Abstract
This paper considers multiple target localization using a non-coherent bi-static radar with multiple receivers, where the targets are located behind a wall. This paper presents a new clustering algorithm inspired by Newtonian gravity that iteratively groups particles at target locations and eliminates particles at non-target locations. We first propose a histogram based pre-processing algorithm that imposes a grid over the region of interest and defines a particle with measurement-dependent mass for each grid square. We then calculate a Newtonian inspired force on each of the particles and move them in the direction of the force. We repeat the process until there is no further movement. The proposed algorithm works even when some of the measurements are unavailable or missing and when some of the measurements are false measurements. Location accuracy is shown to be in the order of 8 cm.
Highlights
Through-the-wall radar (TWR) has attracted considerable interest in recent years because of its increasing applications in rescue and military operations [1,2,3,4,5]
We propose a new multiple target location estimation algorithm consisting of a new pre-processing step which we call Ellipse-Histogram (EH) pre-processing, coupled with a new clustering algorithm motivated by Newtonian gravity
We propose a new clustering algorithm inspired by Newtonian gravity to cluster the particles and identify the target locations
Summary
Through-the-wall radar (TWR) has attracted considerable interest in recent years because of its increasing applications in rescue and military operations [1,2,3,4,5]. As the number of receivers increases, the number of intersection points increases, giving the problem even greater computational complexity This co-linear limitation is problematic for practical applications requiring accuracy in two dimensions, since the ellipse crossings are oblique for targets at broadside, as well as those in the axis of the array, and are sensitive to measurement errors. One approach would be to apply a clustering algorithm, since there is no known model for the statistical distribution of the locations of the ellipse intersection points in the multiple target scenario, there is no existing clustering algorithm that is suitable Existing clustering algorithms, such as k-means, are based on the assumption that the data comes from a source with additive noise. False measurements arise when there is a strong reflection from objects that are not targets (i.e., not of interest, e.g., reflection from walls.)
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