A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures

There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a new probability of detection model is proposed for the updating of crack size distribution. Furthermore, the theoretical derivation shows that most existing probability of detection models are special cases of the new probability of detection model. The least square method is adopted for determining the values of parameters in the new POD model. This new model is also compared with other existing probability of detection models. The results indicate that the new probability of detection model can fit the inspection data better. This new probability of detection model is then applied to the analysis of the problem of crack size updating for offshore structures. The Bayesian updating method is used to analyze the effect of probability of detection models on the posterior distribution of a crack size. The results show that different probabilities of detection models generate different posterior distributions of a crack size for offshore structures.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

Estimation of Probability of Damage Detection in Offshore Structural Inspection

To characterize the uncertainty and fuzziness in offshore structural inspection, probability of detection (POD) must be determined. This paper presents the expressions for the POD of four different damage forms mainly existing in offshore structures. The fuzzy-set theory is applied to estimate human errors through the definition of inspection quality. Expressions of inspection quality are achieved. To verify the validity and correctness of the expressions, the data from an offshore platform field inspection of evaluation results of human errors affecting inspection quality are used to estimate the parameters of the POD. The results show that the present models can provide basis for further study of ofTshore structural inspection reliability.

Performance Analysis of Distributed Neyman-Pearson Detection Systems

The performance of a distributed Neyman-Pearson detection system is considered with the decision rules of the sensors given and the decisions from different sensors being mutually independent conditioned on both hypothese. To achieve the better performance at the fusion center for a general detection system of n 〉 3 sensor configuration, the necessary and sufficient conditions are derived by comparing the probability of detec- tion at the fusion center with that of each of the sensors, with the constraint that the probability of false alarm at the fusion center is equal to that of the sensor. The conditions are related with the performances of the sensors and using the results we can predict the performance at the fusion center of a distributed detection system and can choose appropriate sensors to construct efficient distributed detection systems.

Enhancing the Accuracy for Target Detection by Using Fast Orthogonal Search (FOS)

Target detection and bearing estimation are mainly obtained through spectral analysis of received signals. The detection performance of the periodogram and its variants methods is evaluated. The variants methods Performance evaluation through the Receiver Operating Characteristics （ROCs） are presented and compared from the viewpoint of probability of detection （Pd）, probability of false alarm （Pfa） by computer simulation. When the sinusoid frequency does not correspond to one of the spectral bins （mid-bin frequency situation）, the performance of all the mentioned detectors degrades. This research investigates the development of a bearing estimation method using Fast Orthogonal Search （FOS） to enhance spectral estimation which, improves both target detection and bearing estimation in case of low SNR inputs.

DCT domain filtering method for multi-antenna code acquisition

For global navigation satellite system （GNSS） signals in Gaussian and Rayleigh fading channel, a novel signal detection algorithm is proposed. Under the low frequency uncertainty case, after performing discrete cosine transform （DCT） to the outputs of the partial matched filter （PMF） for every antenna, the high order com- ponents in the transforming domain will be filtered, then the equalgain （EG） combination for the inverse discrete cosine transform （IDCT） reconstructed signal would be done subsequently. Thus, due to the different frequency distribution characteristics between the noise and signals, after EG combination, the energy of signals has almost no loss and the noise energy is greatly reduced. The theoretical analysis and simulation results show that the detection algorithm can effectively improve the signal-to-noise ratio of the captured signal and increase the probability of detection under the same false alarm probability. In addition, it should be pointed out that this method can also be applied to Rayleigh fading channels with moving antenna.

认知无线电中基于加权硬判决的协作频谱感知算法(英文)

Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions. If the energy value falls into the corresponding region,it will be judged as "1",no information or "0". When the probability of false alarm is constrained to be constant,the objective is to maximize the probability of detection. The optimization problem is simplified by separating the weight of the middle region into several intervals. Simulation results show that the sensing performance of the proposed scheme is much better than that of the traditional one bit hard combination scheme and almost the same as that of the equal gain combination(EGC) scheme. Moreover,compared with the traditional one bit hard combination,fewer average sensing bits are required to transmit to the data fusion center with the proposed method.

Novel clutter map CFAR algorithm with amplitude limiter

The traditional clutter map constant false alarm rate (CM-CFAR) detector is affected by interference and self-masking[1] which will cause the low probability of detection. To solve these problems, a novel algorithm named clutter map CFAR with amplitude limiter (ALCM-CFAR) is proposed, in which the amplitude of the input signal is limited by a filter. The simulation results prove the effectiveness of ALCM-CFAR algorithm.

M-ary information processing via an optimal nonlinear detector

This paper presents a novel approach of M-ary baseband pulse amplitude modulated signal processing via a parameter-optimized nonlinear dynamic system. This nonlinear system usually shows the phenomenon of stochastic resonance by adding noise. To thoroughly discuss the signal processing performance of the nonlinear system, we tune the system parameters to obtain a nonlinear detector with optimal performance. For characterizing the output of the nonlinear system, the derivation of the probability of detection error is given by the system response speed and the probability density function of the nonlinear system output. By varying the noise intensity with fixed system parameters, the phenomenon of stochastic resonance is shown and by tuning the system parameters with fixed noise, the probability of detection error is minimized and the nonlinear system is optimized. The detection performance of the two cases is compared with the theoretical probability of detection error, which is validated by numerical simulation.

Significant Improvement in Rainfall Forecast over Delhi:Annual and Seasonal Verification

Regional Weather Forecasting Centre(RWFC)New Delhi has the responsibility to issue and disseminate rainfall forecast for Delhi.So it is very important to scientifically verify the rainfall forecast issued by RWFC.In this study rainfall forecast verification of Delhi has been carried out annually and season wise for the period 2011 to 2021.Various statistical parameters such as Percentage Correct(PC),Probability of Detection(POD),Missing Ratio(MR),False Alarm Ratio(FAR),Critical Success Index(CSI),True Skill Statistics(TSS)and Heidke Skill Score(HSS)have been calculated for season wise and annually.A forecast is considered to be improved if PC,POD,CSI,TSS and HSS increase and FAR and MR decrease over a period of time.The author can conclude that annual accuracy of forecast has increased significantly over the period of time from 2011 to 2021,as PC,POD,CSI,TSS and HSS increase and FAR and MR decrease over a period of time.Maximum contribution in the improved forecast has observed in transition season(pre-monsoon season followed by post-monsoon,having rainfall activity mainly in association with thunderstorms),when FAR and MR have decreased drastically.

An experimental method to obtain the hard alpha anomaly distribution for titanium alloy aeroengine disks

A probability-based damage tolerance methodology has been proposed to improve the recognition of material anomalies, especially hard alpha(TiN) anomalies for aeroengine rotor disks. A key input to this method is hard alpha anomaly distribution, which reflects the occurrence rate and size of anomalies present in the finished part material of titanium rotors. Since anomalies rarely occur naturally, an experimental method is proposed to obtain the anomaly distribution for titanium alloy aeroengine disks to reflect and equivalently replace the manufacturing development in titanium industry. In general, the anomaly distribution information can be divided into two parts: the Probability of Detection(POD) curve and the detected anomaly distribution, which contains the size and frequency data of the detected anomalies. The distribution can be established based on several appropriate assumptions and derivation steps with different confidence levels of POD curves and detected anomaly distributions. In this case, the distribution can be obtained in a relatively short time as a key input to the probability-based damage tolerance methodology. Then the Probability of Failure(POF) can be calculated, and the value is found to vary with different confidence levels. On this basis, the conservative estimated POF can be obtained in conjunction with confidence levels.