Isabel Schlangen
Isabel Schlangen
Research Associate, Fraunhofer FKIE
Verified email at
Cited by
Cited by
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
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
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
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
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
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
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
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
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
Multi-object filtering with second-order moment statistics
IC Schlangen
Heriot-Watt University, 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
Different tools for clutter mapping
I Schlangen, M Daun
INFORMATIK 2010. Service Science–Neue Perspektiven für die Informatik. Band …, 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
Image Registration Using Single Cluster PHD Methods
M Campbell, I Schlangen, E Delande, D Clark
Advanced Maui Optical and Space Surveillance Technologies Conference, 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
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
Distinguishing small targets from sea clutter using dynamic models
I Schlangen, A Charlish
2019 IEEE Radar Conference (RadarConf), 1-6, 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
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
Distinguishing wanted and unwanted targets using point processes
I Schlangen, C Degen, A Charlish
2018 21st International Conference on Information Fusion (FUSION), 1445-1452, 2018
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