Detection Probabilities for Log-Normally Distributed Signals

Heidbreder, Glenn R.; Mitchell, R.

doi:10.21236/ad0485829

Cited by 3 publications

(4 citation statements)

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“…The curves highlight that the correlation provides a beneficial effect on the performance of the angular estimate. In general, the higher the ρ 0 , the lower the

E (][, normalΔ u_{S}^{2})

and

\sqrt{E [Δ θ_{S}^{2}]}

. A possible future work might concern the theoretical analysis of the sequential lobing technique for independent and/or correlated log‐normal [18] or Weibull fluctuating targets [19].…”

Section: Discussionmentioning

confidence: 99%

Theoretical analysis of the sequential lobing technique for correlated targets

Cui

Maio

Pallotta

et al. 2013

IET Radar, Sonar & Navigation

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“…The curves highlight that the correlation provides a beneficial effect on the performance of the angular estimate. In general, the higher the ρ 0 , the lower the

E (][, normalΔ u_{S}^{2})

and

\sqrt{E [Δ θ_{S}^{2}]}

. A possible future work might concern the theoretical analysis of the sequential lobing technique for independent and/or correlated log‐normal [18] or Weibull fluctuating targets [19].…”

Section: Discussionmentioning

confidence: 99%

Theoretical analysis of the sequential lobing technique for correlated targets

Cui

Maio

Pallotta

et al. 2013

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…The log-normal distribution is used in radar detection and has been widely accepted [20]. As discussed above, the ADC error for scenes with isolated strong targets is mainly caused by saturation, which leads to false objects and parasitic sidelobes in a given range.…”

Section: Maximum a Posteriori (Map) Log-normal Distributionmentioning

confidence: 99%

“…However, the applicability of the method is limited, and it can only be used in simple background scenes. Additionally, a priori condition does not have a physical model for support.In this paper, we consider using a log-normal distribution to describe a large dynamic range scene [20,21] and determine the maximum a posteriori (MAP) signal of the raw data. According to the physical model, the optimization algorithm in [18] is updated for use in more general cases.…”

mentioning

confidence: 99%

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Reconstruction of Synthetic Aperture Radar Raw Data under Analog-To-Digital Converter Saturation Distortion for Large Dynamic Range Scenes

et al. 2019

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Digital storage and transmission are common processes in modern synthetic aperture radar systems; thus, analog-to-digital converters are indispensable. Such processes can lead to two types of error: quantization (or granular) error and saturation (or clipping) error, which cause sampling noise, and radiometric and harmonic distortions in final images. Traditionally, reasonable choices of the gain and the number of quantization bits by the analog-to-digital converter based on the echo distribution can effectively reduce these errors. However, establishing the gain control repository of a synthetic aperture radar mission is a long process. In addition, if the dynamic range of the backscattering coefficient is extremely large or if unexpected strong targets appear in a scene, then harmonics occur in the echo, which turns the variable gain amplifier into chaos based on statistic and, inevitably, results in saturation in the raw data. Once raw data saturation occurs, the SAR system can conventionally adjust only the analog-to-digital converter in the next observation, thus reducing timeliness. Power loss compensation based on a statistical model and saturation (clipping) factor on a large-scale could compensate for the energy loss in images; however, detail interference, such as harmonic distortion, cannot be effectively suppressed, which will lead to false targets in the focused data. To address this particular problem, a novel anti-saturation method for large dynamic range scenes is proposed in this paper. The log-normal distribution is used in this article to describe dynamic range scenes with strong isolated targets, which mainly cause receiver saturation. Using the statistical distribution of complex scenes as a priori information, a maximum a posteriori estimation algorithm is proposed to simultaneously compensate for the saturated values in the raw data and retain the non-saturated values. Thus, the details of the weak background are well preserved, and the isolated strong targets with sparsity are reconstructed perfectly. With Monte Carlo simulation, the proposed method can improve the radiometric accuracy by 5 to 10 dB and effectively suppress the energy of false targets. Based on TerraSAR-X, ALOS-2, and Radarsat-1 synthetic aperture radar data, the effectiveness and robustness of the proposed method are also verified by simulations. associated with the analog-to-digital converter (ADC) and storage on the satellite platform, the radar echo is amplified to an appropriate level by an intermediate amplifier and then sampled with small quantization bits. After that, block adaptive quantization (BAQ) or a similar technology compresses the samples again at a lower bitrate for storage, transmission, and imaging [1]. In the data flow, there are four steps that can lead to nonlinear distortions: (a) The saturation of an analogue amplifier for the received echo, (b) ADC for the down-converted echo, (c) lossy compression for digital data, and (d) image quantization for focused data. Currently, due to the development of ...

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