English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Multi-Hypothesis Tracker (MHT) is generally considered to be the best performing conventional tracker. It assesses the feasible association of sequences of measurements, calculates the probabilities of the association hypotheses and has track initiation capability. Whilst conventional tracking systems use a detection algorithm to extract measurements from the sensor data, Track-Before-Detect techniques remove the detection algorithm and supply all information received from the signal processing system as measurements to be associated and filtered by the tracker. We compare the performance of the MHT with that of a grid-based Hidden Markov Model Track-Before-Detect algorithm for low signal-to-noise ratio targets. The performances of the MHT and grid Hidden Markov Model algorithms are quantified using six measures: root-mean-square position error, overall detection probability, instantaneous detection probability, false track count, false track length, and computation resource. The grid Hidden Markov Model algorithm is found to have better detection and significantly better false track performance at the cost of computation resource.   References S. S. Blackman and R. Popoli. Design and Analysis of Modern Tracking Systems . Artech House, 1999. B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications . Artech House, 2004. R. M. Burczewski and N. C. Mohanty. Detection of Moving Optical Objects. In Proceedings of International Telemetering Conference , pages 325--330, 1978. S. J. Davey, M. G. Rutten, and B. Cheung. Comparison of Detection Performance for Several Track-Before-Detect Algorithms. EURASIP Journal on Advances in Signal Processing , 2008. C. A. Barlow and S. S. Blackman. New Bayesian Track-Before-Detect Design and Performance Study. In Proceedings of SPIE 3373 , 1998. L. D. Stone, C. A. Barlow, and T. L. Corwin. Bayesian Multiple Target Tracking . Artech House, 1999. S. B. Colegrove, S. J. Davey, and B. Cheung. Clutter Rejection Using Peak Curvature. IEEE Trans AES , 42:1492--1496, 2006. D. B. Reid. An Algorithm for Tracking Multiple Targets. IEEE Transactions on Automatic Control , AC-24:843--854, December 1979. B. Ristic and N. Gordon. Lecture notes in Multisensor and Data Fusion . Adelaide University, 2007. S. J. Davey, B. Cheung, and M. G. Rutten. Track-Before-Detect for Sensors with Complex Measurements. In Fusion Conference , 2009. S. J. Davey and M. G. Rutten. A Comparison of Three Algorithms for Tracking Dim Targets. In IDC Conference , 2007.

Assessment of multi-hypothesis tracking performance for low signal-to-noise ratio targets