an analysis allows, on one side, quantifying the airborne repeat pass interferogram phase accuracy in terms of the expected motion measurement system accuracy (Chapter 3); on the other side, designing a new efficient airborne SAR processor (Chapter 4). Theoretical results presented in Chapter 3 are, finally, validated by an airborne differential interferometry experiment carried out by using real data (Chapter 5).

The multipath clutter cancellation, the most computationally demanding part of signal processing in passive bistatic radar (PBR), is a key challenge in real time processing. However the previous Extensive Cancellation Algorithm (ECA) versions have their limitations in parallel implementation. In this paper an advanced version of ECA is proposed for multipath clutter cancellation in passive radar. Validated by simulation and theoretical analysis, the proposed algorithm can improve the computation efficiency with an excellent cancelling capability, which provides a valuable basis for real time signal processing in PBR.