Search

This paper proposes a method for the three-dimensional localization of an active aerial target by a single ground based sensor. The proposed method employs the time and frequency differences of arrival of the signal received directly from the aerial target and the signals received after being reflected from some large auxiliary terrestrial targets (pseudo-sensors) with known positions on the ground. Due to the terrestrial nature of the main and the pseudo sensors, it is impossible to solve for the target's altitude using traditional methods. The proposed method employs target motion analysis to obtain target position including its altitude with acceptable accuracy and low computational complexity. Presented simulations confirm acceptable accuracy of the proposed method in determining three dimensional position of the target despite limited number of the pseudo sensors and its low computational complexity.

In detecting shallow buried underground objects, reflected power from ground, i.e. ground surface clutter makes the task extremely difficult. In order to remove ground clutter, conventional methods in the literature are not as much effective as we need for objects buried detection in shallow depths. In this paper, a robust method, based on Time gating and wiener filtering, is proposed, which is very precise and effective in Ultra wideband (UWB) imaging. The problem with time gating method solely is that the timing window length for unknown target depths cannot be determined beforehand with sufficient accuracy. Imprecise window length selection removes parts of target signals along with the clutter and increases missed detection probability. This paper proposes an algorithm to circumvent this problem by first using a wiener filter for cancellation of ground clutter to a reasonable extent and pre detection of target positions by average similarity function (ASF). The time gating method is then used in the second step using the information provided from the first step for window length selection. The combination of the two steps provides better detection of shallow buried objects with less missed detection of targets.

In this paper, a whitening-based algorithm has been applied to sparse non-negative matrix factorization (NMF) as a preprocessing practice enhancing the identification of buried object, significantly. In fact, without utilizing a suitable whitening algorithm, the input signals are very similar to one another. Therefore, conventional approaches are not able to identify and properly discriminate desired buried objects. A suitable whitening algorithm makes the covariance matrix diagonal, such that the processed signals highly resembles the unprocessed versions. Such a similarity would be helpful to identify new received signal. In this study, we have employed the zero-phase component analysis (ZCA) as our whitening method. Hence, although signals of different targets are very similar in many cases, the mentioned advantages, achieved by whitening method, make identification of targets much easier.

One of the widely used denoising methods in different domains is the Savitzky-Golay (SG) filter. The SG filter has two design parameters: window length and the filter order. As the length of the window increases, the estimation variance decreases, but the bias error increases at the same time. Mean square error (MSE) measure includes both bias and variance criteria. In this paper, we obtain the optimal window length of an SG filter with arbitrary order which minimizes the MSE. To achieve the optimal window length, we propose an algorithm whose performance is better than the existing methods. In this paper, we follow the viewpoint proposed by Persson and Strang and design the filter on the basis of Chebyshev orthogonal polynomials

Well-known methods are employed to localize mobile station (MS) using line of sight (LOS) measurements. These methods may result in large error if they are fed with non LOS (NLOS) measurements. Our proposed algorithm, referred to as Sparse Recovery of NLOS using IMAT (SRNI), considers NLOS as unknown variables and solves the resultant underdetermined system emphasizing on its sparsity feature based on IMAT methods. Simulations are conducted to investigate the performance of SRNI in comparison of other conventional algorithms. Results demonstrate that SRNI is fast enough to deal with large combination of BSs and also accurate in lower number of BSs, Comment: 6 pages, 7 figures