The development from adaptive to cognitive radar resource management A Charlish, F Hoffmann, C Degen, I Schlangen IEEE Aerospace and Electronic Systems Magazine 35 (6), 8-19, 2020 | 63 | 2020 |
A second-order PHD filter with mean and variance in target number I Schlangen, ED Delande, J Houssineau, DE Clark IEEE Transactions on Signal Processing 66 (1), 48-63, 2017 | 55 | 2017 |
Marker-less stage drift correction in super-resolution microscopy using the single-cluster PHD filter I Schlangen, J Franco, J Houssineau, WTE Pitkeathly, D Clark, I Smal, ... IEEE Journal of Selected Topics in Signal Processing 10 (1), 193-202, 2015 | 33 | 2015 |
A mnemonic Kalman filter for non-linear systems with extensive temporal dependencies S Jung, I Schlangen, A Charlish IEEE Signal Processing Letters 27, 1005-1009, 2020 | 31 | 2020 |
Joint registration and fusion of an infrared camera and scanning radar in a maritime context D Cormack, I Schlangen, JR Hopgood, DE Clark IEEE Transactions on Aerospace and Electronic Systems 56 (2), 1357-1369, 2019 | 30 | 2019 |
A PHD filter with negative binomial clutter I Schlangen, E Delande, J Houssineau, DE Clark 2016 19th International Conference on Information Fusion (FUSION), 658-665, 2016 | 17 | 2016 |
Joint estimation of telescope drift and space object tracking O Hagen, J Houssineau, I Schlangen, ED Delande, J Franco, DE Clark 2016 IEEE Aerospace Conference, 1-10, 2016 | 13 | 2016 |
Sequential Monte Carlo filtering with long short-term memory prediction S Jung, I Schlangen, A Charlish 2019 22th International Conference on Information Fusion (FUSION), 1-7, 2019 | 12 | 2019 |
Joint multi-object and clutter rate estimation with the single-cluster PHD filter I Schlangen, V Bharti, E Delande, DE Clark 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 12 | 2017 |
Multi-object filtering with second-order moment statistics IC Schlangen Heriot-Watt University, 2017 | 9 | 2017 |
Single-cluster PHD filter methods for joint multi-object filtering and parameter estimation I Schlangen, DE Clark, ED Delande arXiv preprint arXiv:1705.05312, 2017 | 8 | 2017 |
Different tools for clutter mapping I Schlangen, M Daun INFORMATIK 2010. Service Science–Neue Perspektiven für die Informatik. Band …, 2010 | 8 | 2010 |
Time-dependent state prediction for the Kalman filter based on recurrent neural networks S Jung, I Schlangen, A Charlish 2020 IEEE 23rd International Conference on Information Fusion (FUSION), 1-7, 2020 | 4 | 2020 |
Image Registration Using Single Cluster PHD Methods M Campbell, I Schlangen, E Delande, D Clark Advanced Maui Optical and Space Surveillance Technologies Conference, 2017 | 4 | 2017 |
Towards Human-Machine Integration for Signals Intelligence Applications JD Rockbach, LF Bluhm, I Schlangen, L Over, S Apfeld, L Henneke, ... 2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 1-6, 2022 | 3 | 2022 |
State representation of eccentricity-limited targets for bistatic space surveillance radar design H Schily, I Schlangen, C Schwalm, A Charlish, R Hoffmann, M Käske, ... 2022 IEEE Radar Conference (RadarConf22), 01-06, 2022 | 2 | 2022 |
Distinguishing small targets from sea clutter using dynamic models I Schlangen, A Charlish 2019 IEEE Radar Conference (RadarConf), 1-6, 2019 | 2 | 2019 |
A novel approach to image calibration in super-resolution microscopy I Schlangen, J Houssineau, D Clark The 2014 International Conference on Control, Automation and Information …, 2014 | 2 | 2014 |
Self-organising Distributed Sensor Fusion Networks for Hierarchical Swarm Control and Supervision JD Rockbach, I Schlangen, M Bennewitz 2023 IEEE Symposium Sensor Data Fusion and International Conference on …, 2023 | 1 | 2023 |
A Non-Markovian Prediction for the GM-PHD Filter Based on Recurrent Neural Networks I Schlangen, S Jung, A Charlish 2020 IEEE Radar Conference (RadarConf20), 1-6, 2020 | 1 | 2020 |