Target characteristics and correlation detection probability calculation method in photoelectric detection screen testing system

To solve the unstable problem of target tracking detection system, this paper proposes an improved mean-shift algorithm for object tracking, establishes object tracking processing model;provides the processing algorithm of object tracking. According to the principal of object tracking, papersets up sky background brightness calculation model in photoelectric tracking optical detection area and detection capability calculation model of space object, analyzes the effect of background illumination on the signal to noise ratio(SNR) of photoelectric tracking system, gives the change curve of detection capability based on the exposure time of CCD camera, SNR threshold of photoelectric detection system and dark current of photoelectric detection system. Through calculation and test , paperprovidesthe comparison results of the improved mean-shift and traditional mean-shift, verifies the correctness of the proposed algorithm and calculation models for photoelectric detection capability in target tracking detection system, the results show the improved mean-shiftobject tracking algorithm and detection capability calculation model are correct.

This paper investigates the detection performance of an improved energy detector for a secondary user with spatially correlated multiple antennas. In an improved energy detector, an arbitrary positive power operation p replaces the squaring operation in a conventional energy detector, and the optimum value of p that gives the best detection performance may be different from 2. Firstly, for a given value of p, we derive closed-form expressions for the probability of detection and the probability of false alarm when antennas at the secondary user are exponentially correlated. We then find the optimum value of p for two different detection criteria-maximizing the probability of detection for a target probability of false alarm, and minimizing the probability of false alarm for a target probability of detection. We show that the optimum p is strongly dependent on system parameters like number of antennas, antenna correlation coefficient among multiple antennas, and average received signal-to-noise ratio (SNR). From results, we infer that, in low SNR regime, the effect of antenna correlation is less pronounced on the optimum p. Finally, we find the optimum values of p and threshold jointly that minimize the total error rate.

It is still a big challenge to calculate the probability of defect detection for inspecting pits on austenitic stainless steel welding using traditional POD models. Because we determine the decision threshold, the POD curve has a lot of changes as the decision threshold changes, there is no clear reason to insist which threshold is reasonable. This study proposes a new probability of detection (POD) model to quantitatively assess the detection capability of eddy current probes for inspecting pits on austenitic stainless steel welding. The experimental results show that the proposed model is more reasonable than traditional ones. The novel POD model was employed to analyze three eddy current probes, uniform, TR, plus-point probes on austenitic stainless steel welding. The results reveal that the uniform eddy current probe has the best detection capability for inspecting pits on among the three probes.

In Dual Sequence Frequency Hopping (DSFH) communication mode, aiming at improving the detection performance to weak signal under low signal-to-noise ratio (SNR) conditions, stochastic resonance (SR) detection method is proposed. First, the α-stable distribution is used as the impulsive noise model and the influence of α value on the properties of α-stable noise is analyzed. Second, the transmitting and receiving signal model of DSFH communication system is introduced. The SR method is used to detect DSFH signal. In order to analyze the output signal, the fractional Fokker–Planck equation (FFPE) is established, and a new simplified solution method based on sampling decision time is proposed to solve the time-varying fractional differential equation. Base on the theoretical solution of FFPE, a binary hypothesis test statistic is constructed to quantify the signal detection probability and false alarm probability, and the detection performance is analyzed. Finally, simulation experiments verify the theoretical conclusions. The minimum effective SNR for SR detection is obtained, and it is about −20 dB, which provides a theoretical basis for the application of SR in the DSFH communication system.

Measurement of speech intelligibility of cochlear implant (CI) recipients is typically carried out with a speech-innoise test procedure. Metrics which predict speech intelligibility can pre-screen new sound processing strategies prior to comprehensive testing with human subjects.The Output Signal to Noise Ratio (OSNR) metric calculates the Signal to Noise Ratio (SNR) which is present at the CI sound processor output. Watkins et al. (2018) found OSNR was an accurate predictor of speech intelligibility that could predict intelligibility in scenarios where other predictors could not.The current study investigated the effect of the sound processor automatic gain control (AGC) on OSNR and a simplified metric, Separate gain SNR (SSNR), which calculated the SNR at the CI output, assuming no interaction between the signal and noise in the sound processor. Prediction accuracy of OSNR was compared to that of Input SNR and SSNR.It was found that AGC-induced distortion and SNR degradation in speech gaps worsened OSNR. For scenarios with significant non-linear, time-varying processing, OSNR was the most accurate prediction metric. SSNR was found to be an inaccurate predictor.

This study proposed a method to evaluate proper experimental conditions of eddy current tests under a low signal-to-noise ratio. This method was applied to quantify the detection reliability of the eddy current test to detect corrosion pits on the surface of austenitic stainless-steel weld cladding from the perspectives of probability of detection and probability of false alarm. The fluctuations of flaw signal and noise were described using truncated bivariate normal distributions of a two-dimensional vector composed of the real and imaginary components of a signal. The probabilities of detection and false alarms were formalized as cumulative distributions of the estimated distributions of the maximum flaw signal and the noise exceeding a decision threshold. The receiver operating characteristic curves were created by coupling the probabilities of detection and false alarms at various thresholds. The area under the curve was used to quantify the detection reliability, simultaneously considering the probabilities of detection and false alarms. The results obtained in this study support the applicability of this method in quantitatively evaluating the detection reliability of a non-destructive inspection under a low signal-to-noise ratio.

This paper presents a novel analytical derivation of the false alarm probability (FAP) and detection probability (DP) for non-line-of-sight (NLOS) detection based on LTE signals. Position estimation in indoor or urban area with cellular wireless communication systems is getting more and more important. However, especially in these environments NLOS propagation causes positioning errors in the order of hundreds of meters. Thus, detection and mitigation of NLOS signals is a critical task. In this paper, we derive the FAP and DP on power-scaled detectors based on LTE multipath signals. The derivation considers frequency-selective fading channels taking sub-sample multipath interference, channel dynamics and initial frequency offsets into account. The results show a simple way to calculate the DP versus FAP. Simulation results verify the analytical probabilities for the WINNER II C2 channel model. Additionally, the simulations show the improved FAP of an antenna array at the receiver.

Research on detection performance improvement of polarization GM-APD LiDAR with adaptive adjustment of aperture diameter and spatial correlation method

In this paper, we optimize probability of detection and probability of false alarm in cognitive radio network to minimize probability of error of a particular SU in a centralized cognitive radio network using Genetic algorithm (GA). Our objective is to minimize probability of error and find out optimum values of probability of occupancy detection or probability of detection and probability of false alarm. We use Genetic Algorithm to solve this optimization problem. The result is compared with Differential Evolution algorithm and it is evident from the comparison that DE finds better solution and takes much lesser number of evaluations to find optimum solution.

In this study, we propose a simple spectrum sensing method based on exploiting the properties of a group‐delay phaser. Following the theory that an additive white noise should have a flat spectrum over the band of interest, which is not the case for most data‐modulated signals, the spectrum shape of input waveforms has been the test variable. The latter enables a clear distinguishing method between a noise background and a communication signal of a transmission body operating over the desired band. The phaser scatters the frequency components of received signals in time space, allowing a time‐domain inspection of the corresponding spectral response. The accurate closed‐form analytical expression for the probability of detection in an additive white Gaussian noise (AWGN) channel has been derived, in addition to the false alarm probability. The probability of detection has been studied versus signal‐to‐noise ratio (SNR) in AWGN and multipath channels. As well, the receiver operating characteristic (ROC) curves have been plotted for different values of SNR. A good performance has been achieved by our scheme, which has recorded a detection probability of 0.86 for a false alarm probability of 0.1, and further shown a kind of robustness against noise uncertainties. Experimental works have eventually been conducted, and the empirical results also validate the effectiveness of the elaborated approach. An improvement of around 30% in the probability of detection has been realized over the energy‐based sensing technique, at low measures of false alarm probability.

The paper provides an engineering analysis approach for estimating non-destructive evaluation (NDE) flaw size using smaller number of hit-miss data-points. Probability of detection (POD) analysis of inspection test data provides a flaw size, denoted as a90/95. The flaw size has 90% POD and minimum 95% confidence. In this approach, we do not estimate a90/95 flaw size. Here, goal is to estimate the flaw detectability size that is larger than the unknown a90 for the NDE technique. The approach is devised using a large set of simulated signal response data and estimating population a90/95p using an â versus a POD model. The signal response data is converted to hit-miss data using a decision threshold. A hit-miss or â versus a POD model is assumed here. Then a sampling scheme is set-up for generating limited number of data-points and flaw size estimation equations are established by substituting POD model quantities by their approximate estimates based on demonstrated flaw sizes. The data sampling scheme used here is like a Monte Carlo simulation allowing evaluation of engineering estimation approach. The distribution of multiple engineering flaw size estimates provides a confidence on the engineering estimate being larger than a90/95p used in the simulation. NDE practitioner normally uses some rule of thumb to estimate flaw size based on limited flaw detection data. A typical rule of thumb may be to multiply the smallest detected flaw size by a factor such as two. The rule of thumb is subjective. Compared to the rule of thumb, paper provides more complex equations and demonstrates these equations on simulated randomized data. NDE flaw detection size is used in fracture mechanics analysis of aerospace structures. Fracture mechanics uses flaw growth analysis using NDE flaw size to determine service life of the part. The part design is optimized for performance, weight, service life and cost. In order to tolerate larger NDE flaw size, parts are designed to be stronger and heavier to reduce stress. Therefore, a90/95 flaw size is desirable. But many times, POD demonstration, requiring a large number of real known size flaws, is cost prohibitive. Thus, if we can estimate risk or confidence associated with the engineering estimate of flaw detectability size, program managers may accept the engineering approach. Here, we need to assess confidence that engineering estimate of flaw size is greater than or equal to estimated a90/95p of the simulated data. Also, we need to assess smallest possible value of the flaw size estimate and compare it with the estimated a90/95p. Lastly, the probability of false positive (POF) also needs to be assessed. A technique is considered reliable, if it provides flaw detectability size with 90/95 POD/confidence and also provides a POF less than or equal to a chosen value i.e. 1%. Linear correlation is used between the signal response data and flaw size. POD software mh1823 uses generalized linear model (GLM) in POD analysis after transforming the flaw size and signal response, if needed, using logarithm. Therefore, this simulation approach is in agreement with the linear signal correlation used in mh1823. The approach is conservative and is designed to provide a larger flaw detectability size compared to POD approach with high confidence and takes into account number of data-points used. Such NDE flaw detectability size estimation approach, although, not as rigorous as POD, can be cost effective if the larger estimated flaw size can be tolerated.

In the photoelectric detection system, the photoelectric detector can convert the optical signal to be measured into a current signal, and the current amplifier transforms the current signal output by the detector into a voltage signal for amplification. In this study, the photo-multiplier tube (PMT) is selected as the photoelectric detector. Compared with other photoelectric detectors, it can obtain higher internal gain, higher sensitivity, and better response performance. The current amplifier is prepared by pre-amplifier and voltage amplifier. In order to capture photoelectric signals well, a large-format scanning system is set up to design each component module, control module, and host computer module of the system. Besides, a machine learning-based algorithm is proposed, namely semi-supervised manifold image recognition algorithm, which is used for identify photoelectric detection images. In the test process, the printed circuit board (PCB) and sapphire material are firstly used as the substrate of the current amplifier, and their influences in the circuit are compared. The peak value of the output noise of each substrate circuit is around 2.8 mV when the input of the current amplifier is short-circuited. Then, the signal gain and signal bandwidth of the photoelectric detection system remain stable when there is no optical signal input. During the process of changing the system signal gain ratio, the noise output of the system is the lowest when the voltage of PMT is 0.50 V and the current amplifier gain is set to 2.2 × 105 V/A. The proposed recognition algorithm can identify different types of targets well. After the image is projected into a two-dimensional space by the algorithm, the distance between classes increases, and the targets in the class promote aggregation, thereby enhancing the identify-ability between samples.

In this article, kernelized-likelihood ratio tests (LRTs) for binary phase-shift keying (BPSK) signal detection based on the polynomial kernel function are proposed. Specifically, we kernelize the conventional LRT of BPSK signal detection using the so-called kernel trick, such that the inner product of the conventional LRT is replaced with proper polynomial kernel functions allowing for richer feature space to be deployed in the detection. We also derive computationally efficient recursive implementation structures for the proposed methods, resulting overall in six new detectors. With respect to the noise variance uncertainty (NVU), the proposed detectors can be divided into two general classes, namely i) constant false alarm rate (CFAR) and ii) semi-CFAR (S-CFAR) methods. To facilitate efficient operation under NVU, we also propose a new threshold-setting strategy to adjust the level of the proposed S-CFAR detectors. Additionally, we address the well-known energy detector (ED) under NVU and devise a new fixed-level ED formulation while also obtaining closed-form expressions for its false alarm and detection probabilities. Our extensive simulation results show that the proposed S-CFAR detectors outperform the state-of-the-art BPSK signal detectors with 2.4 dB signal-to-noise ratio (SNR) gain under practical worst-case NVU assumptions, while the performance gain is approximately 5.7 dB without NVU. In the case of the proposed CFAR detectors, the corresponding improvement in detection performance is approximately 1.8 dB.

There has been an increasing investigation in distributed radar sensor network (RSN) and MIMO-RSN, including waveform design, antenna strategy and spacetime coding, etc. However, distributed-RSN and MIMO-RSN are more like two parallel paths other than one research field in current literature. In this paper, we address the comparison between distributed-RSN and MIMO-RSN for target detection. We first establish the wireless statistic channel model for them. Unlike the conventional models that assume the received signals are static plus a Gaussian random for simplicity and clarity, we believe it's more genuine to model the received signals as random variables due to the uncertainty of channels. Choosing selection combination (SC) as a combining method of distributed-RSN, we get the receiver operation characteristic (ROC) evaluation of these two types through simulations. A comparative analysis of the advantages and disadvantages of them shows that target detection performance of distributed-RSN is better than that of MIMO-RSN when signal to noise ratio (SNR) is small and the probability of false alarm (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fa</inf> ) is lower. As both SNR and P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fa</inf> increase, the probability of target detection (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> ) of MIMO-RSN exceeds that of distributed-RSN.

This paper presents a novel differential energy detection scheme for multi-carrier systems, which can form fast and reliable decision of spectrum availability even in very low signal-to-noise ratio (SNR) environment. For example, the proposed scheme can reach 90% in probability of detection (PD) and 10% in probability of false alarm (PFA) for the SNRs as low as -21 dB, while the observation length is equivalent to 2 multi-carrier symbol duration. The underlying initiative of the proposed scheme is applying order statistics on the clustered differential energy-spectral-density (ESD) in order to exploit the channel frequency diversity inherent in high data-rate communications. Specifically, to enjoy a good frequency diversity, the clustering operation is utilized to group uncorrelated subcarriers, while, the differential operation applied onto each cluster can effectively remove the noise floor and consequently overcome the impact of noise uncertainty while exploiting the frequency diversity. More importantly, the proposed scheme is designed to allow robustness in terms of both, time and frequency offsets. In order to analytically evaluate the proposed scheme, PFA and PD for Rayleigh fading channel are derived. The closed-form expressions show a clear relationship between the sensing performance and the cluster size, which is an indicator of the diversity gain. Moreover, we are able to observe up to 10 dB gain in the performance compared to the state-of-the-art spectrum sensing schemes.