Deep learning for HRRP-based target recognition in multistatic radar systems

Jarmo Lundén, Visa Koivunen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

34 Citations (Scopus)


In this paper, a deep learning approach is proposed for target recognition based on high range resolution profiles (HRRPs) in multistatic radar systems. The proposed deep learning approach employs deep convolutional neural networks to automatically extract features from the HRRPs. In a multistatic radar system the target can be observed from multiple aspect angles simultaneously which improves the reliability and robustness of target recognition. In this paper, the spatial diversity offered by a multistatic radar system is exploited by averaging the slocal classifier output probabilities from the different monostatic and bistatic transmitter/receiver pairs. The highest global target probability is compared to a threshold to decide whether the target is classified to one of the preknown classes or as unknown. Simulation results show that the proposed deep learning approach has very reliable classification performance even at low signal-To-noise ratios.

Original languageEnglish
Title of host publication2016 IEEE Radar Conference, RadarConf 2016
ISBN (Electronic)9781509008636
Publication statusPublished - 3 Jun 2016
MoE publication typeA4 Article in a conference publication
EventIEEE Radar Conference - Philadelphia, United States
Duration: 2 May 20166 May 2016


ConferenceIEEE Radar Conference
Abbreviated titleRadarCon
CountryUnited States

Fingerprint Dive into the research topics of 'Deep learning for HRRP-based target recognition in multistatic radar systems'. Together they form a unique fingerprint.

Cite this