Global Detection Probability Research Articles

Enhancing Distributed Radar Detection Performance With Reliable Communications

Despite their potential to significantly enhance target detection capability, distributed radar networks, with or without a central coordinator, have not been sufficiently examined. In this paper, we propose a generalized likelihood ratio test (GLRT)-based constant false alarm rate (CFAR) detector for distributed detection in a radar network, wherein only neighboring nodes require sharing their GLRT values. We obtain the analytical expression for the local GLRT and employ distributed average consensus-based estimation to reach the global metric in the case of noise-free communications links. The exact analytical expressions for the local probability of false alarm (PFA) and probability of detection (PD) are derived and used to obtain the exact analytical expression for the global PFA as well as an approximate expression for the global PD. In the case of noisy communications links, we demonstrate the effects of quantization and different modulation techniques, such as chirp and phase shift keying, on the global detection probability. Our results show that the proposed detector performs significantly better than the non-CFAR energy detector (ED) and provides performance comparable to that of the non-CFAR matched filter (MF) detector. It is shown that the performance of the chirp and binary phase shift keying (BPSK) modulations is similar but better than that of quadrature phase shift keying (QPSK). Our results also show that the uniform quantization of GLRT values with only 4 bits is sufficient to enable each node to achieve performance similar to the global performance achieved in the case of error-free communications links.

An analysis of 5G-MIMO communication system based SS for centralized cooperative and non-cooperative users

The purpose of 5G-MIMO communication systems is to boost the intensity of the received signal, and facilitate the application of the users’ apparatus. However, in band-grabbing systems such as these, the accessibility of a spectrum is hindered by gridlocks. Also, to be considered is the spectrum’s waveform, which is the type that contends with 5G networks specifically. This undertaking involves the development of a 5G-MIMO communication system based SS, in which the spectrum is enhanced, by way of a spectrum sensing (SS) algorithm. By replicating the energy detection procedure, this recommended SS algorithm engages the cosine law to filter the traffic signal, and subsequently portions it using the Welch algorithm. The Hann algorithm is then utilized, to window the traffic signal, to damp the high power delivered to the MIMO. The key role, of the SS algorithm, is to sense a spectrum for 5G-MIMO communication system, with the capacity to damp the MIMO effect for a variety of waveforms. The SS algorithm expressions are applicable for both non-cooperative and centralized cooperative users. For each expression, an examination was performed for waveforms used by 5G-MIMO communication systems. This included filtered-orthogonal frequency division multiplexing (F-OFDM), universal filtered multi-carrier (UFMC), and filter bank multi-carrier (FBMC) waveforms. The operating parameters, including SNR, the signal span and power, the antenna count, as well as the types of modulation for both non-cooperative and centralized cooperative users, were scrutinized. The simulation results revealed a notable achievement for the parameters lower than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. An evaluation of the recommended system’s parameters discloses its superiority, in comparison to other previously developed systems.

An analysis of low complexity of 5G-MIMO communication system based CR using hybrid filter detection

The exponential growth in wireless communication has resulted in a high demand for novel wireless services within the frequency bands that can deal with the challenge pertaining to the current spectrum shortage. Thus, a 5G-MIMO based CR communication system has been developed to enhance the spectrum by employing a spectrum sensing (SS) algorithm. This recommended SS algorithm, based on the hybrid filter detection (HFD) method, employs the Cosine law for filtering the traffic signal, and then enables segmentation by applying the Welch algorithm. Then, the Hann algorithm is employed to window the traffic signal, and facilitates damping the MIMO impact for different waveforms. This also includes universal filtered multi-carrier (UFMC), filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and Generalized Frequency Division Multiplexing (GFDM) waveforms. Also, evaluation of the operating parameters such as SNR, the antenna count, the signal span, and power as well as computational complexity was done. Based on the simulation results, a notable achievement was seen for the parameters that were below 0 dB of SNR, <0.09 % global system error probability, >96 % global detection probability, <0.06 % global false alarm probability, and possessing simple complexity. The recommended system’s parameters were assessed, which demonstrated its superiority versus other previously developed systems. This algorithm was found to identify all types of 5G signals and not just one type of 5G.

Detection Performance Evaluation for Marine Wireless Sensor Networks

Detection performance evaluation is one of the inevitable problems for marine wireless sensor networks (MWSNs) deployed for target detection. However, it is a very complicated problem since it associates many different aspects, such as emitter power, range, radar cross-section, weather, geography, working mode, and so on. Targeting this problem, this paper incorporates the Poisson point process model into describing the ranges from sensors to targets. The relationship between sensors and a target is built from the perspective of detection probabilities. Then, a new consistent, conservative target detection probability evaluation is derived within a CFAR framework, and the further global detection probability of the whole MWSN on the target is developed. Additionally, the rationality of this modeling approach is demonstrated via simulation results, which is in accord with the actual situation.

Projected network performance for next-generation xenon monitoring systems

Next-generation radioxenon monitoring systems are reaching maturity and are expected to improve certain aspects of performance in verifying the absence of nuclear tests. To predict the improvement in detecting and locating nuclear test releases, thousands of releases all over the globe were simulated and the global detection probability was calculated for the single xenon isotope 133Xe. This was done for the International Monitoring System network of noble gas samplers as it currently exists (25 certified stations), and how it would be for potential future network sizes of 39 and 79 stations. The probability of detection was calculated for releases ranging from 1010 Bq to 1016 Bq of 133Xe using 10 d of atmospheric transport and presented as coverage maps and global integrals for both current and next-generation monitoring systems. Similarly, the number of detecting stations and the number of detecting samples were tabulated to elucidate the possibilities for enhanced location capability. Improvements in global detection coverage are maximized at different release sizes in a way that depends on the station density. For example, for releases of 3 × 1014 Bq and 39 stations, the detection probability would rise from 60% to 70% with next-generation systems, while for releases of 1013 Bq and 79 stations, it would rise from 37% to 52%. Achieving an average of two detecting stations would require a 1015 Bq release for a 39-station network and a 1014 Bq release for a 79-station network. The largest impact of using next-generation systems may be the confidence, detection redundancy, and location capability that arise from obtaining multiple samples associated with a single release event.

Projected network performance for multiple isotopes using next-generation xenon monitoring systems

Since about 2000 (Bowyer et al., 1998), radioxenon monitoring systems have been under development and testing for the verification of the Comprehensive Nuclear Test-Ban Treaty (CTBT). Operation of the systems since then has resulted in development of a next-generation of systems that are nearly ready for operational deployment. By 2010, the need to screen out civilian sources was well known (Auer et al., 2010; Saey, 2009), and isotopic ratio approaches were soon considered (Kalinowski and Pistner, 2006) to identify specific sources. New generation systems are expected to improve the ability to verify the absence of nuclear tests by using isotopic ratios when multiple isotopes are detected. In this work, thousands of releases were simulated to compute the global detection probability of 131mXe, 133mXe, 133Xe, and 135Xe at 39 noble gas systems in the International Monitoring System (IMS) for both current and next-generation systems. Three release scenarios are defined at 1 h, 1 d, and 10 d past a 1 kt TNT equivalent 235U explosion event. Multiple cases using from one part in a million to the complete release of the xenon isotopic activity are evaluated for each scenario.Coverage maps and global integrals comparing current and next-generation monitoring systems are presented showing that next-generation noble gas systems will create measurable improvements in the IMS. The global detection probability for 133Xe is shown to be strong in all scenarios, but only modestly improved by next-generation equipment. However, the detection probability for 131mXe and 133mXe increased to about 50% in different scenarios, providing a second detectable isotope for many events. As anticipated from shorter sampling intervals, the expected number of detecting samples roughly doubled and the expected number of detecting stations rose by approximately 50% for all release scenarios. Thus, it might be anticipated that future events would consist of multiple 133Xe detections and one or more second isotope detections. Signals of this nature should increase detection confidence, tighten release location estimates, improve rejection of civilian signals, and lessen the impacts from individual systems being offline for maintenance or repair reasons.

A CR-5G network based on multi-user for various waveforms detection

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters.

Optimization of a GIS sensor layout based on global detection probability distribution evaluation

Gas-insulated switchgear (GIS) is an important power equipment. The implementation of health monitoring is limited by the number of sensors, and the global detection results of the system should be highly credible to ensure the reliability of the power supply system. To solve this problem, this study proposes a sensor layout optimization method based on global detection probability performance evaluation. Starting from the cost function, the GIS discharge detection problem is transformed into a Bayesian risk decision problem, the binary state of ‘with discharge’ and ‘without discharge’ is adopted to simplify the cost function and reduce the computing workload, and the objective function representing the global detection performance of the system is obtained. The solution of layout optimization is realized by the improved genetic algorithm. 3-sensor, 4-sensor and 6-sensor layouts, which are digitally simulated at different detection rates, and then the distribution diagram of the global detection rate is obtained. On this basis, the feasibility and effectiveness of the optimization method are verified through an experiment. The results show that, compared with other sensor layout optimization methods, this optimization method can obtain the correct probability distribution of the detection rate globally and realize the graphical quantization of the detection performance distribution of the system so as to ensure the system performance.

Statistical Analysis of Lifetime in Wireless Cognitive Sensor Network for Multi-Channel Cooperative Spectrum Sensing

This paper deals with the issue of reducing energy consumption and evaluating the network lifetime in a wireless cognitive sensor network (WCSN) composed of randomly-distributed sensors to perform multi-channel cooperative spectrum sensing (MCCSS). More precisely, the paper presents a probabilistic approach for analyzing the WCSN lifetime whereas the detection quality is also considered. To this end, it is assumed that both the number of sensors and primary users in the area follow two-dimensional Poisson distributions, and accordingly, the detection probability and false alarm probability for the sensors are calculated, statistically. Next, the number of required sensors for MCCSS is minimized under the global probability of detection (GDP) and the global probability of false alarm constraints. Then, the tail probability of the lifetime by individual sensors is found based on the average sensors' energy consumption. After that, a tail probability for the network lifetime is calculated through statistical analysis, and it is used to obtain an analytical expression for the complementary cumulative density function (CCDF) of the network lifetime. Using the CCDF, one can find the probability that the network lifetime extends until a given time if the minimum number of sensors is used for satisfying the detection quality and the sensors are distributed randomly. Finally, computer simulations using the Monte-Carlo method are used to verify the achieved results.

Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold, sensing time and user selection

Cooperative spectrum sensing schemes proposed to solve the hidden terminal problem and mitigate multipath fading and shadowing effects, which enhance the sensing performance and throughput in cognitive radio (CR) neworks. However, increasing the number of cooperative SUs leads to more communication overhead, which will increase the energy consumption of the CR network. In this paper, a new scheme is proposed to solve the joint optimization problem of the sensing time, the detection threshold and the selection of the sensing and data transmitting secondary users (SUs) for improvement of the throughput and minimization of the energy consumption of the CR network under the constrains on the global probability of detection and global probability of false alarm. For these purposes, we find the optimal values of detection threshold and sensing time such that the detection constraints are satisfied. The convex optimization methods are used to determine the sensing and data transmitting SUs. The simulation results show that there exists the optimal detection threshold and sensing time for selected sensing and transmitting SUs that can improve the average throughput and minimize the energy consumption of the CR network in comparison to other schemes.

Spectrum Detection Based on SNR in Cognitive Radio

Fading shadowing and hidden terminal problems in cognitive radio systems cannot be avoided. Cooperative spectrum detection is an appropriate way to solve this problem. Because the cognitive node’s SNR has influence in cooperative detection, Smart antenna and power control are combined to reduce interference and error rate to improve the performance of the system, then to combine with twice cooperative spectrum detection. in this way cognitive radio system can improve transmission power, SNR, reducing global missing detection probability, false alarm probability in global. Simulation results show the way is available.

Combined pre-detection and sleeping for energy-efficient spectrum sensing in cognitive radio networks

In this paper, we propose a cooperative spectrum sensing scheme including a combined pre-detection and sleeping policy. In the scheme, the designed pre-detection sub-phase is applied at the beginning of the detection of the presence of a primary user, where all sensing nodes are involved to improve the detection performance. The sleeping policy is applied for each sensing node respectively at the end of the pre-detection sub-phase and the beginning of the transmission of the local detection results, in order to decrease sensing energy consumption. We formulate the problem of minimizing the maximum average energy consumption per sensing node in Rayleigh fading channels, considering the constraints of the required global detection and false alarm probabilities and the tolerable interference caused to the primary user. Numerical results show that the scheme achieves significant energy saving as compared to a scheme that includes only a sleeping policy and does not consider pre-detection.

Cognitive Radio Network With Energy-Harvesting Based on Primary and Secondary User Signals

Cognitive radio (CR) provides a radio-access method for unlicensed/secondary users to share the spectrum of licensed/primary users. To improve the energy efficiency of CR networks, we propose an energy-harvesting-aided spectrum sensing and data transmission scheme in this paper. In the proposed scheme, the individual sensing and the cooperative sensing of multi-users are combined corresponding to the strong and weak signal of primary users. Meanwhile, the transmission signal of local decisions in the cooperative sensing and the strong signal of primary users are harvested as RF energy by secondary users. In order to improve the performance of spectrum sensing and energy harvesting, we formulate a multi-objective optimization problem that jointly maximizes the detection probability of the presence of primary user and minimizes the false alarm probability while limiting the least harvested energy and the interference from secondary users on the primary receiver. The multi-objective optimization problem is solved by transferring it into a single objective problem. The single objective problem on the parameters without certain range is transferred into the problem on the parameters with certain range to obtain the final solution. Numerical results prove the effectiveness of our proposed scheme, that is, the interference from primary users on secondary users can improve the global detection probability and the harvested energy at secondary users.

An Improved Adaptive Genetic Algorithm Based on Multi - user Cooperative Spectrum Sensing

In this paper, the collaborative spectrum sensing technique of multiuser is studied, and the global detection probability is obtained by allocating the weight of each user through the control center under the given false alarm probability. The maximum global detection probability is deduced, and an improved adaptive genetic algorithm is used to solve the optimal combination of weight distribution to reduce the detection time of spectrum sensing. In addition, the non-cooperative and cooperative, optimal linear, genetic algorithm and improved adaptive genetic algorithm are simulated and compared. The results show that the detection reliability of spectrum sensing system can be effectively improved by multi-user cooperative, and the convergence rate of the optimal solution is improved by the improved adaptive genetic algorithm, which reduces the spectrum detection time.

A multistage approach to decision fusion using a distributed network of non-identical nodes

A distributed sensor network deployed to detect a binary event using a hard decision fusion scheme is considered. Earlier works show that the K-out-of-N counting rule is optimum in minimizing the total error rate when the sensor nodes participating in the event detection have identical performance indexes (detection and false-alarm probability pairs). In most scenarios, this optimum rule turns out to be the half-voting or majority rule. However, when the sensor nodes have non-identical performance indexes, the optimum value of K that gives the maximum correct decision probability may be anything from 1 to N and no general expression for it is available. Trying all possibilities before choosing the best value of K is computationally very demanding and the complexity grows rapidly as the number of sensor nodes increases, making it an impractical approach for a large sensor network. We propose a multistage decision fusion scheme for such networks using unanimity fusion rules and show that it gives an improved performance over unanimity, half-voting, majority, and some soft fusion rules and a comparable performance to the best K-out-of-N fusion rule in many cases with considerably reduced computational complexity in determining the global detection and false-alarm probabilities.

Gamma-ray burst radio afterglows from Population III stars: simulation methods and detection prospects with SKA precursors

We investigate the prospects of detecting radio afterglows from long Gamma-Ray Bursts (GRBs) from Population III (Pop III) progenitors using the SKA precursor instruments WMA (Murchison Widefield Array) and ASKAP (Australian SKA Pathfinder). We derive a realistic model of GRB afterglows that encompasses the widest range of plausible physical parameters and observation angles. We define the best case scenario of Pop III GRB energy and redshift distributions. Using probability distribution functions fitted to the observed microphysical parameters of long GRBs, we simulate a large number of Pop III GRB afterglows to find the global probability of detection. We find that ASKAP may be able to detect 35% of Pop III GRB afterglows in the optimistic case, and 27% in the pessimistic case. A negligible number will be detectable by MWA in either case. Detections per image for ASKAP, found by incorporating intrinsic rates with detectable timescales, are as high as $\sim$ 6000 and as low as $\sim$ 11, which shows the optimistic case is unrealistic. We track how the afterglow flux density changes over various time intervals and find that, because of their very slow variability, the cadence for blind searches of these afterglows should be as long as possible. We also find Pop III GRBs at high redshift have radio afterglow lightcurves that are indistinguishable from those of regular long GRBs in the more local universe.

To AND or To OR: On Energy-Efficient Distributed Spectrum Sensing With Combined Censoring and Sleeping

Distributed spectrum sensing improves the detection reliability of a cognitive radio network but generally comes at the price of a large power consumption. Since cognitive radios are generally low-power sensors with limited batteries, a combined censoring and sleeping scheme is considered as an energy-efficient algorithm for distributed spectrum sensing. Each sensor switches off its sensing module with a specific sleeping rate. When the sensor is on, a censoring policy is employed to send the sensing result to the fusion center. The result is only transmitted, if it is deemed to be informative. Hence, the energy consumption of each sensor, including the sensing and transmission energies, is reduced. The underlying sensing parameters are derived by minimizing the maximum average energy consumption per sensor subject to a lower-bound on the global probability of detection and an upper-bound on the global probability of false alarm. We analyze the problem for the OR and the AND rule and provide a performance analysis for a case study based on the IEEE 802.15.4/ZigBee standard. It is shown that the combined censoring and sleeping scheme achieves a significant energy saving compared to the case where no censoring or sleeping is taken into account.

Throughput Improvement in Energy-Efficient Cooperative Spectrum Sensing Based on Sensor Selection

Cooperative spectrum sensing in cognitive sensor networks has been shown to be very effective in improving the sensing performance. Sensors in these networks are assumed to be power re-source constrained. We propose an energy efficient cooperative spectrum sensing, in which all sensors are not participated in spectrum sensing and some nodes send their data to a fusion center only in order to improve the throughput of the network. We determine the nodes which sense the spectrum and the nodes which only contribute in data transmitting. The constraints on our problem are the global probability of detection, global probability of false alarm and the minimum required throughput. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing and data transmitting. Simulation results show that our algorithm leads to less energy consumption and improves the throughput in different conditions.

Energy-Efficient Sensor Selection for Cooperative Spectrum Sensing in the Lack or Partial Information

In this paper, we address an energy efficient sensor selection method in wireless sensor networks for cooperative spectrum sensing when only partial information about the sensors and primary user is available. Since it is very difficult to know signal-to-noise ratio for each node, we break down the node selection process as follows. First, there is not any information about the distance between the nodes and primary user. In this case, the probability density function of the received power from the primary user is obtained. Based on the probability density function of the received power, the average probability of detection is determined. According to the average probability of detection, the minimum number of nodes which should be distributed randomly to satisfy the average global probability of detection, is computed. Second, the location of the sensor nodes is assumed to be known while the distance between each node and primary user is not deterministic. Based on the convex optimization framework, we propose a numerical method to obtain the priority of the sensors for spectrum sensing. The sensing nodes are selected to satisfy the average global probability of detection and minimize the energy consumption. Simulation results show that our theoretical solution is in line with our simulation results.

Globally Optimal Cooperation in Dense Cognitive Radio Networks

In cooperative spectrum sensing, local sensing at different sensing nodes is done either using soft decisions or hard decisions. The hard decision-based sensing has the advantage of using only one bit to report the local decision. In the literature, the hard decisions are combined at the fusion center using AND, OR, or MAJORITY rules. Although the problem of finding the fusion rule was addressed and solved for the soft decisions fusion, it was not solved in the hard-decisions sensing. The problem of calculating the local and global decision thresholds in hard decisions-based cooperative spectrum sensing is known for its mathematical intractability. Hence, previous studies relied on simple suboptimal counting rules for decision fusion in order to avoid the exhaustive numerical search required for obtaining the optimal thresholds. These simple rules are not globally optimal as they do not maximize the overall global detection probability by jointly selecting local and global thresholds. Instead, they try to maximize the detection probability for a specific global threshold. In this paper, a globally optimal decision fusion rule for Primary User signal detection based on the Neyman-Pearson (NP) criterion is derived. The algorithm is based on a novel representation for the global performance metrics in terms of the regularized incomplete beta function. Based on this mathematical representation, it is shown that the globally optimal NP hard decision fusion test can be put in the form of a conventional one dimensional convex optimization problem. A binary search for the global threshold can be applied yielding a complexity of $$\mathcal {O}(\log _{2}(N))$$O(log2(N)), where $$N$$N represents the number of cooperating users. The logarithmic complexity is appreciated because we are concerned with dense networks, and thus $$N$$N is expected to be large. The proposed optimal scheme outperforms conventional counting rules, such as the OR, AND, and MAJORITY rules. It is shown via simulations that, although the optimal rule tends to the simple OR rule when the number of cooperating secondary users is small, it offers significant SNR gain in dense cognitive radio networks with large number of cooperating users.