Mitigation of target distortion in pulse‐agile sensors via Richardson–Lucy deconvolution

Kirk, Benjamin H.; Martone, Anthony F.; Sherbondy, Kelly D.; Narayanan, Ram M.

doi:10.1049/el.2019.2650

Cited by 28 publications

(17 citation statements)

References 10 publications

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“…The target is much more visible when the DQN-established policy is utilized compared to the policy iteration approach, as the processed data being more coherent. While distortion effects from cognitive radar transmissions may be corrected by signal processing techniques, such as the Richardson-Lucy deconvolution algorithm or non-identical multiple pulse compression (NIMPC) [41], [42] the DQN approach may mitigate the need for computationally expensive post-processing.…”

Section: A Experimental Resultsmentioning

confidence: 99%

Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

Thornton,

Kozy,

Buehrer

et al. 2020

Preprint

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In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance. The radar learns to vary the bandwidth and center frequency of its linear frequency modulated (LFM) waveforms to mitigate mutual interference with other systems and improve target detection performance while also maintaining sufficient utilization of the available frequency bands required for a fine range resolution.We demonstrate that our approach, based on the Deep Q-Learning (DQL) algorithm, enhances important radar metrics, including SINR and bandwidth utilization, more effectively than policy iteration or senseand-avoid (SAA) approaches in a variety of realistic coexistence environments. We also extend the DQL-based approach to incorporate Double Q-learning and a recurrent neural network to form a Double Deep Recurrent Q-Network (DDRQN). We demonstrate the DDRQN results in favorable performance and stability compared to DQL and policy iteration. Finally, we demonstrate the practicality of our proposed approach through a discussion of experiments performed on a software defined radar (SDRadar) prototype system. Our experimental results indicate that the proposed Deep RL approach significantly improves radar detection performance in congested spectral environments when compared to policy iteration and SAA.

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Section: A Experimental Resultsmentioning

confidence: 99%

Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

Thornton,

Kozy,

Buehrer

et al. 2020

Preprint

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“…Compared with the usual Richardson-Lucy, the special case has many advantageous characteristics. The speed of iterative deconvolution in the paper is much faster than the usual one whose iterative speed is lower than the Wiener filter [23]. Moreover, due to the iterative time reversal can focus more and more energy on the strongest target, the peak-signal power of the iterative deconvolution is higher and higher with the number of iteration increasing.…”

Section: Introductionmentioning

confidence: 93%

“…Recent publications have shown that deconvolution-based methods can yield higher resolution and higher processing gain than the conventional methods, such as the CLEAN algorithm [12], Wiener filter [16] and Richardson-Lucy algorithm [21][22][23][24][25]. The Richardson-Lucy algorithm was devised by Richardson [26] and Lucy [27], which is an iterative deconvolution algorithm and recently applied in active radar/sonar to remove the blur caused by the transmitted signal [23][24][25]. In [23,24], the Richardson-Lucy algorithm is applied to the range-Doppler image, whereas it is used to the range image in the case of zero-Doppler in the paper.…”

Section: Introductionmentioning

confidence: 99%

“…The Richardson-Lucy algorithm was devised by Richardson [26] and Lucy [27], which is an iterative deconvolution algorithm and recently applied in active radar/sonar to remove the blur caused by the transmitted signal [23][24][25]. In [23,24], the Richardson-Lucy algorithm is applied to the range-Doppler image, whereas it is used to the range image in the case of zero-Doppler in the paper. It will be shown that the iterative deconvolution is actually an iterative time reversal processing in the special case of a one-dimensional, time-invariant blur function, which is easy to be met for the case in the paper.…”

Section: Introductionmentioning

confidence: 99%

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An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Guo

Zhao

2020

Sensors

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Matched filtering is widely used in active sonar because of its simplicity and ease of implementation. However, the resolution performance generally depends on the transmitted waveform. Moreover, its detection performance is limited by the high-level sidelobes and seriously degraded in a shallow water environment due to time spread induced by multipath propagation. This paper proposed a method named iterative deconvolution-time reversal (ID-TR), on which the energy of the cross-ambiguity function is modeled, as a convolution of the energy of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density. Similarly, the generalized target reflectivity density is a convolution of the spread function of channel with the reflectivity density of target as well. The ambiguity caused by the transmitted signal and the spread function of channel are removed by Richardson-Lucy iterative deconvolution and the time reversal processing, respectively. Moreover, this is a special case of the Richardson-Lucy algorithm that the blur function is one-dimensional and time-invariant. Therefore, the iteration deconvolution is actually implemented by the iterative temporal time reversal processing. Due to the iterative time reversal method can focus more and more energy on the strongest target with the iterative number increasing and then the peak-signal power increases, the simulated result shows that the noise reduction can achieve 250 dB in the “ideal” free field environment and 100 dB in a strong multipaths waveguide environment if a 1-ms linear frequency modulation with a 4-kHz frequency bandwidth is transmitted and the number of iteration is 10. Moreover, the range resolution is approximately a delta function. The results of the experiment in a tank show that the noise level is suppressed by more than 70 dB and the reverberation level is suppressed by 3 dB in the case of a single target and the iteration number being 8.

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“…A major challenge for frequency agile cognitive radar systems is target distortion in the range-Doppler processed data as a result of clutter modulation from intra-CPI pulse adaptations [9]. RL-based radar control is useful to mitigate this problem since radar behavior is motivated through a human-defined reward mapping.…”

Section: Introductionmentioning

confidence: 99%

Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

Thornton,

Buehrer,

Martone

et al. 2020

Preprint

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In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of Q-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.

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Mitigation of target distortion in pulse‐agile sensors via Richardson–Lucy deconvolution

Cited by 28 publications

References 10 publications

Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

An Iterative Deconvolution-Time Reversal Method with Noise Reduction, a High Resolution and Sidelobe Suppression for Active Sonar in Shallow Water Environments

Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

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