Spectrum Allocation for Noncooperative Radar Coexistence

Martone, Anthony F.; Ranney, Kenneth I.; Sherbondy, Kelly D.; Gallagher, Kyle A.; Blunt, Shannon D.

doi:10.1109/taes.2017.2735659

Cited by 97 publications

(34 citation statements)

References 28 publications

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“…The notched waveform design formulation in Section 2 relies on the availability of knowledge regarding where spectral notches are required. For spectrum sensing and estimation approaches such as FSS described in Section 3 and [31], the act of estimating interference in real data introduces the possibility of estimation error even for stationary RFI. While the SINR degradation related either to underestimating interference (missed detection) or overestimating interference (false alarm) is relatively obvious, there also exists the prospect of correctly estimating RFI, but with varying bandwidth from pulse to pulse.…”

Section: Impact Of Notch Width Modulationmentioning

confidence: 99%

“…Here the FM noise waveform spectral notching capability is incorporated into a cognitive radar framework that performs spectrum sensing on a per‐pulse basis, estimates the spectral footprint of any in‐band interference, and then adjusts the notch location(s) and width(s) in an automated manner. The fast spectrum sensing (FSS) algorithm [30, 31], which mimics the rapid data assimilation capability of the human thalamus [32], is used to quickly estimate the frequency intervals requiring notching. For interference taking the form of frequency‐hopping orthogonal frequency division multiplexed (OFDM) communications, this overall cognitive strategy employs FSS to inform the subsequent notching of FM noise waveforms, with the ultimate goal of achieving real‐time RFI avoidance.…”

Section: Introductionmentioning

confidence: 99%

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Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar

Ravenscroft

Owen

Jakabosky

et al. 2018

IET Radar, Sonar & Navigation

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Spectrum sensing and transmit notching is a form of cognitive radar that seeks to reduce mutual interference with other spectrum users in the same band. This concept is examined for the case where another spectrum user moves in frequency during the radar's CPI. The physical radar emission is based on a recent FM noise waveform possessing attributes that are inherently robust to sidelobes that otherwise arise for spectral notching. Due to increasing spectrum sharing with cellular communications, the interference considered takes the form of in-band OFDM signals that hop around the band. The interference is measured each PRI and a fast spectrum sensing algorithm determines where notches are required, thus facilitating a rapid response to dynamic interference. To demonstrate the practical feasibility and to understand the tradespace such a scheme entails, free-space experimental measurements based on notched radar waveforms are collected and synthetically combined with separately measured hopping interference under a variety of conditions to assess the efficacy of such an approach, including the impact of interference hopping during the radar CPI, latency in the spectrum sensing/waveform design process, notch tapering to reduce sidelobes, notch width modulation due to spectrum sensing, and the impact of digital up-sampling on notch depth.

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Section: Impact Of Notch Width Modulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar

Ravenscroft

Owen

Jakabosky

et al. 2018

IET Radar, Sonar & Navigation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another approach was recently investigated to refine the spectral content into a subset of representative information, thereby reducing the number of frequency bins (

) input to SS-MO [ 14 ]. This fast SS-MO technique simultaneously reduces the computational complexity while preserving the performance of multi-objective optimization.…”

Section: Data Collection Generation and Analysismentioning

confidence: 99%

“…In order to ensure efficient and interference-free operation of cognitive radar to optimize target detection performance, the radar illuminator must dynamically sense and avoid spectral regions wherein RFI is present [ 10 , 11 , 12 , 13 , 14 ]. Based upon the detection of such interference bands or sub-bands, appropriate waveform design strategies can then be employed to enhance cognitive radar performance [ 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

The Spectrum Analysis Solution (SAS) System: Theoretical Analysis, Hardware Design and Implementation

Narayanan

Pooler

Martone

et al. 2018

Sensors

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This paper describes a multichannel super-heterodyne signal analyzer, called the Spectrum Analysis Solution (SAS), which performs multi-purpose spectrum sensing to support spectrally adaptive and cognitive radar applications. The SAS operates from ultrahigh frequency (UHF) to the S-band and features a wideband channel with eight narrowband channels. The wideband channel acts as a monitoring channel that can be used to tune the instantaneous band of the narrowband channels to areas of interest in the spectrum. The data collected from the SAS has been utilized to develop spectrum sensing algorithms for the budding field of spectrum sharing (SS) radar. Bandwidth (BW), average total power, percent occupancy (PO), signal-to-interference-plus-noise ratio (SINR), and power spectral entropy (PSE) have been examined as metrics for the characterization of the spectrum. These metrics are utilized to determine a contiguous optimal sub-band (OSB) for a SS radar transmission in a given spectrum for different modalities. Three OSB algorithms are presented and evaluated: the spectrum sensing multi objective (SS-MO), the spectrum sensing with brute force PSE (SS-BFE), and the spectrum sensing multi-objective with brute force PSE (SS-MO-BFE).

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“…In addition, radar systems must employ real-time dynamic spectrum access for spectrum sharing due to an increasingly congested RF spectrum. The need for a cognitive radar system to adapt its transmit waveform in real time to avoid RF interference (RFI) has been underscored [2,3]. Cognitive radar systems employing RFI avoidance via centre frequency (CF) and bandwidth adaptation have been shown to significantly improve the signal-to-interference-plus-noise ratio while operating in-band with other RF sources by mitigating mutual interference [4].…”

mentioning

confidence: 99%

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

et al. 2019

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Pulse-agile radar systems are becoming more prevalent as the demand for adaptive and cognitive systems increases. This focus is motivated by the need for interference avoidance and spectrum sharing. Pulse agility within a coherent processing interval (CPI) or intra-CPI adaption has been shown to cause distortion, which will negatively impact the radar's performance. This problem can be framed in the context of image processing such that an ideal range-Doppler image is corrupted by some point spread function. Deconvolution is then applied to remove this distortion and improve radar performance while maintaining the ability to use computationally efficient fast Fourier transform-based processing.

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Spectrum Allocation for Noncooperative Radar Coexistence

Cited by 97 publications

References 28 publications

Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar

Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar

The Spectrum Analysis Solution (SAS) System: Theoretical Analysis, Hardware Design and Implementation

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

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