FPGA based random waveform generators for noise radars

Lukin, S. M.; Zemlyaniy, Oleg; Lukin, Konstantin

doi:10.1109/irs.2015.7226402

Cited by 10 publications

(6 citation statements)

References 3 publications

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“…Consequently, according to Parseval's Theorem [36], it evaluates to one. Finally, Equation (14) becomes…”

Section: Advanced Pulse Compression Noise Radarsmentioning

confidence: 99%

“…Many different approaches have been proposed in the literature to generate waveforms characterized by stochastic processes that better fulfill these radar system requirements [5,[14][15][16][17][18]. Still, they all suffer from low Doppler tolerance [19], an inherent characteristic of traditional noise radars that hinders their use in several applications, including surveillance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications

Barbosa,

Pralon,

Ramos

et al. 2024

Sensors

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The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal’s ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than −10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems.

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“…Consequently, according to Parseval's Theorem [36], it evaluates to one. Finally, Equation (14) becomes…”

Section: Advanced Pulse Compression Noise Radarsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications

Barbosa,

Pralon,

Ramos

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…[61], a real-time digital signal generator based on a FPGA was proposed for different distributions. More approaches have been made, for continuous wave and pulsed noise radar [62][63][64][65]. Either way, waveform optimisation will be needed because NRT signals have a non-desired characteristic which is the fluctuations created on the autocorrelation function sidelobes.…”

Section: Noise Radar Technologymentioning

confidence: 99%

Artificial Intelligence applications in Noise Radar Technology

Sénica,

Marques,

Figueiredo

2024

IET Radar Sonar & Navi

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Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use.

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“…With respect to a typical use of the LFSR (e.g., as bipolar modulating signal [14,15,22]), in this work it is exploited in an alternative mode. In fact, noise radar signals are generally synthesized using PN numbers (e.g., [23,24]), so the bit sequences are combined in order to get an approximation of independent uniform random variables.…”

Section: Transmitting Sectionmentioning

confidence: 99%

“…In particular, they show outstanding performances in high resolution profiling problems; for example in [13] two constant modulus phase modulated signals are developed and successfully tested. Moreover, in this particular field, modern digital processing techniques provide the possibility to develop low complexity pseudo-noise (PN) signal generators and to perform matched filtering by using commercial field programmable gate arrays (FPGAs) [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Processing Architecture for Range Profiling Using Noise Radar Technology

2018

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Abstract:The importance of high resolution range profiles (HRRPs) for radar applications like tracking or classification is well known. In the scientific literature several approaches have been investigated to obtain HRRPs from wideband radar signals. Recent works show that noise radar waveforms can be exploited in this sense due to their high resolution and low peak to sidelobe ratio (PSLR) properties. However their use can cause some issues in applications where long time integrations are required, e.g., in the presence of a low effective radiated power (ERP) transmitter: recording the reference signal in this case would be difficult due to the big quantity of data. This work proposes a real time digital processing schematic based on linear feedback shift registers (LFSRs) which is very flexible and has a low computational burden: its internal state can be easily controlled and reproduced in reception, where a multichannel correlator is exploited as matched filter. The resulting signal, compared to typical "pulse compression" and noise radar waveforms, shows similar performances but a lower order of complexity in terms of real time generation and reception.

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FPGA based random waveform generators for noise radars

Cited by 10 publications

References 3 publications

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications

On a Closer Look of a Doppler Tolerant Noise Radar Waveform in Surveillance Applications

Artificial Intelligence applications in Noise Radar Technology

An Efficient Processing Architecture for Range Profiling Using Noise Radar Technology

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