Required Detection Probability Research Articles

Designing a Surveillance Sensor Network with Information Clearinghouse for Advanced Air Mobility

To ensure safe, secure, and efficient advanced air mobility (AAM) operations, an AAM surveillance network is needed to detect and track AAM traffic. Additionally, a cloud-based surveillance data collection, monitoring, and distribution center is needed, where AAM operators and service suppliers, law enforcement agencies, correctional facilities, and municipalities can subscribe to receiving relevant AAM traffic data to plan and monitor AAM operations. In this work, we developed an optimization model to design a surveillance sensor network for AAM that minimizes the total sensor cost while providing full coverage in the desired region of operation, considering terrain types of that region, terrain-based sensor detection probabilities, and meeting the minimum detection probability requirement. Moreover, we present a framework for the low altitude surveillance information clearinghouse (LASIC), connected to the optimized AAM surveillance network for receiving live surveillance feed. Additionally, we conducted a cost–benefit analysis of the AAM surveillance network and LASIC to justify an investment in it. We examine six potential types of AAM sensors and homogeneous and heterogeneous network types. Our analysis reveals the sensor types that are the most profitable options for detecting cooperative and non-cooperative aircraft. According to the findings, heterogeneous networks are more cost-effective than homogeneous sensor networks. Based on the sensitivity analysis, changes in parameters such as subscription fees, the number of subscribers, sensor detection probabilities, and the minimum required detection probability significantly impact the surveillance network design and cost–benefit analysis.

СТАТИСТИЧЕСКОЕ ОБОСНОВАНИЕ ДОПУСТИМОГО СНИЖЕНИЯ ЧУВСТВИТЕЛЬНОСТИ МАГНИТОМЕТРА ПРИ СОХРАНЕНИИ ТРЕБУЕМОЙ ВЕРОЯТНОСТИ ОБНАРУЖЕНИЯ ПОДВОДНОГО ОБЪЕКТА

The search for underwater potentially dangerous objects using towed magnetometric systems during environmental surveys and rescue operations does not lose its relevance. The existing practice of increasing their effectiveness by increasing the sensitivity of the magnetic field sensor (up to 0.01–0.0001 nT) entailed an increase in weight and size characteristics and cost. At the same time, the stochastic nature of the search process was not taken into account, leading to an indefinite signal-to-noise ratio associated with the random position of an underwater object in the search band, which led to its unacceptable omission. Therefore, the modern search for potentially dangerous underwater objects required their guaranteed detection with an increased probability. The developed methodology for using data from statistical analysis of the amplitude characteristics of the magnetic field induction of an underwater object (signal) and a towing vehicle (interference) makes it possible to determine the width of the guaranteed detection band. The values of the ratios of the average values of the amplitudes of their magnetometric signals, corresponding to the probabilistic characteristics of detection, depending on the values of the magnetic moments in the search band, are determined. Boundary conditions for zones of guaranteed detection of an underwater object with an increased probability Pc = 0.8–1.0 are established in accordance with the level of "signal-to-interference" ratios. Taking into account the stochastic nature of the process of searching for an underwater object, a substantiation of the permissible sensitivity of the magnetometer was carried out, which leads to a decrease in its weight, size and cost characteristics without reducing the required detection probability.

Neural Network Based AMP Method for Multi-User Detection in Massive Machine-Type Communication

In massive machine-type communications (mMTC) scenarios, grant-free non-orthogonal multiple access becomes crucial due to the small transmission latency, limited signaling overhead and the ability to support massive connectivity. In a multi-user detection (MUD) problem, the base station (BS) is unaware of the active users and needs to detect active devices. With sporadic devices transmitting signals at any moment, the MUD problem can be formulated as a multiple measurement vector (MMV) sparse recovery problem. Through the Khatri–Rao product, we prove that the MMV problem is transformed into a single measurement vector (SMV) problem. Based on the basis pursuit de-noising approximate message passing (BPDN-AMP) algorithm, a novel learning AMP network (LAMPnet) algorithm is proposed, which is designed to reduce the false alarm probability when the required detection probability is high. Simulation results show that when the required detection probablity is high, the AMP algorithm based on LAMPnet noticeably outperforms the traditional algorithms with acceptable computational complexity.

Deterministic deployment based on information coverage in wireless sensor networks

ABSTRACTIn this study, a deterministic deployment problem in wireless sensor networks is examined. On the basis of information coverage, we study equilateral triangle and square deployment strategies, and we provide the maximum distance between sensors in order to reach the required detection probability for any point in the monitoring field. First, we provide a model of the signal attenuation. On the basis of the detected signal from the K sensors, the best linear and unbiased estimation is used to estimate the signal parameter with the corresponding error. For the equilateral triangle deployment, the maximum distance between sensors is computed and provided when the received signal data from two or three sensors is used. Similarly, we have computed and supplied the maximum distance between sensors in the square deployment. Simulations are performed to show the relationship between the number of sensors and the detection probability. The simulation results show that it is not a good choice to improve the detection probability with a larger number of sensors.Copyright © 2012 John Wiley & Sons, Ltd.

Coherent follow-up of Continuous Gravitational-Wave candidates: minimal required observation time

We derive two different methods to compute the minimal required integration time of a fully coherent follow-up of candidates produced in wide parameter space semi-coherent searches, such as global correlation StackSlide searches using Einstein@Home. We numerically compare these methods in terms of integration duration and computing cost. In a Monte Carlo study we confirm that we can achieve the required detection probability.

Coalition Formation Games for Collaborative Spectrum Sensing

Collaborative spectrum sensing (CSS) between secondary users (SUs) in cognitive networks exhibits an inherent tradeoff between minimizing the probability of missing the detection of the primary user (PU) and maintaining a reasonable false alarm probability (e.g., for maintaining good spectrum utilization). In this paper, we study the impact of this tradeoff on the network structure and the cooperative incentives of the SUs that seek to cooperate to improve their detection performance. We model the CSS problem as a nontransferable coalitional game, and we propose distributed algorithms for coalition formation (CF). First, we construct a distributed CF algorithm that allows the SUs to self-organize into disjoint coalitions while accounting for the CSS tradeoff. Then, the CF algorithm is complemented with a coalitional voting game to enable distributed CF with detection probability (CF-PD) guarantees when required by the PU. The CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e., coalitions that achieve the target detection probability with minimal costs. For both algorithms, we study and prove various properties pertaining to network structure, adaptation to mobility, and stability. Simulation results show that CF reduces the average probability of miss per SU up to 88.45%, relative to the noncooperative case, while maintaining a desired false alarm. For CF-PD, the results show that up to 87.25% of the SUs achieve the required detection probability through MWCs.