Probability Of Missed Detection Research Articles

Missed detection probability evaluation for the RAIM based on worst-case fault magnitude searching

Missed detection probability is a critical metric for the integrity performance of receiver autonomous integrity monitoring (RAIM) in the presence of faults. The traditional missed detection probability evaluation method for RAIM is limited by impractical time consumption because of the absence of accurate searching interval for the magnitude of a worst-case fault. To address this issue, the searching interval for the magnitude of a worst-case fault is constructed by the combination of minimum detectable magnitude and minimum hazardous magnitude, and the searching interval adjustment is designed to avoid the absence of worst-case fault magnitude so that the maximum missed detection probability can be accurately evaluated. The simulation result indicates that the proposed method can achieve higher accuracy for the worst-case fault magnitude searching. Furthermore, the accuracy of worldwide evaluated missed detection rate can achieve an improvement of 57·66% at most by the proposed method for the different classical RAIM algorithms.

Efficient spectrum allocation by heterogeneous automated frequency coordination network within 6 GHz band

This work proposes a heterogeneous automated frequency coordination network (HAFCN) to enhance reliability and enable dynamic spectrum allocation for unlicensed Wi-Fi devices (UWDs) within the 6 GHz band. This process relies heavily on spectrum sensing within the HAFCN. However, the widespread implementation of spectrum sensing by multiple UWDs in an automated frequency coordination (AFC) network using conventional fusion schemes poses computational challenges at the AFC provider. In response to this challenge, we present a selective soft-information (SSI) fusion scheme for the proposed HAFCN. First, we present generic mathematical expressions of false-alarm and missed detection probabilities for the HAFCN using an SSI fusion scheme. Second, a generalized AFC SSI fusion problem (GASFP) is formulated to minimize the system’s total error probability. Further, to mitigate the AFC provider’s overhead in solving the GASFP, this work presents the swift-sensing problem, determining the minimum antennas at UWD required to achieve a desired total error probability. Finally, comparative numerical results demonstrate that the HAFCN with the SSI fusion scheme shows a significant performance improvement over conventional fusion schemes in terms of the total error probability.

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

Sequential Detection under Correlated Observations using Recursive Method

Sequential analysis has been used in many cases when the decision is supposed to be taken quickly such as for signal detection in statistical signal processing, namely sequential detector. For identical error probabilities, a sequential detector needs a smaller average sample number (ASN) than its counterpart of a fixed sample number quadrature detector based on Neyman-Pearson criteria. The optimum sequential detector was derived based on the assumption that the observations are uncorrelated (independent). However, in realistic scenario, such as in radar, the assumption is commonly violated. Using a sequential detector under correlated observations is sub-optimal and it poses a problem. It demands a high computational complexity, since it needs to recalculate the inverse and the determinant of the signal covariance matrix for each new sample taken. This paper presents a technique for reducing the computational complexity, which involves using recursive matrix inverse to subsequently calculate conditional probability density functions (pdf). This eliminates the need to recalculate the inverse and determinant, leading to a more reasonable solution in real-world scenario. We evaluate the performance of the proposed (recursive) sequential detector by using Monte-Carlo simulations and we use the conventional and non-recursive sequential detectors for comparisons. The results show that the recursive sequential detector has equal probabilities of false alarm and miss-detection with the conventional sequential detector and performs better than the non-recursive sequential detector. In terms of ASN, it maintains comparable results to the two conventional detectors. The recursive approach has reduced the computational complexity for matrix multiplication to from and it also has rendered the calculation of matrix determinant to be unnecessary. Therefore, by having better probabilities of error and reduced computational complexities under correlated observations, the proposed recursive sequential detector may become a viable alternative to obtain a more agile detection system as required in future applications, such as in radar and cognitive radio.

Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things.

The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds.

A multi-user cognitive radio full-duplex channel under imperfect spectrum sensing: Achievable rates and energy efficiency

In this work, the achievable rates and energy efficiency of a multi-user cognitive radio (CR) uplink channel with full-duplex (FD) are studied. For this multi-user CR channel, secondary users can work in FD mode to simultaneously transmit and listen to the channel. We consider a realistic scenario in which both FD operation and spectrum sensing are imperfect. At first, we examine the sensing performance and the rate region through the evaluation of the probability of miss-detection and the probability of false alarm when the primary user (PU) activity is assumed to be non-time-slotted. Due to an imperfect sensing operation, the cognitive radio channel is modeled as a non-Gaussian channel impacted by a Gaussian-mixture (GM) noise plus PU interference. Under this practical assumption, it is not possible to calculate the achievable rates and the rate region directly. Instead, we derive simple approximations of these metrics which can be easily calculated with arbitrarily small errors. In the second part of the paper, we further evaluate the derivatives of these achievable rates at low-power regimes. This calculation helps us evaluate the energy efficiency and obtain the minimum energy per bit Eb/N0min when Gaussian and discrete input signals are used.

APM-YOLOv7 for Small-Target Water-Floating Garbage Detection Based on Multi-Scale Feature Adaptive Weighted Fusion.

As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.

Spectrum Sensing Based on Sticky Hidden Markov Model With Beta Prior of Channel State Switches

Spectrum sensing is a key functionality in cognitive radio to avoid unacceptable interference to primary users. When channel traffic becomes heavy, channel state switches within a sensing duration with a high probability, and conventional spectrum sensing methods suffer from a performance floor in miss-detection probability. In this work, after formulating a binary hypothesis problem, we develop a sticky hidden Markov model with a Beta distribution prior (SHMM-Beta) algorithm to categorize the received samples into two clusters corresponding to channel states ON and OFF. Then, we design an SHMM-Beta based detector for spectrum sensing whose test statistic is the ratio of the posterior probabilities of the hidden state variable at the last time instant of the sensing duration. Simulation results show that the proposed SHMM-Beta based detector does not exhibit performance floor in miss-detection probability, and significantly outperforms the conventional detectors specifically designed to handle the switches of channel states.

FBMC-Based Random Access Signal Design and Detection for LEO Base Stations

The integration of non-terrestrial networks into the 5G ecosystem is mainly driven by the possibility of provisioning service in remote areas. In this context, the advent of flying base stations at the low Earth orbit (LEO) will enable anywhere and anytime connectivity. To materialize this vision, it is of utmost importance to improve radio protocols with the aim of allowing direct satellite access. Bearing this aspect in mind, we present a new random access signal, which is based on the filter bank multicarrier (FBMC) waveform, and a computationally efficient detection scheme. The proposed solution outperforms the standardized access scheme based on single-carrier frequency division multiplexing (SC-FDM), by reducing out-of-band (OOB) emissions and reducing the missed detection probability in presence of very high carrier frequency offset (CFO), which is inherent to LEO satellite systems. The improvement is related to the fine frequency resolution of the detector and the use of pulse shaping techniques. Interestingly, the FBMC-based random access signal achieves a high level of commonality with 5G new radio, as the preamble generation method and the time-frequency allocation pattern can be kept unchanged. Concerning the practical implementation aspects, the complexity of the detector is similar in both SC-FDM and FBMC.

A Multi-Beam Seafloor Constant False Alarm Detection Method Based on Weighted Element Averaging

Due to the influence of environmental noise, sidelobe data, and tunnel emission under the background of multi-background underwater surveying and mapping, it is challenging to detect seafloor terrain in the background noise. Constant false alarm detection of seafloor terrain under the condition of constant false alarm probability has been an important research field. The constant false alarm detection can eliminate noise interference in a water body in the seabed topography mapping process and provide clear and accurate seabed topography information. Therefore, it is a challenging task to increase the detection probability, reduce the missing probability, and increase the detection speed in constant false alarm detection methods. Aiming at the shortcomings of the existing algorithms, this paper proposes an efficient weighted cell averaged constant false alarm detection method (WCA-CFAR). First, the cross-window reference unit sampling method is used to improve the detection speed and accurately sample the background noise unit. Then, the reference unit weighted average constant false alarm detection method is employed to calculate the detection threshold to achieve the purpose of target detection. The proposed method is verified by the simulation data detection test and a test on the actual lake test data. The test results show that the proposed method can effectively reduce the missing detection probability and improve the detection probability.

Reliable Uplink Synchronization Maintenance for Satellite-Ground Integrated Vehicular Networks: A High-Order Statistics-Based Timing Advance Update Approach

Satellite-ground integrated vehicular network can provision ubiquitous and unlimited network connectivity for massive vehicles, and is expected to play a vital role in 6G-supported intelligent transportation systems (ITS). However, due to its high-dynamic channel environments and limited satellite payload, the uplink synchronization has become a major bottleneck to restrict vehicular communication performance. Focusing on maintaining reliable uplink synchronization, we propose an efficient timing advance (TA) update approach in this paper. Specifically, an enhanced preamble format is first presented based on the periodical pairing sounding reference signals (SRSs), which enables the satellite to continuously track uplink timing variation with a low signaling overhead. By taking full advantage of all the fourth-order autocorrelation produces from the received preamble, we further design a novel timing metric consisted of the correlation and differential normalization functions, which is capable of having a considerably increased correlation length and shaper mainlobe, as compared to the existing ones. Through theoretical performance analysis, it is indicated that the proposed approach not only notably promotes class distance between the correct and wrong timing indexes, but also can achieve the immunity to multi-path effect and large carrier frequency offset (CFO), while having a reduced computational complexity. Simulation results in a typical low-earth-orbit (LEO) scenario reveal the superiority of our approach in terms of the false alarm probability, the missed detection probability, as well as the timing mean square error.

A Comprehensive Analysis of Energy Efficiency Using Cooperative Spectrum Sensing Network

The proposed cooperative spectrum sensing network presented in this paper has potential properties that could improve the detection probability. The proposed network consists of multiple cognitive radio (CR) nodes. Each CR node is equipped with several antennas, and selection combining is used to identify the highest value of sensing data associated with each antenna. Further, this sensing data is passed to fusion center and primary user activity is identified at the fusion centre using the fusion rules. This study mainly focuses on identifying unknown signals in a Rayleigh fading environment using an improved energy detector. Initially, the novel analytical closed-form of expressions for false alarm and missing detection probabilities was provided under Rayleigh fading with multiple antennas at each CR. Further, the performance of the total error rate is studied under single and multiple antenna scenarios, and a quantitative evaluation is offered to determine average throughput and energy efficiency performance. Later, to simulate the performance with a solid foundation of mathematical analysis, a simulation test-bed is created in MATLAB. Finally, the performance is improved with the proposed scheme compared to conventional scheme.

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)- based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability

Anomaly Detection of Wireless Relays Based on Markov Models Through the Wald-Wolfowitz Runs Test

This letter presents a new anomaly detection method adopting the Wald–Wolfowitz runs test for abnormal wireless relays with intermittent packet loss modeled as Markov chains. The proposed method identifies anomalies by leveraging the characteristics of runs in time series of packet loss behaviors without prior knowledge. An asymptotic analysis of the detection performance shows that the false alarm probability rapidly converges to a sufficiently small value, and the missed detection probability decays to zero exponentially. According to theoretical analysis, this method can discern arbitrary Markov-based intermittently abnormal relays from normal ones compared to the general approach based on the overall proportion of dropped packets. Numerical results verify the analysis and corroborate its superiority of wide detection range and short detection time.

Sightless but Not Blind: A Non-Ideal Spectrum Sensing Algorithm Countering Intelligent Jamming for Wireless Communication

Aiming at the existing intelligent anti-jamming communication methods that fail to consider the problem that sensing is inaccurate, this paper puts forward an intelligent anti-jamming method for wireless communication under non-ideal spectrum sensing (NISS). Under the malicious jamming environment, the wireless communication system uses Q-learning (QL) to learn the change law of jamming, and considers the false alarm and missed detection probability of jamming sensing, and selects the channel with long-term optimal reporting in each time slot for communication. The simulation results show that under linear sweep jamming and intelligent blocking jamming, the proposed algorithm converges faster than QL with the same decision accuracy. Compared with wide-band spectrum sensing (WBSS), an algorithm which failed to consider non-ideal spectrum sensing, the decision accuracy of the proposed algorithm is higher with the same convergence rate.

Application of Sports Clustering Deconstruction Based on Neural Network

Sports cluster analysis can mainly provide more teaching ideas for physical education. Teachers can make scientific and reasonable arrangements for the teaching plan according to the analyzed data results, so as to achieve better teaching purposes. However, due to various factors such as exercise time and course time conflict, this method cannot be widely used. The neural network had a good memory function, and it can be used to integrate physical education resources in sports. Then, a knowledge framework was formed by simulating a large number of human brain neuron structures, which can solve the problems existing in the motion cluster analysis to a certain extent. In this paper, the sports based on neural network is used to improve the problems existing in the practical teaching application of sports clustering analysis. A teaching system model based on motion recognition was established to improve motion cluster analysis and promote the implementation of data-based education. According to the experimental data obtained from the experimental test, people can know that the detection rate of the sports cluster analysis model is about 89.5%, and the average missed detection probability is about 5.5%.

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

A New Abnormal Node Detection Method With Dynamic Hoeffding Test

In this letter, we propose a new method based on the dynamic Hoeffding test that can distinguish the abnormal relay nodes from those with normal packet loss in wireless communications. An asymptotic analysis of the detection performance on the Hoeffding test is conducted to determine the new dynamic detection threshold for decreasing the total probability of errors. With the theoretical guarantees, both false alarm and missed detection probabilities of the proposed scheme converge <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exponentially</i> as the number of detection periods increases. Simulation results validate our analysis and demonstrate the superior detection performance of the proposed method.

Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation.

Directivity in RF Sensor Networks for Widespread Spectrum Monitoring

Widespread RF spectrum monitoring could enable data-driven modeling of spectrum usage, enhance spectral utilization, and help automate policy enforcement. Previous works in wireless sensor networks offer design insights for RF sensors, but they assume emitters that radiate omnidirectionally. This paper develops a new framework for directional sensors and emitters, which are increasingly common with the growth of millimeter wave technologies. We focus on two-dimensional random sensor deployments modeled as Poisson point processes. Specifically, we determine the probability that a sensor network detects a single emitter for a channel model including path loss, fading, and the directivity of emitters and sensors with random orientations and locations. Our results suggest that with a path loss exponent of 4, quartering the emitter half-power beamwidth doubles the required average sensor density. We also conclude that omnidirectional sensors optimize detection probability. For multiple emitters, we develop a lower bound on the probability of multi-emitter detection and find the average number of undetected emitters. Finally, assuming higher sensor quality results in higher sensor cost, we consider a fixed-budget deployment and observe that decreasing the individual sensor cost by a decade and therefore increasing the quantity of sensors reduces the missed detection probability by about a decade.