Low False Alarm Probability Research Articles

Amplitude quantization method for autonomous threshold estimation in self-reconfigurable cognitive radio systems

Self-adaptive threshold adjustment algorithms (SATAs) are required to reconfigure their parameters autonomously (i.e. to achieve self-parameter adjustment) at runtime and during online use for effective signal detection in cognitive radio (CR) applications. In this regard, a CR system embedded with the functionality of a SATA is termed a self-reconfigurable CR system. However, SATAs are challenging to develop owing to a lack of methods for self-parameter adjustment. Thus, a plausible approach towards realizing a functional SATA may involve developing effective non-parametric methods, which are often pliable to achieve self-parameter adjustment since they are distribution-free methods. In this article, we introduce such a method termed the non-parametric amplitude quantization method (NPAQM) designed to improve primary user signal detection in CR without requiring its parameters to be manually fine-tuned. The NPAQM works by quantizing the amplitude of an input signal and then evaluating each quantized value based on the principle of discriminant analysis. Then, the algorithm searches for an effective threshold value that maximally separates noise from signal elements in the input signal sample. Further, we propose a new heuristic, which is an algorithm designed based on a new corollary derived from the Otsu’s algorithm towards improving the NPAQM’s performance under noise-only regimes. We applied our method to the case of the energy detector and compared the NPAQM with other autonomous methods. We show that the NPAQM provides improved performance as against known methods, particularly in terms of maintaining a low probability of false alarm under different test conditions.

Vibration Detection in Distributed Acoustic Sensor With Threshold-Based Technique: A Statistical View and Analysis

Detecting vibrations with high probability and low false alarm probability is crucial for prompting distributed acoustic sensors (DASs) to real applications. It is known that detection performance mainly depends on signal-to-noise ratio (SNR) and many efforts have been made to improve it. However, the relationship between SNR and detection performance has not been quantitatively analyzed so far. Threshold-based vibration detection is a simple and commonly used technique, but how to set the decision threshold in DAS is still an open question. In this work, for the first time, we propose a model to quantify the relationship between SNR and detection performance and provide a method for setting the decision threshold. Firstly, we build a model to differentiate vibrations from the background noise based on their short-time average energy. This model reveals that setting decision threshold requires perfect knowledge of noise power, which is a difficult task in DAS since noise power varies frequently with time and position. To solve this problem, secondly, we propose a noise-irrelevant threshold setting method based on autocorrelation-energy. Finally, experimental validation is performed on a DAS system along 47.4km sensing fiber with 5m spatial resolution. Results of autocorrelation-energy-based method show 100% and 98.1% detection probability for two vibrations with $1.12 \times {10^{ - 7}}$ false alarm probability in a one-hour measurement period.

Real-Time Plume Detection and Segmentation Using Neural Networks

Applications of artificial intelligence have been gaining extraordinary traction in recent years across innumerable domains. These novel approaches and technological leaps permit leveraging profound quantities of data in a manner from which to elucidate and ease the modeling of arduous physical phenomena. ExoAnalytic collects over 500,000 resident space object images nightly with an arsenal of over 300 autonomous sensors; extending the autonomy of collection to data curation, anomaly detection, and notification is of paramount importance if elusive events are desired to be captured and classified. Efforts begin with rigorous image annotation of observed glints, streaking stars, and resident space objects with plumes from debris shedding events. Preliminary results permitted the successful classification of observed debris generating events from AMC-9, Telkom-1, and Intelsat-29e. After initial proof-of-concept, these events are incorporated into the training pipeline in order to characterize potentially unknown debris generating or anomalous events in future observations. The inclusion of a visual tracking system aides in reducing false alarms by roughly 30%. Future efforts include applications on both historical datamining as well as real-time indications and warnings for satellite analysts in their daily operations while maintaining a low probability of false alarm through detection and tracking algorithm refinement.

Adaptive multi-threshold object selection in remote sensing images

Introduction: Detection, selection and analysis of objects of interest in digital images is a major problem for remote sensing and technical vision systems. The known methods of threshold detection and selection of objects avoid using the processing results, therefore not providing a low probability of false alarms, and not keeping the shape of the selected objects well enough. There are only few results from the studies about quantifying the quality of such algorithms on either model or real images. Purpose: Studying the effectiveness of algorithms for detecting, selecting, and localizing objects of interest using their geometric characteristics, when the object properties and background are a priori uncertain, and the shape of the selected objects is kept unchanged. Results: We have obtained and studied the characteristics of algorithms for detecting and selecting objects of interest on test models of monochrome images. These software-implemented algorithms use multi-threshold processing, providing a set of binary slices. This makes it possible to perform morphological processing of objects on each slice in order to analyze their geometric characteristics and then select them according to geometric criteria, taking into account the percolation effect which causes changes in the area, and fragmentation of the objects. As a result of analyzing these changes, an adaptive detection threshold is set for each of the selected objects. The selection allows you to significantly reduce the number of false positives during the detection and to use lower thresholds, increasing the correct detection probability. We present the detection characteristics and the results of test model processing, as well as the results of object selection on a real television and radar image, confirming the effectiveness of the considered algorithms. Practical relevance: The proposed algorithms can more effectively select objects on images of various nature obtained in remote sensing, material research or medical diagnostics systems. Their microprocessor implementation is much simpler than the implementation of universal trainable neural network algorithms.

An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization. Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users (MUs), and spectrum sensing data falsification (SSDF) attack is one of the most harmful attacks on spectrum sensing performance. In this article, an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks. In this algorithm, secondary users (SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result. The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained. The fuzzy weight is calculated by the fuzzy entropy. The credibility weight is obtained by updating the credibility. On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula. Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms. It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.

Aircraft Mode S Transponder Fingerprinting for Intrusion Detection

Nowadays, aircraft safety is based on different systems and four of them share the same data-link protocol: Secondary Surveillance Radar, Automatic Dependent Surveillance System, Traffic Collision Avoidance System, and Traffic Information System use the Mode S protocol to send and receive information. This protocol does not provide any kind of authentication, making some of these applications vulnerable to cyberattacks. In this paper, an intrusion detection mechanism based on transmitter Radio Frequency (RF) fingerprinting is proposed to distinguish between legitimate messages and fake ones. The proposed transmitter signature is described and an intrusion detection algorithm is developed and evaluated in case of different intrusion configurations, also with the use of real recorded data. The results show that it is possible to detect the presence of fake messages with a high probability of detection and very low probability of false alarm.

Efficient modeling of correlated noise

Periodograms are common tools used to search for periodic signals in unevenly spaced time series. The significance of periodogram peaks is often assessed using false alarm probability (FAP), which in most studies assumes uncorrelated noise and is computed using numerical methods such as bootstrapping or Monte Carlo. These methods have a high computational cost, especially for low FAP levels, which are of most interest. We present an analytical estimate of the FAP of the periodogram in the presence of correlated noise, which is fundamental to analyze astronomical time series correctly. The analytical estimate that we derive provides a very good approximation of the FAP at a much lower cost than numerical methods. We validate our analytical approach by comparing it with Monte Carlo simulations. Finally, we discuss the sensitivity of the method to different assumptions in the modeling of the noise.

A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data

Typical radar detectors exploit only a small proportion of the valuable information contained in radar reflections, i.e. magnitude and Doppler. A neural network-based approach for augmenting traditional radar detector structures using machine learning (ML) is proposed in this paper. Specifically, the network is designed to augment target detection in the field of maritime wide area surveillance for non-coherent data. A combination network consisting of a convolutional neural network (CNN) to extract spatial features and a long short-term memory (LSTM) for extracting temporal patterns in the spatial features is proposed. The network augments the detector structure by blanking out regions of the frame which are classified as not containing a target, thus reducing false alarms. The network is tested on data containing four marine targets collected by a ground-based radar. The data set was chosen because it contains strong sea clutter returns. When ML is used, the receiver operating characteristic (ROC) curves are shifted to lower probability of false alarm (PFA). A Kalman filter tracker was applied to the ML-augmented and baseline detections, and it was shown that ML-augmented detections produced similar tracks at lower PFA. The feature discovering capability of the network is analyzed through a series of tests, and the argument is made that the CNN-LSTM network presented in this work demonstrates the ability to improve the detection performance by exploiting spatial and temporal information in the data.

User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection

In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.

User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection

In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.

Standard Condition Number Based Spectrum Sensing Under Asynchronous Primary User Activity

Cognitive radio (CR) is a promising technology which enables the secondary user (SU) to sense and detect the presence or absence of the primary user (PU) in the frequency band of interest. Therefore, high detection probability is needed to ensure that primary user is adequately protected, while low false-alarm probability provides the opportunity of using free channel and a better throughput performance of secondary user. Most of the studies on spectrum sensing techniques were conducted assuming a perfect synchronization between the secondary and the primary users. However, in practice, the synchronous assumption is very hard to satisfy in the context of real cognitive radio networks environment. In this paper, we present a theoretical formulation of the standard condition number (SCN) based spectrum sensing technique under an asynchronous cognitive radio networks. We assume the case where the primary users generate asynchronous slotted traffic. The standard condition number distributions under null and alternative hypotheses are derived where the secondary user is equipped with two receive antennas. Under asynchronous primary user traffic, we establish the existence of a lower limit of the false-alarm probability below which we are unable to derive a decision threshold for the SCN-based detector.

A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.

Detection of a recurrent static target using binary sensor networks

The authors address the problem of decentralised detection of a recurrent event produced by a static uncooperative target in a wireless network of spatially distributed sensors. The presence or absence of the target is determined by applying fusion rules on local binary decisions of individual sensors. Optimal fusion rules require complete statistical knowledge about local detection and false alarm probabilities; hence, in practice they are infeasible. Previous works, such as the counting rule test, scan statistics and the generalised likelihood ratio test, have suggested sub-optimal fusion rules based on the total number of detecting sensors. Neither method takes full advantage of the spatial or temporal correlation between adjacent sensors. In practice, the decisions of adjacent sensors are dependent both in space (as the target is static) and in time (as the event reoccurs). Herein, the authors propose an approach that capitalises on the temporal and spatial arrangement of the local decisions. Simulation examples demonstrate that the detection probability of the proposed spatio-temporal rule is significantly higher than that of the common methods, and also achieves a lower probability of false alarm. Moreover, real data results show that the proposed approach is robust to strong environmental noises.

How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?

We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust towards signal morphologies that differ from the training set, and they do not require many training injections/data to guarantee good detection efficiency and low false alarm probability. In fact, we only need to train one CNN on signal/noise maps in a single 150 Hz band; afterwards, the CNN can distinguish signals/noise well in any band, though with different efficiencies and false alarm probabilities due to the non-stationary noise in LIGO/Virgo. We demonstrate that we can control the false alarm probability for the CNNs by selecting the optimal threshold on the outputs of the CNN, which appears to be frequency dependent. Finally we compare the detection efficiencies of the networks to a well-established algorithm, the Generalized FrequencyHough (GFH), which maps curves in the time/frequency plane to lines in a plane that relates to the initial frequency/spindown of the source. The networks have similar sensitivities to the GFH but are orders of magnitude faster to run and can detect signals to which the GFH is blind. Using the results of our analysis, we propose strategies to apply CNNs to a real search using LIGO/Virgo data to overcome the obstacles that we would encounter, such as a finite amount of training data. We then use our networks and strategies to run a real search for a remnant of GW170817, making this the first time ever that a machine learning method has been applied to search for a gravitational wave signal from an isolated neutron star.

Enhanced spectrum sensing using a combination of energy detector, matched filter and cyclic prefix

In this paper, we propose a sensing scheme based on energy detection, matched filter and cyclic prefix. Both Equal Gain Combining (EGC) and optimal combination of the aforementioned detectors are investigated in cooperative and non-cooperative spectrum sensing scenarios. In packet transmission systems such as OFDM (Orthogonal Frequency Division Multiple access) systems, the proposed scheme takes advantage of utilizing more samples than individual detectors, i.e., cyclic prefix, training or pilot samples, and data payload samples. The proposed combine-sensing scheme offers higher detection probability and lower false alarm probability, as compared with the performance of individual detectors over the same frame duration. Simulation results are congruent with the theoretical curves and confirm the validity of our derivations.

Biases in retrieving planetary signals in the presence of quasi-periodic stellar activity

Abstract Gaussian process regression is a widespread tool used to mitigate stellar correlated noise in radial velocity (RV) time series. It is particularly useful to search for and determine the properties of signals induced by small-sized low-mass planets (Rp < 4 R⊕, mp < 10 M⊕). By using extensive simulations based on a quasi-periodic representation of the stellar activity component, we investigate the ability in retrieving the planetary parameters in 16 different realistic scenarios. We analyse systems composed by one planet and host stars having different levels of activity, focusing on the challenging case represented by low-mass planets, with Doppler semi-amplitudes in the range 1–3 $\rm{\,m\,s^{-1}}$. We consider many different configurations for the quasi-periodic stellar activity component, as well as different combinations of the observing epochs. We use commonly employed analysis tools to search for and characterize the planetary signals in the data sets. The goal of our injection-recovery statistical analysis is twofold. First, we focus on the problem of planet mass determination. Then, we analyse in a statistical way periodograms obtained with three different algorithms, in order to explore some of their general properties, as the completeness and reliability in retrieving the injected planetary and stellar activity signals with low false alarm probabilities. This work is intended to provide some understanding of the biases introduced in the planet parameters inferred from the analysis of RV time series that contain correlated signals due to stellar activity. It also aims to motivate the use and encourage the improvement of extensive simulations for planning spectroscopic follow-up observations.

Research on aircraft wake vortex recognition using AlexNet

In order to solve the flight safety issues threatened by wake vortex of leading aircraft, ensure air traffic safety, and improve the capacity of airdrome and airspace, an AlexNet convolutional neural network model algorithm is proposed to identify aircraft wake vortex. Combined with the detection principle of Doppler LiDAR and the classic model of Hallck-Burnham wake vortex velocity, the AlexNet neural network model was constructed to extract the image features of the wake vortex velocity images in the atmosphere and identify the aircraft wake vortex. The research shows that the model is able to accurately identify the aircraft wake vortex in the target airspace. After the network model converges, the accuracy rate reaches to 91.30%, which can effectively realize the identification work. Meanwhile, this study also demonstrates the low probability of false alarm of the AlexNet neural network in detecting wake vortex, which meets the requirement of early warning and monitoring of the aircraft wake vortex.

Research and Design of proximity fuze Scheme Based on 24GHz Radar Sensor

Modern warfare requires more and more adaptability to the environment. The proximity fuze based on microwave radar sensor has high anti-interference performance, which can achieve accurate hit and high efficiency damage in the complex environment. The microwave radar sensor has the characteristics of strong anti-interference ability, high precision of ranging and high speed measurement, and can accurately extract target information. This paper mainly introduces a design and implementation scheme of proximity fuze based on 24GHz microwave radar. With a small spatial structure and high integration, combined with the Doppler principle, antenna scanning technology and FFT system software design, the detonation range of the lowest 0.5m and 30m is determined effectively and reliably with a low false alarm probability.

Multiscale hysteresis threshold detection algorithm for a small infrared target in a complex background

In the infrared small target detection, the clutter formed by buildings, trees and protruding clouds is densely distributed and difficult to filter out. The hysteresis threshold detection algorithm utilizes the geometric features of small target to reduce false alarms. Images are filtered in multiple scales, the location and scale of the points of interest are extracted by non-maximum suppression. To determine the connection state of the focus and clutter, local gradient second-order origin moment is proposed to eliminate strong edges. The hysteresis threshold segmentation is performed to exclude stubborn false alarms and detect small targets. Experiments show that the proposed algorithm has a significant effect in removing false alarms, and achieves both the high detection probability and low false alarm probability.

An evaluation of statistical, NMME and hybrid models for drought prediction in China

The predictability of droughts in China was investigated using a series of statistical, dynamic and hybrid models. The results indicate that, statistical models exhibit better skill in forecasting the Standardized Precipitation Index in six months (SPI6) than dynamic models. Overall, the ensemble streamflow prediction (ESP) method and wavelet machine learning models outperform other statistical models in forecasting SPI6. The hybrid model can improve the performance of SPI6 forecast by combining statistical and dynamic models using Bayesian model averaging (BMA) method. As for drought onset detection, the ‘low probability of detection (POD) low probability of false alarm (POF)’ and ‘high POD high POF’ phenomena exist in statistical and dynamic models, respectively. On average, less than 20% drought onsets can be detected in statistical models while less than 40% in dynamic models, with more than 40% false alarms appearing in statistical models and more than 75% in dynamic models. The hybrid model can slightly balance them, resulting in a POD of 20% and a POF of 50%. In spite of the low predictability, some stations with high equitable threat score (ETS) can be used in early drought warning under certain requirement. These conclusions may help improving drought prediction at a local or national scale.