Group sparsity based imaging algorithm for TWRI under wall parameter uncertainties

With recent advances in both algorithm and component technologies, through-the-wall radar imaging (TWRI) is emerging as an affordable sensor technology in civil and military settings. Design of appropriate waveforms is important for improving the performance of TWRI systems. We present optimal waveforms, based on the matched illumination concept, for detection of targets inside enclosed structures using a monostatic multi-antenna radar system. We evaluate the effect of these optimal detection waveforms on the performance of through-the-wall radar imaging. The design flexibility for transmit signal duration and its role in the ultimate range resolution of the TWRI system is investigated. Supporting examples based on numerical electromagnetic modeling are also provided.

To achieve high resolution through-the-wall radar imaging (TWRI), long wideband antenna arrays need to be considered, thus resulting in massive amounts of data. Compressive sensing (CS) techniques resolve this issue by allowing image reconstruction using much fewer measurements. The performance of different CS algorithms, when applied to TWRI, has not been investigated in a comprehensive and comparative manner. In this paper, popular CS algorithms are evaluated, to see which are most suitable for TWRI applications. As for the evaluation criteria, the notion of F1-score is adopted and used in the context of TWRI; thus emphasising the algorithms ability to reconstruct an image with correctly detected targets. Algorithms responses to different levels of signal-to-noise ratio (SNR) and compression rate are evaluated. Numerical results show that for systems with low SNR, alternating direction based algorithms work better than others. When the SNR is high, algorithms depending on spectral gradient-projection methods give good results even with high compression rates.

Through-the-wall radar imaging (TWRI) is usually associated with the use of ultra wideband (UWB) signals to allow for wall penetration and high resolution. It is difficult to keep the antenna pattern fixed at all frequencies. Due to the large bandwidth and associated big data, compressive sensing (CS) is used for TWRI. We propose to embed the directivity of the antenna in the designed sparsity dictionary. The results show improved image reconstruction.

In this paper, we provide a review of the different approaches used to detect activities inside a building using through-the-wall radar. We classify two main types of radar that are used in detection activities behind the wall. Stepped frequency and pulse compression radar are the most frequently used types of radar in through-the-wall radar imaging (TWRI). In addition, a preference has been shown for using frequencies in the range of 1–3 GHz. Though a high volume of research is being done in this area, there are still many open issues. Detectability of the human being, wall modeling, and target differentiation have been identified as the main open issues in the field of TWRI.

Polarimetric through-the-wall radar imaging (TWRI) system has the enhancing performance in the detection, imaging, and classification of concealed targets behind the wall. We propose a group sparse basis pursuit denoising- (BPDN-) based imaging approach for polarimetric TWRI system in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with the nonuniform fast Fourier transform (NUFFT) technique to implement the imaging reconstruction of observed scene. The experimental results have demonstrated that compared to the existing compressive sensing- (CS-) based imaging algorithms, the proposed NUFFT-based SPGL1 algorithm can significantly reduce the required computer memory and achieve the improved imaging reconstruction performance with the high computational efficiency.

Multipath exploitation has been successfully applied in through-the-wall radar imaging given prior knowledge of the room geometry. Actually, the prior information of the walls is generally not known. A Contraposition Subtraction (CONS) algorithm is proposed to solve the problems of poor wall parameter reconstruction accuracy and extremely high initial value requirements of iterative algorithms under fuzzy wall geometry, and the CONS algorithm model is established to solve the problems of high initial value requirements of traditional quasi-Newton algorithms and improved quasi- Newton-particle swarm optimization algorithms. The estimates obtained have large errors and other problems. The CONS algorithm combined with the Block Orthogonal Matching Pursuit (BOMP) algorithm can not only accurately reconstruct the position of the sidewalls, but also reconstruct the moving and stationary targets, which improves the limitations and accuracy of the algorithm to a certain extent. The simulation results and data analysis verify the performance of the proposed method.

Through-the-wall radar imaging (TWRI) provides the capability to see inside buildings using electromagnetic waves for both defence and civilian applications. Unlike traditional free-space radar operation, the presence of walls introduces unique challenges that need to be addressed to render TWRI a viable technology. In this chapter, we present a review of various approaches for building layout determination and imaging of stationary indoor scenes. These approaches effectively address the associated challenges to provide effective and reliable radar operation. Specifically, we consider both ground-based and airborne radar operation and discuss both non-adaptive time-domain and frequency-domain beamforming approaches for through-the-wall image formation of stationary scenes and feature extraction-based building layout determination methods.

Through-the-wall radar imaging (TWRI) is currently an active and growing area of research and development because the detection and localization of targets in enclosed structures has many civilian and military applications such as rescue operations in case of fire or after earthquakes. However, contrary to free-space imaging, methods needed for TWRI are not a straight-forward process and face many challenges, all imposed by the presence of the building walls [1]. One problem pertains to frequency dependent attenuation and phase dispersion caused by exterior walls. This limits the useful frequency range to the UHF band with its lower frequency limited by the antenna size. Consequently, the bandwidth and image resolution of TWR systems is limited. The second issue is related to the scattering from the building walls and the multipath signals they create. The specular reflection and multiple bounces within the wall create a very strong signal much larger than that of the target behind the wall which normally has a small radar cross section (e.g. human). This leads to two problems: If the target is close to the wall, it's return can be completely covered by the wall return or if the target is further away from the wall, it can still be covered by the strong multiple bounce response or the point spread function sidelobes of the wall return. It is possible to reduce the sidelobe level but at the expense of range resolution by windowing the frequency response of the radar return.

A three-dimensional (3D) beamformer for through-the-wall radar imaging (TWI) for a general multilayered wall case is presented. The far field layered medium Green's function is incorporated in the 3D TWI algorithm for the compensation of the wall effect. Numerical results show that the 3D beamformer can provide high quality focused image of the target behind the walls.

The high orbit height leads to the ultralow signal-to-noise ratio (ULSNR) of geosynchronous (GEO) targets, so it is necessary to increase the coherent processing interval (CPI) to improve the coherent accumulation gain. The ultra-long CPI (ULCPI) causes wide rotational angles and complex signal modulation, which poses a serious challenge to conventional inverse synthetic aperture radar (ISAR) imaging algorithms. Moreover, the high orbit height and high-speed motion of GEO targets make the “stop-and-go” model no longer applicable, and intra-pulse motion errors must be taken into consideration. To address the problems, this article proposes a high-resolution ISAR imaging algorithm with joint intra-pulse and inter-pulse high-order motion compensation (JIPHOMC) and sub-aperture images fusion for GEO targets at ULCPI. In this technique, ULCPI is divided into several sub-apertures. Then, with respect to the motion compensation in sub-apertures, we innovatively propose a JIPHOMC algorithm with particle swarm optimization (PSO), which can correct the complex spatial-time-variant (STV) motion errors caused by wide rotational angles and high-speed motion, and eliminate the 2-D high-order coupling. By utilizing image overall constraints, JIPHOMC avoids signal source decomposition processing, thereby boosting algorithm efficiency and robustness. For the sub-aperture images obtained from different perspectives, we develop a sub-aperture image fusion algorithm (SAIF) based on non-negative matrix factorization (NMF) of structured weighted sparse enhancement, which can ensure the integrity of the target structure and enhance the image signal-to-noise ratio (SNR), so as to achieve high-resolution ISAR imaging of GEO targets at ULCPI. Extensive experiments based on both scattering point simulation data and electromagnetic calculation data corroborate that the proposed algorithm outperforms traditional ISAR imaging methods for GEO targets at ULCPI.

The azimuth multichannel synthetic aperture radar (MC-SAR) is an effective means of earth observation due to its high-resolution and wide-swath (HRWS) imaging capability. Compared with the traditional MC-SAR, the two-dimensional (2D) spectrum of the squint multichannel synthetic aperture radar (SMC-SAR) is skewed, leading to the severe coupling between azimuth signals and range signals. Therefore, the traditional imaging algorithms are inapplicable to SMC-SAR. Currently, the existing imaging algorithms for SMC-SAR need interpolation or sub-aperture processing, which results in low efficiency. This paper proposes a novel imaging scheme for spaceborne SMC-SAR to handle this issue. With the squint angle known, the Doppler centroid can be compensated for by the spectrum shift function. Additionally, the frequency correlation method (FCM) is utilized to estimate the channel phase imbalance. After that, the azimuth multichannel data are reconstructed by the filter banks to eliminate the Doppler spectrum aliasing. Finally, the data are imaged based on the modified equivalent slant range model (MESRM). Both the simulation and Gaofen-3 (GF-3) data experiments demonstrate the effectiveness and efficiency of the proposed method.

Fuzzy systems (FSs) have been extensively applied to automatic control, pattern recognition, and decision analysis. However, a common bottleneck is encountered in the derivation of fuzzy rules, which is often difficult, time consuming, and relies on expert knowledge. To automate the design of FSs, many metaheuristic learning algorithms have been proposed. One major optimization category uses Swarm Intelligence (SI) model (Kennedy et al., 2001). The SI technique studies collective behavior in decentralized systems. Its development was based on mimicking the social behavior of animals or insects in an effort to find the optima in the problem space. SI models are initialized with a population of random solutions. One well-known SI model is particle swarm optimization (PSO) (Kennedy & Eberhart, 1995). Many modified PSO models have been proposed and successfully applied to different optimization problems (Clerc & Kennedy, 2002; Bergh & Engelbrecht, 2004; Ratnaweera et al., 2004; Juang, 2004; Kennedy & Mendes, 2006; Parrott & Li, 2006; Chen & Li, 2007). FS design using PSO has also been proposed in several studies (Juang, 2004; Chatterjee et al., 2005; Juang et al., 2007; Araujo & Coelho, 2008; Sharma et al., 2009). Another well-known SI is ant colony optimization (ACO) (Dorigo & Stutzle, 2004). The ACO technique is inspired by real ant colony observations. It is a multi-agent approach that was originally proposed to solve difficult discrete combinatorial optimization problems, such as the traveling salesman problem (TSP) (Dorigo et al., 1996; Dorigo & Gambardella, 1997). In the original ACO meta-heuristic, artificial ant colonies cooperate to find good solutions for difficult discrete optimization problems. Different ACO models have been applied to FS design problems (Cassillas et al., 2000; Cassillas et al., 2005; Mucientes & Casillas; 2007; Juang & Lo, 2007; Juang et al., 2008; Juang & Lu; 2009). In (Cassillas et al.,2000; Mucientes & Casillas; 2007; Juang et al., 2008; Juang & Lu; 2009), the FS input space was partitioned in grid type with antecedent part parameters of an FS manually assigned in advance. In (Juang & Lo, 2007), the FS input space was flexibly partitioned using a fuzzy clustering-like algorithm in order to reduce the total number of rules. For all of these studies, the consequent part parameters were optimized in discrete space using ACO. Since only the consequent part parameters are optimized, and the optimization space is restricted to be discrete, the designed FSs are unsuitable for problems where high accuracy is a major concern. Source: Fuzzy Systems, Book edited by: Ahmad Taher Azar, ISBN 978-953-7619-92-3, pp. 216, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

A single algorithm for various radar imaging applications is presented. The idea behind the proposed approach is that any motion between radar and target can always be modelled as a composition of translational and rotational components. Novel techniques to estimate and compensate these two motion components are discussed and also validated by application to real radar data for different targets (ships, aircraft, ground). By the translational-rotational-motion-compensation algorithm, good results can be achieved, especially in cases characterised by evident variation of the rotation rate during the coherent processing time for imaging. This usually happens in aircraft, ship or ground mover inverse synthetic aperture radar (ISAR) imaging as well as in squinted or forward-looking SAR. In any case, the efficiency and the accuracy of the algorithm presented for translational motion compensation might be the initial step of alternative radar imaging processing. The simplicity of the proposed algorithms makes them suitable and convenient for real-time application even for compact and low-weight radar systems to be fitted on board unmanned aerial vehicles (UAV) or other airborne platforms. The proposed concept is the result of the authors' decennial experience devoted to achieve a single, simple, robust, reliable and efficient signal processing algorithm for several real-time radar imaging applications.

This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for com- pressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behind- the-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view.

In this paper, measured results for complex permittivity of some commonly used building walls under difierent hydration (wetness) levels are presented and a simple hybrid measurement and electromagnetic modeling approach for the estimation of power returns from targets located behind walls in various through-the-wall radar imaging (TWRI) scenarios is discussed. The radar cross section (RCS) of some typical targets of interest, such as an AK47 assault ri∞e and human, are flrst investigated in free-space using numerical electromagnetic modeling. A modifled radar range equation, which analytically accounts for the wall efiects, including multiple re∞ections within a given homogeneous or layered wall, is then employed in conjunction with wideband measured parameters of various common wall types, to estimate the received power versus frequency from modeled targets of interest. The proposed technique, which can be helpful in TWRI dynamic-range system design consideration, is, in principle, applicable to both bistatic and monostatic operations. The results for various wall types, including drywall, brick, solid concrete, and cinder block, the latter two under both wet and dry conditions, are presented.