Probability Of False Alarm Research Articles

Pilot Contamination Attack Detection Methods—An Exhaustive Performance Evaluation Through Probability Metrics and Statistical Classification Parameters

Among the numerous strategies that an attacker can initiate to enhance its eavesdropping capabilities is the Pilot Contamination Attack (PCA). Two promising methods, based on Phase-Shift Keying (PSK) modulation of Nth order—2-N-PSK and Shifted 2-N-PSK, can detect an existing PCA by means of analysis of the constellation that the correlation product of received pilot signals belongs to. The overall efficiency of the methods can be studied by the most commonly used probability metrics—detection probability and false alarm probability. However, this information may be insufficient for comparison purposes; therefore, to acquire a more holistic perspective on the methods’ performances, statistical evaluation metrics can be obtained. Depending on the particular application of the system in which the PCA detection methods are incorporated and the distribution of attack initiation among all samples, different classification parameters are of varying significance in the efficiency assessment. In this paper, 2-N-PSK and Shifted 2-N-PSK are comprehensively studied through their probability parameters. In addition, the methods are also compared by their most informative statistical parameters, such as accuracy, precision and recall, F1-score, specificity, and fall-out. A large number of simulations are carried out, the analyses of which indisputably prove the superior behavior of the Shifted 2-N-PSK compared to the 2-N-PSK detection method. Since a method’s performance is strongly related to the number of antenna elements at the base station, all simulations are conducted for scenarios with different antennae numbers. The most promising realization of Shifted 2-N-PSK improves the receiver operating characteristics results of the original 2-N-PSK by 7.38%, 4.33%, and 5.61%, and outperforms the precision recall analyses of 2-N-PSK by 10.02%, 4.82% and 3.86%, for the respective number of 10, 100 and 300 antenna elements at the base station.

Devising a method for detecting an aerial object by radar with an additional channel of passive reception

The object of this paper is the process of detecting an aerial object by a radar with active and passive reception channels. The main hypothesis of the study assumed that the introduction of an additional channel of passive reception would increase the conditional probability of correct detection at a fixed value of the conditional probability of false alarms. When detecting an aerial object, it was considered that the signals from aerial object represent a reflected signal after being emitted by the active radar channel elemitted own radio signals. A radar with active and passive reception channels assumes the presence of two channels. The active location channel provides reception of signals reflected from an aerial object, their processing and detection according to the Neumann-Pearson criterion. The passive location channel functions according to the principle of panoramic spectral analysis based on windowed Fourier transforms. The information combining device is intended for the compatible combining of information from active and passive channels of radar reception. At the output of the passive location channel, an output signal is formed, and the coordinates of the aerial object are measured. To ensure the functioning of the radar with active and passive reception channels, it is necessary to ensure the time synchronization of the reception channels. The quality of detection of aerial objects by radar with active and passive signal reception channels was evaluated. The quality indicator defines the conditional probability of correct detection. The dependences of the conditional probability of correct detection are given for a radar with only an active reception channel and a radar with active and passive reception channels. It was established that the introduction of an additional channel of passive reception to the radar increases the conditional probability of correct detection by an average of (20–40) %, depending on the signal-to-noise ratio.

Weighted joint LRTs for cooperative spectrum sensing using K-means clustering

In this paper, a blind centralized cooperative spectrum sensing (CSS) with soft decision fusion is considered. The fusion center (FC) constructs the energy vector from the collaborating secondary nodes. The energy feature is assumed to be an efficient measure, as it is widely used and directly correlates with the strength of the received signal. The K-means clustering algorithm is employed to extract descriptive statistical features about the distributions of the absence and presence of the primary user (PU) signal, such as the mean and non-centrality parameter. In the framework of this statistical analysis, the signal-to-noise ratio (SNR) at each node is easily estimated and examined. Equal, selective, and weighted combining techniques are applied to develop three joint likelihood ratio test (JLRT)-based algorithms. These algorithms are implemented in the context of simple hypothesis testing, where the distribution of the data is fully specified. Furthermore, they are justified by the Neyman-Pearson theorem, which constructs the most powerful test for a given significance level. The proposed selective and weighted JLRT approaches are based on the estimated SNRs at each sensor, reflecting their reliability. Several comparison scenarios between the proposed algorithms, K-means, fuzzy c-means (FCM), and OR-rule are simulated over Rayleigh fading channel with low average SNR and few samples. The simulation results reveal that the proposed tests outperform other CSS techniques. Additionally, asymptotic theoretical expressions for probability of detection and the probability of false alarm are derived, which show high agreement with the simulated results.

Performance analysis of mean level CFAR detectors in homogeneous gamma-distributed radar clutter

ABSTRACT In this paper, a novel and exact mathematical formulation of three mean level constant false alarm rate (CFAR) detectors has been developed considering a homogeneous gamma-distributed (GM) clutter. These detectors include the cell averaging (CA), the greatest-of (GO), and the smallest-of (SO)-CFAR detection schemes. To do so, we derived closed-form expressions for the probability density function (pdf), and the cumulative distribution function (cdf) of the sum of an exponential fluctuating target submerged in gamma-distributed sea clutter. Then, we developed exact and explicit expressions for the probability of false alarm (P fa ) and probability of detection (P d ) of the optimal fixed threshold detector and the three considered CFAR detectors. The correctness of our expressions is then examined and validated via numerical simulations. Assuming a homogeneous GM background, the detection performance of these three detectors is carried out, discussed, and compared with that of the optimal detector. Experimental results with Sentinel-1 synthetic aperture radar (SAR) data demonstrate the effectiveness of the proposed closed-form expressions and show that the considered CFAR schemes are efficient for ship detection in sea clutter backgrounds.

Secure distributed estimation via an average diffusion LMS and average likelihood ratio test

Secure distributed estimation algorithms are designed to protect against a spectrum of attacks by exploring different attack models and implementing strategies to enhance the resilience of the algorithm. These models encompass diverse scenarios such as measurement sensor attacks and communication link attacks, which have been extensively investigated in existing literature. This paper, however, focuses on a specific type of attack: the multiplicative sensor attack model. To counter this, the paper introduces the Average diffusion least mean square (ADLMS) algorithm as a viable solution. Furthermore, the paper introduces the Average Likelihood Ratio Test (ALRT) detector, which provides a straightforward detection criterion. In the presence of communication link attacks, the paper considers the manipulation attack model and presents an ALRT adversary detector. The analysis extends to these ALRT detectors, encompassing the calculation of adversary detection probability and false alarm probability, both achieved in closed form. The paper also provides the mean convergence analysis of the proposed ADLMS algorithm. Simulation results reveal that the proposed algorithms exhibit enhanced performance compared to the DLMS algorithm, while the incremental complexity remains only marginally higher than that of the DLMS algorithm.

Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems

This paper exploits multi-modal Physical (PHY)-layer features in terms of artificial fingerprint, In-phase/Quadrature (IQ) imbalance and Angle of Arrival (AoA) to propose a novel PHY-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-enabled communication system. First, we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility. To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques, artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs. Then, we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities. To theoretically evaluate the effectiveness of the proposed authentication framework, the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing. Finally, we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models, and also discuss the relation between system security and communication service quality under different artificial fingerprint level.

Adaptive approach to spectrum monitoring in cognitive radio networks through signal detection optimization

The article considers the improvement of the adaptive algorithm of the spectral monitoring method for cognitive radio networks by introducing adaptive wavelet transforms and filters. The use of adaptive Morle and Dobechy wavelet transforms, as well as adaptive Kalman, LMS, and RLS filters is proposed, which allows dynamically changing parameters depending on the conditions of the radio environment. The comparative analysis with traditional methods showed that adaptive methods significantly increase the efficiency of signal detection in conditions of low SNR values, reducing the noise level, improving the accuracy of signal detection and reducing the probability of false alarms. The results of the study confirm the perspective of using adaptive methods to increase the reliability and efficiency of spectral monitoring in real operating conditions.

Monte Carlo Simulations of Spent Nuclear Fuel Dry Storage Cask Measurements with a New External Remote Monitoring System for Developing Safeguards Approaches

Until a long-term solution for the disposal of spent nuclear fuel (SNF) is available, interim dry casks will be increasingly used for the storage of SNF discharged from civilian nuclear power reactors. Dry casks containing commercial SNF may hold several significant quantities of plutonium, so appropriate nuclear material safeguards monitoring is needed. An external remote monitoring system (RMS) has been developed to advance dry cask safeguards monitoring beyond the current method of containment and surveillance used to maintain continuity of knowledge. In this study, neutron transport simulations of SNF assemblies in a dry cask were performed for several special nuclear material diversion scenarios. The simulations considered various loading patterns and fuel storage durations as long as 100 years. For each fuel loading pattern and storage time investigated, the simulation results were used to calculate the required measurement time to achieve a nondetection probability β ≤ 10% for the diversion of any single fuel assembly in the cask. The calculations were performed for false alarm probabilities α as low as 0.0001% (or 10−6). A Monte Carlo postprocessing approach was developed to consider the impact on the required measurement time of uncertainty in the burnup of fuel assemblies. The study found that the external RMS is well suited for the surveillance of SNF in dry cask storage for nuclear safeguards or other purposes and is able to detect the diversion of a single SNF assembly even after decades of storage and with a very low false alarm probability.

Fusion neutron source and array of particle detectors for nondestructive interrogation of special nuclear materials

Presented herein are the outcomes of an experimental test involving a pioneering portable-active interrogation system designed for the nondestructive detection of special nuclear materials (SNMs). The system relies on the threshold energy neutron analysis concept and incorporates a portable deuterium–deuterium (DD) neutron generator producing a particle intensity of 5 × 107 n/s, coupled with three arrays of tensioned metastable fluid detectors (TMFDs) to detect secondary neutrons from the fissile material. In the presence of the fissile material, prompt fission neutrons are emitted, with an average energy of approximately 2 MeV, and around 30% of these neutrons have energies above that of the DD neutron source (2.45 MeV). The detection of a statistically significant neutron population exceeding this threshold firmly indicates the presence of SNM. TMFDs exhibit high sensitivity in efficiently detecting neutrons above the threshold while adeptly discriminating against neutrons below the threshold as well as gamma rays. This unique feature allows the interrogation system to maintain a lightweight profile without necessitating substantial shielding materials. The validation experiments involved the placement of 70 or 140 g masses of U-235 within a 1 m3 inspection volume. Measurements were carried out over 30 min intervals, repeated numerous times, both with and without U-235, at a DD neutron source intensity of 8 × 105 n/sec. Experimental count rates with natural uranium (NU) are consistently above those without NU. The probability of detection (PD) and probability of false alarm (PFA) were assessed utilizing these count rates. The DD neutron source intensity and inspection time were normalized at 5 × 107 n/sec and 90 s, respectively. The results indicated a PD of approximately 74% and 98% for detecting 70 and 140 g of U-235, respectively, with a PFA of <5%. These promising outcomes align with the specified PD (>90%) and PFA (<5%) targets outlined in ANSI standards.

A hybrid deep learning based approach for spectrum sensing in cognitive radio

The primary user (PU) transmission is sporadic in nature, which explains why the PU is inactive during some time slots, geographic directions or frequency bands. The frequency bands where the PU is not active are called "spectrum holes". Secondary users (SUs) periodically perform sensing to detect the spectrum holes and monitor primary spectrum. For the best possible spectrum utilization, PU signal detection is very crucial. For measuring the spectrum sensing performance, two main metrics are applied, like, probability of false alarm (PFA) and probability of detection (PD). Due to PFA and PD, the conventional sensing techniques have to face issues. These two constraints used to hinder spectrum utilization. Traditional sensing strategies are mostly based on feature extraction of received signal. Advancement of artificial intelligence (AI) has reduced the inaccuracy in detection of spectrum hole. Deep learning (DL) based approaches have shown a remarkable improvement in this aspect. Hence, the present research work was undertaken to address the problem of spectrum sensing in low SNR and improves accuracy. This research penetrates into the use of deep neural network (DNN) for sensing the vacant spectrum accurately. In this article, RadioML2016.10b dataset was used for the experiments. The results are also studied. The proposed approach shows betterment in sensing than other existing spectrum detection models. DeepSenseNet model was validated through simulation results and showed that it has achieved 98.84% prediction accuracy (Pa) with 97.53% precision and 97.62% recall.

Methodology for assessing the effectiveness of the impact intermittent noise interference to a radar station with adaptive detection threshold formation

Problem statement. For aviation suppression equipment, it is characteristic to separate the modes of interference and reconnaissance radiation in time due to the problem of their electromagnetic compatibility. It is also possible to alternately suppress various objects with a limited bandwidth of the interference station. This leads to periodic interruptions of the noise interference and, consequently, a decrease in its spectral density. A feature of radar stations of the latest and modernized means of intercepting air defense systems aimed at hitting targets in a complex interference environment is adaptive control of parameters and modes of operation of various subsystems. In particular, modern radar stations provide for the adaptive formation of a detection threshold based on estimates of the statistical characteristics of such interference. Such estimates are formed over the interference integration interval, the size of which is generally selected from the condition of insignificant influence of signals reflected from the targets on them. Moreover, for the most correct consideration of the interference situation in the vicinity of the detected target, the strobe pulses of the false alarm probability stabilization circuit, forming the integration interval, are placed on both sides of the analyzed range resolution element. Goal. To evaluate the effect of intermittent noise interference parameters on the operation of radar stations with adaptive detection threshold foration. Assumptions. When developing the methodology, the following typical assumptions were made: in target detection mode, the radar station emits pulse signals with a low or medium pulse repetition rate; the signal reflected from the target, noise interference and internal noise are independent normal random processes; interference and internal noise have a uniform spectral density within the bandwidth of the radar receiver; the receiving path of the radar station has a traditional structure, which includes: an intermediate frequency amplifier, a linear amplitude detector, an integrator and a threshold device. Results. For the first time, the obtained technique takes into account the influence of time and energy parameters of intermittent noise interference on the probability of correct target detection by a radar station with adaptive detection threshold formation. Reducing the duration of the interference pulse in the period of intermittent noise interference leads to a nonlinear increase in the probability of correct target detection. To maximize this indicator, it is necessary to ensure a sufficiently high accuracy in estimating the statistical characteristics of the power of intermittent noise interference. The reliability of the results obtained using the developed methodology is confirmed by the fact that the effectiveness of intermittent noise interference, while excluding pauses in its radiation, is reduced to the effectiveness of continuous noise interference. Practical significance. The developed technique makes it possible to clarify the technical requirements for radar stations that implement flexible control of the target detection mode under the influence of intermittent noise interference of unknown intensity.

Continuity risk evaluation of the Bayesian posterior integrity monitoring against multiple faults

Compared to the prior integrity monitoring of global navigation satellite system (GNSS), the posterior integrity monitoring incorporates the used GNSS measurements into the computation of integrity risk, thereby providing a more accurate representation of the reliability of position solution. However, the continuity risk evaluation in posterior integrity monitoring remains an unresolved issue. After formulating the posterior integrity risk of each event hypothesis as a parity function, we propose a method to evaluate the posterior continuity risk by computing the cumulative probability of false alarm within a sphere constructed in the parity space. This approach guarantees a conservative estimate of the probability of false alarm, thereby establishing an upper bound for posterior continuity risk. Experiment and analysis based on positioning cases involving GPS, Galileo, and BeiDou satellites suggest that the posterior integrity monitoring can achieve a lower probability of false alarm compared to prior integrity monitoring under certain integrity monitoring configurations. Furthermore, the evaluation accuracy of probability of false alarm inversely correlates with the severity of the differences in solution separation variances.

Identifying the electromagnetic counterparts of LISA massive black hole binaries in archival LSST data

ABSTRACT LSST will catalogue the light curves of up to 100 million quasars. Among these there can be $\sim$100 ultra-compact massive black hole (MBH) binaries, whose gravitational waves (GWs) can be detected 5–15 yr later by LISA. Here, we assume such a LISA detection occurred, and assess whether or not its electromagnetic (EM) counterpart can be identified as a periodic quasar in archival LSST data. We use the binary’s properties derived from the LISA waveform, including the evolution of its orbital frequency, its total mass, distance, and sky localization, to predict the redshift, magnitude, and historical periodicity of the quasar expected in the LSST data. We then use Monte Carlo simulations to compute the false alarm probability (FAP), i.e. the number of quasars in the LSST catalogue matching these properties by chance, based on the (extrapolated) quasar luminosity function, the cadence of LSST, and intrinsic ‘damped random walk’ quasar variability. We analyse four fiducial LISA binaries, with masses and redshifts of $(M_{\rm bin}/{\rm M_{\odot }},z) = (3\times 10^5,0.3)$, $(3\times 10^6,0.3)$, $(10^7,0.3)$, and $(10^7,1)$. While noise and aliasing due to LSST’s cadence produces false periodicities by chance, we find that the frequency chirp of the LISA source during the LSST observations washes out these noise peaks and allows the genuine source to stand out in appropriately scaled Lomb–Scargle periodograms. We find that all four fiducial binaries can be uniquely identified, with ${\rm FAP}\lt 10^{-5}$, a week or more before merger. This should enable follow-up EM observations targeting individual EM counterparts during their inspiral stage.

A Combined Data-Driven and Model-Based Algorithm for Accurate Battery Thermal Runaway Warning.

With the increasingly widespread application of large-scale energy storage battery systems, the demand for battery safety is rising. Research on how to detect battery anomalies early and reduce the occurrence of thermal runaway (TR) accidents has become particularly important. Existing research on battery TR warning algorithms can be mainly divided into two categories: model-driven and data-driven methods. However, the common model-driven methods are often of high complexity, with poor versatility and low early warning capability; and the common data-driven methods are mostly based on neural networks, requiring substantial training costs, with better early warning capabilities but higher false alarm probabilities. To address the limitations of existing works, this paper proposes a combined data-driven and model-based algorithm for accurate battery TR warnings. Specifically, the K-Means algorithm serves as the data-driven module, capturing outliers in battery data, and the Bernardi equation serves as the model-driven module used to evaluate battery temperature. Ultimately, the outputs of the weighted model-driven module and data-driven module are combined to comprehensively assess whether the battery is abnormal. The proposed algorithm combines the advantages of model-driven and data-driven approaches, achieving a 25 min advance warning for thermal runaway, with a significantly reduced probability of false alarms.

Exploiting Cascaded Channel Signature for PHY-Layer Authentication in RIS-Enabled UAV Communication Systems

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications face a critical security threat from impersonation attacks, where adversaries impersonate legitimate entities to infiltrate networks to obtain private data or unauthorized access. To combat such security threats, this paper proposes a novel physical layer (PHY-layer) authentication scheme for validating UAV identity in RIS-enabled UAV wireless networks. Considering that most existing works focus on traditional communication systems such as IoT and millimeter wave multiple-input multiple-output (MIMO) systems, there is currently no mature PHY-layer authentication scheme to serve RIS-UAV communication systems. To this end, our scheme leverages the unique characteristics of cascaded channels related to RIS to verify the legitimacy of UAV transmitting signals to the base station (BS). To be more precise, we first use the least squares estimate method and coordinate a descent-based algorithm to extract the cascaded channel feature. Next, we explore a quantizer to quantize the fluctuations of the channel gain that are related to the extracted channel feature. The 1-bit quantizer’s output findings are exploited to generate the authentication decision criteria, which are then tested using a binary hypothesis. The statistical signal processing technique is utilized to obtain the analytical formulations for detection and false alarm probabilities. We also conduct a computational complexity analysis of the proposed scheme. Finally, the numerical results validate the effectiveness of the proposed performance metric models and show that our detection performance can reach over 90% accuracy at a low signal-to-noise ratio (e.g., −8 dB), with a 10% improvement in detection accuracy compared with existing schemes.

IRS assisted spectrum sensing in cognitive radio network with grey wolf optimization

Cognitive radio (CR) is of crucial importance in providing efficient management of limited spectrum resources. However, its performance relies on efficient spectrum sensing. This paper investigates a novel approach for CR networks that leverages intelligent reflecting surface (IRS) specifically for spectrum sensing and non-orthogonal multiple access (NOMA) for data transmission. We propose a Grey-Wolf Optimization (GWO) based IRS optimization approach to maximize spectrum sensing performance. Independent of the IRS, NOMA is employed to improve spectral efficiency during data transmission. The performance is evaluated in terms of throughput and spectrum sensing parameters, namely probability of false alarm and missed detection. Numerical and simulation results demonstrate that GWO-based IRS optimization significantly outperforms conventional nature-inspired algorithms, achieving approximately 97% improvement in spectrum sensing accuracy. Based on the improved spectrum sensing results, the effective data transmission throughput is evaluated and validated through extensive simulation.

Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade

Structural Health Monitoring Systems (SHMS) are currently widely investigated in the literature, however, their application to the aerospace industry is still limited. One of the reasons lies in the lack of methods aimed at evaluating their economic impact on the lifecycle of the structural element to be monitored [1]. In this work the economic impact of FBG-based SHMS was evaluated on a composite helicopter rotor blade, considering two perspectives: Beginning Of Life (BOL), concerning the blade production, and Middle Of Life (MOL), concerning its usage. The processes were modeled using the IDEF0 methodology, which allowed deriving the cost models for two scenarios: the current one, and the one including the SHMS. In BOL, the SHMS was supposed providing support to the development of new composite rotor blades, including the curing cycle definition and certification tests. In MOL the SHMS was supposed performing automatic scheduled inspections coupled with the traditional ones. The cost model was implemented in the Probabilistic Damage Tolerance Analysis [2] to compare the scenarios having the same blade Probability Of Failure and considering the performances of the SHMS in terms of Probability Of Detection and Probability of False Alarm. Results show that an economic benefit may be achieved using the SHMS in the development of new blades, potentially reducing the number of blades tested and number of autoclave cycles. Moreover, it was found that the traditional scheduled detailed inspection intervals could be extended if automatic SHMS inspections are performed in addition, maintaining the same Probability Of Failure. [1] Büchter, K. D. et al. (2022). Modeling of an aircraft structural health monitoring sensor network for operational impact assessment. Structural Health Monitoring, 21(1), 208-224. [2] Lin, K. Y., & Styuart, A. V. (2007). Probabilistic approach to damage tolerance design of aircraft composite structures. Journal of Aircraft, 44(4), 1309-1317.

CFAR Target Detector in Synthetic Aperture Radar

Introduction. Constant false alarm rate (CFAR) detectors have found application in synthetic aperture radar (SAR) systems. The operating principle of a classic cell averaging detector (CA-CFAR detector) is based on comparing the decision statistics in the test resolution element with an adaptive threshold, which is calculated from signals in the reference cells. The decision statistic is an estimate of the signal power. Therefore, target signal detection is based on the brightness contrast of the test and reference resolution cells. Such a detector is optimal provided that the noise background is homogeneous. In cases where the background homogeneity is violated, the quality of detection deteriorates. There are several known methods for improving the quality of detection (GO-CFAR, SO-CFAR, OS-CFAR, etc.). However, the precise principle of detection by brightness contrast in such CFAR detectors remains unchanged.Aim. To synthesize a CFAR detector that uses not only the brightness contrast between the test and reference resolution cells, but also the spectral differences of the signals.Materials and methods. The proposed CFAR detector uses estimates of the algebraic moments of the power spectral density of signals in range cells, based on which three decision statistics are calculated containing information about the power, the position of the energy center, and the width of the signal spectrum. The decision about the presence of a target in the test cell is carried out according to the 2/3 rule (2 threshold overshoots out of 3 comparisons).Results. A comparison of the proposed detector with the SO-CFAR detector, performed by computer simulation, showed that, under a signal-to-clutter ratio of -6 dB and a false alarm probability of 10-4, the detection probability of the proposed detector was 0.933 versus 0.708 for the SO-CFAR detector.Conclusion. The article proposes a three-parameter CFAR detector for a synthetic aperture radar system, in which the decision on the presence of a target in the test cell is made via estimation of the first three algebraic moments of the signal spectrum. The synthesized detection algorithm can also be used when detecting moving targets in SAR.

An improved correction of radial velocity systematics for the SOPHIE spectrograph

High-precision spectrographs can on occasion exhibit temporal variations in their reference velocity or nightly zero point (NZP). One way to monitor the NZP is to measure bright stars, whose intrinsic radial velocity variation is assumed to be much smaller than the instrument precision. The variations of these bright stars, which is primarily assumed to be instrumental, are then smoothed into a reference radial velocity time series (master constant) that is subtracted from the observed targets. While this method is effective in most cases, it does not fully propagate the uncertainty arising from NZP variations. We present a new method for correcting for NZP variations in radial velocity time series. This method uses Gaussian processes based on ancillary information to model these systematic effects. Moreover, it enables us to propagate the uncertainties of this correction into the overall error budget. Another advantage of this approach is that it relies on ancillary data that are collected simultaneously with the spectra and does not solely depend on dedicated observations of constant stars. We applied this method to the SOPHIE spectrograph at the Haute-Provence Observatory using a few instrument housekeeping data, such as the internal pressure and temperature variations. Our results demonstrate that this method effectively models the red noise of constant stars, even with a limited number of housekeeping data, while preserving the signals of exoplanets. Using simulations with mock planets and real data, we found that this method significantly improves the false-alarm probability of detections. It improves the probability by several orders of magnitude. Additionally, by simulating numerous planetary signals, we were able to detect up to 10% more planets with small-amplitude radial velocity signals. We used this new correction to reanalyse the planetary system around HD158259 and to improve the detection of the outermost planets. We propose this technique as a complementary approach to the classical master-constant correction of the instrumental red noise. We also suggest to decrease the observing cadence of the constant stars to optimise the telescope time for scientific targets.

Model selection techniques for seafloor scattering statistics in synthetic aperture sonar images of complex seafloors

AbstractIn quantitative analysis of seafloor scattering measurements, it is common to model the single‐point probability density function of the scattered intensity or amplitude. For more complex seafloors, the pixel amplitude distribution has previously been modelled with a mixture model consisting of two K distributions, but the environment may have more identifiable scattering mechanisms. Choosing the number of components of a mixture model is a decision that must be made, using a priori information, or using a data driven approach. Several common model selection techniques from the statistics literature are explored (the Akaike, Bayesian, deviance, and Watanabe‐Akaike information criteria) and compared to the authors' choice. Examples are given for synthetic aperture sonar data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway, using the HISAS‐1032 synthetic aperture sonar system. The Bayesian information criterion aligned most closely with the interpretation of both the acoustic images and the plots of the probability of false alarm.