Institution
Defence Science and Technology Organisation
Nonprofit•Canberra, Australian Capital Territory, Australia•
About: Defence Science and Technology Organisation is a nonprofit organization based out in Canberra, Australian Capital Territory, Australia. It is known for research contribution in the topics: Radar & Clutter. The organization has 2465 authors who have published 3856 publications receiving 90614 citations.
Topics: Radar, Clutter, Laser, Paris' law, Continuous-wave radar
Papers published on a yearly basis
Papers
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TL;DR: In this article, a series of tests were conducted to investigate the blast resistances of slabs constructed with both plain ultra-high performance fiber concrete (UHPFC) and reinforced UHPFC, and slabs reinforced with externally bonded (EB) fibre reinforced polymer (FRP) plates.
303 citations
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TL;DR: Simple criteria are provided that guarantee that a deterministic sensing matrix satisfying these criteria acts as a near isometry on an overwhelming majority of k-sparse signals; in particular, most such signals have a unique representation in the measurement domain.
Abstract: Compressed Sensing aims to capture attributes of k-sparse signals using very few measurements. In the standard compressed sensing paradigm, the N × C measurement matrix ? is required to act as a near isometry on the set of all k-sparse signals (restricted isometry property or RIP). Although it is known that certain probabilistic processes generate N × C matrices that satisfy RIP with high probability, there is no practical algorithm for verifying whether a given sensing matrix ? has this property, crucial for the feasibility of the standard recovery algorithms. In contrast, this paper provides simple criteria that guarantee that a deterministic sensing matrix satisfying these criteria acts as a near isometry on an overwhelming majority of k-sparse signals; in particular, most such signals have a unique representation in the measurement domain. Probability still plays a critical role, but it enters the signal model rather than the construction of the sensing matrix. An essential element in our construction is that we require the columns of the sensing matrix to form a group under pointwise multiplication. The construction allows recovery methods for which the expected performance is sub-linear in C, and only quadratic in N, as compared to the super-linear complexity in C of the Basis Pursuit or Matching Pursuit algorithms; the focus on expected performance is more typical of mainstream signal processing than the worst case analysis that prevails in standard compressed sensing. Our framework encompasses many families of deterministic sensing matrices, including those formed from discrete chirps, Delsarte-Goethals codes, and extended BCH codes.
296 citations
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TL;DR: Stable superhydrophobic surfaces with water contact angles over 170 degrees and sliding angles below 7 degrees were produced by simply coating a particulate silica sol solution of co-hydrolysed TEOS/fluorinated alkyl silane with NH(3).
290 citations
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TL;DR: By implementing the algorithm, simulations show successful recovery of signals with sparsity levels similar to those possible by matching pursuit with random measurements, a significant improvement over existing algorithms.
290 citations
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TL;DR: This work investigates the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs) and confirms the superiority of the PFs for this difficult nonlinear tracking problem.
Abstract: We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs). Three different (PFs) are proposed for this problem which is formulated as a multiple model tracking problem in a jump Markov system (JMS) framework. The proposed filters are (i) multiple model PF (MMPF), (ii) auxiliary MMPF (AUX-MMPF), and (iii) jump Markov system PF (JMS-PF). The performance of these filters is compared with that of standard interacting multiple model (IMM)-based trackers such as IMM-EKF and IMM-UKF for three separate cases: (i) single-sensor case, (ii) multisensor case, and (iii) tracking with hard constraints. A conservative CRLB applicable for this problem is also derived and compared with the RMS error performance of the filters. The results confirm the superiority of the PFs for this difficult nonlinear tracking problem.
289 citations
Authors
Showing all 2476 results
Name | H-index | Papers | Citations |
---|---|---|---|
Peng Shi | 137 | 1371 | 65195 |
Wayne Hu | 98 | 308 | 33371 |
Johan A. Martens | 88 | 720 | 28126 |
Maria Forsyth | 84 | 749 | 33340 |
Patrick M. Sexton | 75 | 350 | 21559 |
Xungai Wang | 68 | 675 | 19654 |
Michael D. Lee | 65 | 288 | 16437 |
Tanya M. Monro | 65 | 568 | 15880 |
Jan E. Leach | 64 | 222 | 13086 |
Raymond C. Boston | 63 | 454 | 15839 |
Adrian P. Mouritz | 61 | 284 | 14191 |
Christine E. A. Kirschhock | 52 | 231 | 9225 |
Robin J. Evans | 52 | 551 | 14169 |
Chun H. Wang | 51 | 331 | 8300 |
Branko Ristic | 48 | 253 | 10982 |