Orthogonal Subspace Projection Algorithm Research Articles

Resolution Threshold Analysis of the Microwave Radar Coincidence Imaging

The resolution of the microwave radar coincidence imaging (MRCI) can break the diffraction limit, which has been validated by experiments. However, there is still no theoretical analysis. In this article, the resolution of the MRCI is theoretically analyzed using the orthogonal subspace projection algorithm based on the space spanned by the discrete reference radiation mode. First, a target location estimate (TLE) method using the data from the nonfocusing radar array of the MRCI system is proposed to estimate the target position assisted by the equivalent detection method. The estimation precision of the TLE method is approximately equal to the 3-dB beamwidth of the coherent transmitting radar array with the same aperture; hence, the imaging plane can be obtained. Then, the equivalent internal noise (generated by the error of the target distance estimation, i.e., the location of the imaging plane) is theoretically analyzed. Finally, the imaging resolution threshold of the MRCI system is theoretically analyzed. The relationship between the resolution threshold and the factors (such as the deployment of the transmitting radar array, the distance between the target and the radar array, and the signal-to-noise ratio of the imaging system) is summarized. The proposed estimation method and the analyses of the MRCI system are validated through a set of simulations and experiments.

Hyperspectral anomaly detection based on anomalous component extraction framework

Anomaly detection has become an important topic in Hyperspectral Imagery (HSI) analysis in the last two decades with the advantage of detecting the targets surrounding in diverse backgrounds without prior knowledge. HSIs usually have complex and redundant spectral signals due to the complicated land-cover distribution. Generally, it is difficult to estimate the background accurately, and distinguish the anomaly targets. The performances of traditional algorithms are difficult to meet the requirements. In this paper, we propose a novel anomalous component extraction framework for hyperspectral anomaly detection based on Independent Component Analysis (ICA) and Orthogonal Subspace Projection (OSP). In the proposed method, the brightest anomalous component is extracted to initialize the projection vector, by which the performance of ICA can be improved greatly. Moreover, the Independent Component (IC) containing the most abnormal information can be obtained according to the vector. Besides, The OSP algorithm is applied to suppress the background components in the remaining data. Then the data are iteratively processed by ICA to extract the anomalous component subtly. Therefore, in the initialization process, the possible situation of detecting the pixels in the same position can be effectively avoided, and the interference of the last iteration procedure can be cut down greatly, helping to optimize the detection. Finally, the experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art methods in the field of anomaly detection.

Anti-Jamming Performance Study of Adaptive Antenna

This paper analyzes the radar anti-jamming adaptive antenna beamforming algorithm, orthogonal subspace projection algorithm used in radar anti-jamming eigenvectors in the process can not be completely orthogonal subspace decomposition, constraints annihilatorspace decomposition algorithm to ensure orthogonal subspace eigenvectors. Finally, the simulation proved: subspace tracking algorithm has stronger anti-interference performance relative to the traditional adaptive algorithm.

Comparative Assessment of Some Target Detection Algorithms for Hyperspectral Images

Target detection is of particular interest in hyperspectral image analysis as many unknown and subtle signals (spectral response) unresolved by multispectral sensors can be discovered in hyperspectral images. The detection of signals in the form of small objects and targets from hyperspectral sensors has a wide range of applications both civilian and military. It has been observed that a number of target detection algorithms are in vogue; each has its own advantages and disadvantages and assumptions. The selection of a particular algorithm may depend on the amount of information available as per the requirement of the algorithm, application area, the computational complexity etc. In the present study, three algorithms, namely, orthogonal subspace projection (OSP), constrained energy minimization (CEM) and a nonlinear version of OSP called kernel orthogonal subspace projection (KOSP), have been investigated for target detection from hyperspectral remote sensing data. The efficacy of algorithms has been examined over two different hyperspectral datasets which include a synthetic image and an AVIRIS image. The quality of target detection from these algorithms has been evaluated through visual interpretation as well as through receiver operating characteristic (ROC) curves. The performance of OSP algorithm has been found to be better than or comparable to CEM algorithm. However, KOSP out performs both the algorithms. Defence Science Journal, 2013, 63(1), pp.53 -62 , DOI:http://dx.doi.org/10.14429/dsj. 63.3764

Analysis of a shallow water environment by multispectral satellite images using a subpixel classification algorithm

Optical satellite images observed in shallow water represent a mixture of information concerning bottom type, water quality, and water depth. In this study, we extracted such information by estimating the abundance using the mixed pixel classification technique. The method is based on the Orthogonal Subspace Projection algorithm which can first eliminate unwanted information, and then match for the information in which we are interested in. Our results indicated that using the subpixel classification approach, information correlated with water depth can be extracted from optical multi-spectral images. To further test this approach, we used satellite images of Itu-Aba Island in the southern part of the South China Sea as a test image and compared with truth depth data from echo sounder sonar system for verification. The results were promising and showed that the information extracted from the satellite image corresponding to bathymetry was highly correlated to the true water depth.

Kernel orthogonal subspace projection for hyperspectral signal classification

In this paper, a kernel-based nonlinear version of the orthogonal subspace projection (OSP) operator is defined in terms of kernel functions. Input data are implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The OSP expression is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for detection of roads, roof tops, mines, and targets in hyperspectral imagery, and it is shown that the kernelized OSP method outperforms the conventional OSP approach.