• Corpus ID: 239016785

How can a Cognitive Radar Mask its Cognition?

@inproceedings{Pattanayak2021HowCA,
  title={How can a Cognitive Radar Mask its Cognition?},
  author={Kunal Pattanayak and Vikram Krishnamurthy and C. Berry},
  year={2021}
}
We study how a cognitive radar can mask (hide) its cognitive ability from an adversarial jamming device. Specifically, if the radar optimally adapts its waveform based on adversarial target maneuvers (probes), how should the radar choose its waveform parameters (response) so that its utility function cannot be recovered by the adversary. This paper abstracts the radar’s cognition masking problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and… 

Figures from this paper

References

SHOWING 1-10 OF 24 REFERENCES
ELINT: The Interception and Analysis of Radar Signals
TLDR
This book examines Elint systems characteristics, performance issues, and essential functions, and gives new insight into Pri and intrapulse analysis so you can obtain better results and more data for identifying signals.
Hybrid Phased-MIMO Radar: A Novel Approach With Optimal Performance Under Electronic Countermeasures
TLDR
This letter presents a novel technique for optimizing the performance of a phased-multi-in multi-out (MIMO) radar in the presence of strong jamming effects that overcomes the noise jamming but also counters the effects of deception jamming.
Low probability of intercept-based distributed MIMO radar waveform design against barrage jamming in signal-dependent clutter and coloured noise
TLDR
It is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise, and demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.
Moving-Target Tracking by Cognitive RF Stealth Radar Using Frequency Diverse Array Antenna
  • Wen-qin Wang
  • Computer Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2016
TLDR
A moving-target tracking approach to achieve cognitive radio frequency stealth using an FDA antenna for surveillance applications using a traditional high-gain phased-array antenna beam with spoiled frequency increments is proposed.
Langevin Dynamics for Inverse Reinforcement Learning of Stochastic Gradient Algorithms
TLDR
A generalized Langevin dynamics algorithm to estimate the reward function ofverse reinforcement learning is presented; specifically, the resulting Langevin algorithm asymptotically generates samples from the distribution proportional to $\exp(R(\theta)$).
Apprenticeship learning via inverse reinforcement learning
TLDR
This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.
ECCM scheme against interrupted sampling repeater jammer based on time-frequency analysis
The interrupted sampling repeater jamming(ISRJ) is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation(LFM) radar. An electronic
Afriat and Revealed Preference Theory
Suppose that we can observe a number of decisions xi (where xi is a non-negative N dimensional vector for i = 1, 2, ..., I) which some decision-making unit has made and let us further suppose that
Maximum Entropy Inverse Reinforcement Learning
TLDR
A probabilistic approach based on the principle of maximum entropy that provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods is developed.
Introduction to RF stealth
This is the only book focused on the complete aspects of RF Stealth design. It is the first book to present and explain first order methods for the design of active and passive stealth properties.
...
1
2
3
...