Fok Hing Chi Tivive

An SVD-based approach for mitigating wall reflections in through-the-wall radar imaging

In this paper, we mitigate wall EM returns in through-the-wall radar imaging (TWRI) using singular value decomposition (SVD). To suppress wall reflections, the SVD is applied to the B-scan matrix of the received signals. The signal space is decomposed into three subspaces: the clutter subspace, the target subspace, and the noise subspace. Then, a set of normalized and smoothed eigen-components are combined to produce the target signal. Finally, delay-and-sum beamforming is applied to the reconstructed B-scan matrix to form the image. Experimental results demonstrate that the proposed method is effective in removing background, reducing clutter, and high-lighting the targets.

Efficient training algorithms for a class of shunting inhibitory convolutional neural networks

This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

Wall clutter mitigation based on eigen-analysis in through-the-wall radar imaging

This paper presents an effective approach for mitigating the wall EM returns in through-the-wall radar imaging. The wall returns tend to obscure indoor targets, rendering target detection and classification difficult, if not impossible. The proposed approach recognizes the relative strength of the front wall returns compared to behind-the-wall targets, and uses eigen-structure methods to identify, and then remove the wall subspace that is typically associated with the dominant eigenvalues. The paper provides analyses of wall and target subspace characteristics, dwelling on the underlying property that the wall and target subspaces are, in most cases, spanned by complex sinusoidal components. It is shown that both the wall and the target subspaces can be of multiple dimensions. The paper demonstrates, using simulated and real data, the effectiveness of the proposed approach and compares its performance to that of background subtraction.

A Gender Recognition System using Shunting Inhibitory Convolutional Neural Networks

In this paper, we employ shunting inhibitory convolutional neural networks to develop an automatic gender recognition system. The system comprises two modules: a face detector and a gender classifier. The human faces are first detected and localized in the input image. Each detected face is then passed to the gender classifier to determine whether it is a male or female. Both the face detection and gender classification modules employ the same neural network architecture; however, the two modules are trained separately to extract different features for face detection and gender classification. Tested on two different databases, Web and BioID database, the face detector has an average detection accuracy of 97.9%. The gender classifier, on the other hand, achieves 97.2% classification accuracy on the FERET database. The combined system achieves a recognition rate of 85.7% when tested on a large set of digital images collected from the Web and BioID face databases.

A human gait classification method based on radar Doppler spectrograms

An image classification technique, which has recently been introduced for visual pattern recognition, is successfully applied for human gait classification based on radar Doppler signatures depicted in the time-frequency domain. The proposed method has three processing stages. The first two stages are designed to extract Doppler features that can effectively characterize human motion based on the nature of arm swings, and the third stage performs classification. Three types of arm motion are considered: free-arm swings, one-arm confined swings, and no-arm swings. The last two arm motions can be indicative of a human carrying objects or a person in stressed situations. The paper discusses the different steps of the proposed method for extracting distinctive Doppler features and demonstrates their contributions to the final and desirable classification rates.

A Subspace Projection Approach for Wall Clutter Mitigation in Through-the-Wall Radar Imaging

One of the main challenges in through-the-wall radar imaging (TWRI) is the strong exterior wall returns, which tend to obscure indoor stationary targets, rendering target detection and classification difficult, if not impossible. In this paper, an effective wall clutter mitigation approach is proposed for TWRI that does not require knowledge of the background scene nor does it rely on accurate modeling and estimation of wall parameters. The proposed approach is based on the relative strength of the exterior wall returns compared to behind-wall targets. It applies singular value decomposition to the data matrix constructed from the space-frequency measurements to identify the wall subspace. Orthogonal subspace projection is performed to remove the wall electromagnetic signature from the radar signals. Furthermore, this paper provides an analysis of the wall and target subspace characteristics, demonstrating that both wall and target subspaces can be multidimensional. While the wall subspace depends on the wall type and building material, the target subspace depends on the location of the target, the number of targets in the scene, and the size of the target. Experimental results using simulated and real data demonstrate the effectiveness of the subspace projection method in mitigating wall clutter while preserving the target image. It is shown that the performance of the proposed approach, in terms of the improvement factor of the target-to-clutter ratio, is better than existing approaches and is comparable to that of background subtraction, which requires knowledge of a reference background scene.

Texture Classification using Convolutional Neural Networks

In this paper, we propose a convolutional neural network (CoNN) for texture classification. This network has the ability to perform feature extraction and classification within the same architecture, whilst preserving the two-dimensional spatial structure of the input image. Feature extraction is performed using shunting inhibitory neurons, whereas the final classification decision is performed using sigmoid neurons. Tested on images from the Brodatz texture database, the proposed network achieves similar or better classification performance as some of the most popular texture classification approaches, namely Gabor filters, wavelets, quadratic mirror filters (QMF) and co-occurrence matrix methods. Furthermore, The CoNN classifier outperforms these techniques when its output is postprocessed with median filtering

Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging

This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the measurement matrix is vectorized so that a single measurement vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.

Automatic classification of human motions using Doppler radar

This paper presents a new approach to classify human motions using a Doppler radar for applications in security and surveillance. Traditionally, the Doppler radar is an effective tool for detecting the position and velocity of a moving target, even in adverse weather conditions and from a long range. In this paper, we are interested in using the Doppler radar to recognize the micro-motions exhibited by people. In the proposed approach, a frequency modulated continuous wave radar is applied to scan the target, and the short-time Fourier transform is used to convert the radar signal into spectrogram. Then, the new two-directional, two-dimensional principal component analysis and linear discriminant analysis are performed to obtain the feature vectors. This approach is more computationally efficient than the traditional principal component analysis. Finally, support vector machines are applied to classify feature vectors into different human motions. Evaluated on a radar data set with three types of motions, the proposed approach has a classification rate of 91.9%.

Fuzzy Logic-Based Image Fusion for Multi-view Through-the-Wall Radar

In this paper, we propose a new technique for image fusion in multi-view through-the-wall radar imaging system. As most existing image fusion methods for through-the-wall radar imaging only consider a global fusion operator, it is desirable to consider the differences between each pixel using a local operator. Here, we present a fuzzy logic-based method for pixel-wise image fusion. The performance of the proposed method is evaluated on both simulated and real data from through-the-wall radar imaging system. Experimental results show that the proposed method yields improved performance, compared to existing methods.