Projects per year
Abstract / Description of output
Minimising the expected mean squared error is one of the fundamental metrics applied to adaptive waveform design for active sensing. Previously, only cost functions corresponding to a lower bound on the expected mean squared error have been expressed for optimisation. In this paper we express an exact cost function to optimise for minimum mean squared error adaptive waveform design (MMSE-AWD). This is expressed in a general form which can be applied to non-linear systems.
Additionally, we provide a general example for how this method of MMSE-AWD can be applied to a system that estimates the state using a particle filter (PF). We make the case that there is a compelling reason to choose to use the PF (as opposed to alternatives such as the Unscented Kalman filter and extended Kalman filter), as our MMSE-AWD implementation can re-use the particles and particle weightings from the PF, simplifying the overall computation.
Finally, we provide a numerical example, based on a simplified multiple-input-multiple-output radar system, which demonstrates that our MMSE-AWD method outperforms a simple non-adaptive radar, whose beam-pattern has a uniform angular spread, and also an existing approximate MMSE-AWD method.
Additionally, we provide a general example for how this method of MMSE-AWD can be applied to a system that estimates the state using a particle filter (PF). We make the case that there is a compelling reason to choose to use the PF (as opposed to alternatives such as the Unscented Kalman filter and extended Kalman filter), as our MMSE-AWD implementation can re-use the particles and particle weightings from the PF, simplifying the overall computation.
Finally, we provide a numerical example, based on a simplified multiple-input-multiple-output radar system, which demonstrates that our MMSE-AWD method outperforms a simple non-adaptive radar, whose beam-pattern has a uniform angular spread, and also an existing approximate MMSE-AWD method.
Original language | English |
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Pages (from-to) | 1361 - 1373 |
Journal | IEEE Transactions on Signal Processing |
Volume | 66 |
Issue number | 5 |
Early online date | 22 Dec 2017 |
DOIs | |
Publication status | Published - 1 Mar 2018 |
Keywords / Materials (for Non-textual outputs)
- Adaptive waveform design, minimum mean squared error, active sensing, MIMO, radar, Bayesian, particle filters, optimal design.
Fingerprint
Dive into the research topics of 'MMSE adaptive waveform design for active sensing with applications to MIMO radar'. Together they form a unique fingerprint.Projects
- 1 Finished
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Signal Processing in the Networked Battlespace
Mulgrew, B., Davies, M., Hopgood, J. & Thompson, J.
1/04/13 → 30/06/18
Project: Research
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Computationally simple MMSE (A-optimal) Adaptive Beam-pattern Design for MIMO Active Sensing Systems via a Linear-Gaussian Approximation
Herbert, S., Hopgood, J. R. & Mulgrew, B., 15 Sept 2018, In: IEEE Transactions on Signal Processing. 66, 18, p. 4935 - 4945 11 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
MMSE Adaptive Waveform Design for a MIMO Active Sensing System Tracking Multiple Moving Targets
Herbert, S., Hopgood, J. & Mulgrew, B., 15 Apr 2018, (Accepted/In press) IEEE International Conference on Acoustics, Speech and Signal Processing. Institute of Electrical and Electronics EngineersResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Optimality Criteria for Adaptive Waveform Design in MIMO Radar Systems
Herbert, S., Hopgood, J. & Mulgrew, B., 6 Dec 2017, Sensor Signal Processing for Defence. p. 11-15 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Datasets
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Software for "Approximate Adaptive Beam-Pattern Design"
Herbert, S. (Creator), Hopgood, J. (Supervisor) & Mulgrew, B. (Supervisor), Edinburgh DataShare, 3 Aug 2018
DOI: 10.7488/ds/2403
Dataset
Profiles
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James Hopgood
- School of Engineering - Personal Chair of Statistical Signal Processing
- Acoustics and Audio Group
Person: Academic: Research Active