Spatial Spectrum-Based Imaging for UWB Through-the-Wall MIMO Arrays

Hindawi Publishing Corporation International Journal of Antennas and Propagation Volume 2014, Article ID 825403, 13 pages http://dx.doi.org/10.1155/2014/825403 Research Article Spatial Spectrum-Based Imaging for UWB Through-the-Wall MIMO Arrays Biying Lu, Xin Sun, Yang Zhao, and Zhimin Zhou College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China Correspondence should be addressed to Biying Lu; Received 2 April 2014; Revised 23 June 2014; Accepted 28 June 2014; Published 21 July 2014 Academic Editor: Ahmed Shaharyar Khwaja Copyright © 2014 Biying Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To keep the system complexity at a reasonable level and conform to the propagation demands, MIMO arrays are usually sparse in through-the-wall applications, which results in corrupted and gapped data. The corresponding imaging results are seriously affected by the high-level sidelobes. To solve this problem, a new imaging model for ultra-wideband (UWB) MIMO arrays is constructed via spatial spectrum theory in this paper. Based on the model, the characteristics of the spatial spectrum for the MIMO array and its effects on imaging are discussed. To improve the imaging quality, a through-the-wall imaging enhancement method is proposed via spatial spectrum estimation. Synthetic and experimental results show that, unlike the conventional amplitude weighting methods and nonlinear techniques, the proposed method can efficiently suppress sidelobes in the imagery, especially for the sparse MIMO array, and consequently improve the target image quality without degrading the mainlobe resolution. The proposed method has been successfully used in our real through-the-wall radar system. 1. Introduction Ultra-wideband (UWB) through-the-wall imaging (TWI) approaches that can detect objects through obstacles, such as walls, doors, and other opaque materials, are considered powerful tools for a variety of civilian and military applications [1–5]. In TWI applications, the imaging component of the application is considered the most important because it is usually the first step for the subsequent processes, such as detection, identification, and wall parameters estimation [6–10]. Currently, to obtain a satisfying target image, two types of radars are widely used: synthetic aperture radar (SAR) and multiple input multiple output (MIMO) radar. Although SAR has better resolution, it has a heavy time cost. By using the high-speed electronic switch, the time to acquire a dataset in a MIMO system is greatly reduced, compared to SAR systems. Therefore, MIMO radar is preferred over SAR in real applications, especially for moving target imaging. By using the proper array design method, we can obtain an optimal array configuration. However, in certain real cases, the equipment complexity and the shape may be our first consideration. Therefore, we make the tradeoff between size and performance [11]. For example, to achieve the Nyquist sampling criterion, the interelement space (𝑑) must be kept below half of the wavelength (𝜆) for the MIMO array [12]. However, conforming to this criterion will lead to a large number of array elements, even for a small aperture. Usually, when a MIMO array is used in TWI applications, the element spacing is made significantly higher than 𝜆/2 to keep the system complexity at reasonable levels and to increase the element size to achieve an acceptable SNR. Furthermore, for a typical TWI radar system, the most commonly used frequency range is from 1 GHz to 3 GHz to support the range resolution and wall propagation ability. Therefore, for the ultrawideband signal, even if more elements can be placed in the equipment, the elements are usually dense in the low frequency band but sparse in the high frequency band. In such a case, the MIMO array will not be optimal but it will be sparse with gapped virtual elements, which would otherwise diminish the array imaging performance. As a result, the image quality of the TWI results, in real 2 applications, is significantly limited by the ratio of the main to sidelobe amplitude. To suppress the sidelobes and improve the image quality, many imaging methods for through-the-wall imaging, including the back projection (BP) method [13, 14], the beamforming method [15, 16], and the tomography method [17, 18], are presented in recent years. In these methods, the sidelobes are reduced by applying an amplitude weighting function to the data prior to the final IFFT. However, the sidelobes have been reduced at the expense of the main lobe width, which determines the ultimate resolution of the imagery [19]. For example, the Hanning main lobe is twice as wide (null-to-null) as the sinc function. These methods are consequently a compromise between a narrow main lobe (high resolution) and low sidelobes. To retain the main lobe resolution while reducing the sidelobes, several nonlinear signal processing methods are introduced into radar imaging. Typical methods include spatially variant apodization (SVA), super-SVA, and the CLEAN technique [19–22]. By using interpolation or extrapolation operations, these methods are successfully used in SAR signal data processing to minimize the effects of corrupted and gapped data. However, for MIMO radar, because of the more complicated signal channels, the distribution of the received data is significantly different from that in SAR. In this situation, the performance of these methods is seriously affected. Based on the rigorous derivation of the UWB MIMO array and experimental validation via real TWI radar systems, we proposed in this paper a through-the-wall imaging enhancement method via spatial spectrum theory. Unlike the conventional amplitude weighting methods and nonlinear techniques, the proposed method can effectively suppress the sidelobes from imagery, especially for the UWB sparse MIMO array, and consequently enhance the target image quality without degrading the main lobe resolution. This paper is organized as follows. In Section 2, the imaging model for the MIMO array is constructed via spatial spectrum theory. Then, the spatial spectrum of the UWB MIMO array is deeply analyzed. The effects of the spatial spectrum distribution on PSF are discussed, and the spatial spectrum characteristics for the typical TWI UWB MIMO array are obtained. In Section 4, to improve the image quality, an imaging enhancement method by spatial spectrum estimation is proposed. Synthetic and experimental processing results are given in Section 5. Conclusions end this paper. International Journal of Antennas and Propagation 𝜃 𝜌 rs = (𝜌, 𝜃) rt = (𝜌t , 𝜃t ) rr = (𝜌r , 𝜃r ) ··· T R Figure 1: The geometry of the MIMO array imaging scene. transmitted signal, which has a frequency range of [𝑓0 (...truncated)