Neyman-Pearson Criterion Research Articles

Neyman–Pearson Criterion-Based Change Detection Methods for Wavelength–Resolution SAR Image Stacks

This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman–Pearson criterion. The first proposed method uses the data from wavelength–resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods.

Research on fusion detection of centralized mobile target based on seismic wave sensor

Aiming at the limitation of single sensor detection and the deficiency of low information utilization rate in distributed fusion mode, according to the characteristics of strong energy and long propagation distance on free surface of seismic wave and the advantages of centralized fusion detection method, a centralized fusion detection algorithm based on Neyman-Pearson criterion is proposed. The probability formula of single sensor detection and centralized fusion detection is deduced and simulated. Through the experiment of ground maneuvering target, it is proved that the algorithm can effectively improve the detection probability and performance of the target.

Extended GLRT Detection of Moving Targets for Multichannel SAR Based on Generalized Steering Vector.

A generalized likelihood ratio test (GLRT) with the constant false alarm rate (CFAR) property was recently developed for adaptive detection of moving targets in focusing synthetic aperture radar (SAR) images. However, in the multichannel SAR-ground moving-target indication (SAR-GMTI) system, image defocus is inevitable, which will remarkably degrade the performance of the GLRT detector, especially for the lower radar cross-section (RCS) and slower radial velocity moving targets. To address this issue, based on the generalized steering vector (GSV), an extended GLRT detector is proposed and its performance is evaluated by the optimum likelihood ratio test (LRT) in the Neyman-Pearson (NP) criterion. The joint data vector formulated by the current cell and its adjacent cells is used to obtain the GSV, and then the extended GLRT is derived, which coherently integrates signal and accomplishes moving-target detection and parameter estimation. Theoretical analysis and simulated SAR data demonstrate the effectiveness and robustness of the proposed detector in the defocusing SAR images.

Dependent Randomization in Parallel Binary Decision Fusion

We consider a parallel decentralized detection system employing a bank of local detectors (LDs) to access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (“absent” ) or H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (“present” )). The kth LD uses a local decision rule to compress its local observations y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> into a binary local decision u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ; u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> =0 if the kth LD accepts H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> = 1 accepts H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> . The kth LD sends its decision u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the local decisions it receives from n LDs (u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ,...,u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) into a single binary global decision u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0=</sub> 0 for accepting H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> or u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> =1 for accepting H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ). If each LD uses a single deterministic local decision rule (calculating u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> from the local observations y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ) and the DFC uses a single deterministic global decision rule (calculating u <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> from n local decisions), the team receiver operating characteristic (ROC) curve is in general non-concave. The system's performance under a Neyman-Pearson criterion may then be suboptimal in the sense that a mixed strategy may yield a higher probability of detection when the probability of false alarm is constrained not to exceed a certain value, α>0. Specifically, a “dependent randomization” detection scheme can be applied in certain circumstances to improve the system's performance by making the ROC curve concave. This scheme requires a coordinated and synchronized action between the DFC and the LDs. In this study, we specify when dependent randomization is needed, and discuss the proper response of the detection system if synchronization between the LDs and the DFC is temporarily lost.

Stochastic Resonance Effect in Optimal Decision Solution Under Neyman–Pearson Criterion

In this paper, stochastic resonance effect on the optimal detection under Neyman–Pearson (NP) criterion is investigated for a general nonlinear system. To this end, a noise enhanced detection optimization problem for maximizing the probability of detection under a constant constraint on the probability of false-alarm is formulated, where an additive noise is added to the nonlinear system input and the final decision is made based on the system output according to the NP criterion. Firstly, the noise modified NP decision rule is derived. Since one additive noise corresponds to one noise modified NP decision rule, the noise-modified NP decision rule can be viewed as a function of the additive noise. Then, the improvability of the optimal detection performance under NP criterion via the proposed noise-modified decision solution is simply discussed. The optimal additive noise is deduced as a random signal of no more than two constant vectors and the corresponding noise-modified NP decision rule is also determined. Finally, the performance comparisons between the original and the noise-modified optimal NP decision solutions for the sine transform system and the Amplitude limit system are made to illustrate the theoretical results.

Privacy-Utility Tradeoff for Hypothesis Testing Over a Noisy Channel

We study a hypothesis testing problem with a privacy constraint over a noisy channel and derive the performance of optimal tests under the Neyman-Pearson criterion. The fundamental limit of interest is the privacy-utility tradeoff (PUT) between the exponent of the type-II error probability and the leakage of the information source subject to a constant constraint on the type-I error probability. We provide an exact characterization of the asymptotic PUT for any non-vanishing type-I error probability. Our result implies that tolerating a larger type-I error probability cannot improve the PUT. Such a result is known as a strong converse or strong impossibility theorem. To prove the strong converse theorem, we apply the recently proposed technique in (Tyagi and Watanabe, 2020) and further demonstrate its generality. The strong converse theorems for several problems, such as hypothesis testing against independence over a noisy channel (Sreekumar and Gündüz, 2020) and hypothesis testing with communication and privacy constraints (Gilani et al., 2020), are established or recovered as special cases of our result.

Semi-Supervised Anomaly Detection Algorithm Using Probabilistic Labeling (SAD-PL)

To detect abnormal data via semi-supervised learning, unlabeled data are generally assumed to be normal data. This assumption, however, causes inevitable performance degradation when a small fraction of abnormal data is included in the unlabeled dataset. To overcome the degradation and to maintain stable detection performance, we propose a semi-supervised anomaly detection algorithm using probabilistic labeling (SAD-PL) for unlabeled data. The proposed SAD-PL is composed of two steps: (1) estimating local outlier factor (LOF) scores of latent vectors from both labeled and unlabeled data and (2) estimating labeling probability on the unlabeled data by using the prior missing probability of the labeled data via the Neyman-Pearson (NP) criterion. The SAD-PL runs iteratively by using the proposed complementary learning functions until the rate of label changes is lower than the predefined threshold. Experimental results reveal that the SAD-PL shows superior detection probability over the existing algorithms and stable performance regardless of the normal to abnormal data ratio in unlabeled data and the ratio of change variation of unlabeled data statistics to labeled data statistics.

Receiver Design With Reduced DOF in Frequency Domain for Target Detection Under Gaussian Clutter

This paper addresses the problem of target detection against a background of Gaussian clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector and detection performance under the Neyman-Pearson criterion for general frequency snapshot selection with arbitrary DOF. When the clutter statistics are unknown, we use a uniformly random frequency snapshot selection method and show how the DOF employed affects the detection performance. When the clutter return follows a stationary Gaussian distribution with slowly varying power spectral density, the optimal selection is derived. When the clutter is composed of reflected versions of the transmitted waveforms, a greedy-based method for selecting the frequency snapshots is presented. Numerical experiments show that a receiver with reduced DOF can lead to detection performance which is very close to that of the receiver with full DOF.

Detection performance characteristics of the digital undersampling receiver

Multichannel digital receivers based on the signal processing technology involving undersampling are used for the instantaneous wideband analysis of the electronic environment. One of the most common algorithms for measuring input signal’s carrier frequency in such receivers includes unfolding of the signal’s spectrums from the first Nyquist zone of all receiver’s channels to the single frequency axis and searching for the frequency where the spectrum components from all of the receiver’s channels coincided. Performance of the signal detector, which uses this algorithm in its operation, was not studied. In the absence of a mathematical description of such a detector, evaluating the digital undersampling receiver’s sensitivity becomes possible only in the late stages of prototyping when it can be done through experimental study. Additionally, it is impossible to set a detection threshold in the receiver according to the Neyman-Pearson criterion, which hardens building constant false alarm rate (CFAR) systems based on this type's receivers. This paper aims to develop the mathematical description of the digital undersampling receiver's detector and then, using this model, to get expressions and computer models to evaluate the characteristics of such receiver even in early stages of its development. This paper's main result is the developed mathematical tools necessary to evaluate the multichannel digital undersampling receiver’s signal detector performance. It is shown that the false alarm probability in such a detector does not exceed some value no matter how small the detection threshold is. The expression for evaluating the maximum false alarm probability by the receiver’s parameters is also presented in the paper alongside the true positive rate plots as a function of signal-to-noise ratio for the three-channel receiver. These results can be used in evaluating the digital undersampling receiver’s characteristics in the early stages of its development. It allows one to choose optimal values of the receiver’s parameters which are hard and expensive to change after prototyping is done, and there is an opportunity to evaluate the receiver’s characteristics experimentally. Moreover, the obtained mathematical expressions make it possible to set the receiver's detection threshold according to the Neyman-Pearson criterion and build on its base a CFAR-systems widely used for wideband signal analysis.

Channel Path Identification in mmWave Systems With Large-Scale Antenna Arrays

We consider the uplink channel estimation problem in a millimeter wave (mmWave) system with large-scale antenna arrays. Unlike many existing works which estimate the channel assuming that the number of channel paths is known a priori , we address the problem of channel estimation with an unknown number of channel paths. The spatial channel is transformed into the beamspace channel by the discrete Fourier transform (DFT). Based on the sparsity property of the beamspace channel, we propose three algorithms to estimate the number of paths, direction of arrivals (DoAs) and path gains. The first one is the Spectrum Weighted Identification of Signal Sources (SWISS) for the case when the channel statistics are unknown, which introduces a weight vector to amplify the desired signal and suppress the noise. The second one is the Neyman-Pearson criterion based-Detector (NPD) based on the Rician channel model, which adopts the Neyman-Pearson criterion to decide whether there exists a path on each DFT point. In practice, the DoAs are continuously distributed, leading to the power leakage problem. We solve this leakage problem by proposing the combined algorithm with leakage (CAL). Simulation results show that the proposed algorithms perform better than the conventional spatial smoothing.

3D Spectrum Sharing for Hybrid D2D and UAV Networks

In this paper, we study a three-dimensional (3D) spectrum sharing between device-to-device (D2D) and unmanned aerial vehicles (UAVs) communications. We consider that UAVs perform spatial spectrum sensing to opportunistically access the licensed channels that are occupied by the D2D communications of ground users. The objective of the considered 3D spectrum sharing networks is to maximize the area spectral efficiency (ASE) of UAV networks while guaranteeing the required minimum ASE of D2D networks. Using the tools from machine learning, we obtain the probability of spatial false alarm and the probability of spatial missed detection at the UAV, which helps us to characterize the density of active UAVs. Then, based on the Neyman-Pearson criterion, we further derive the coverage probability of D2D and UAV communications by leveraging the tools from stochastic geometry. In addition, the ASE of the D2D and UAV networks are also obtained. Simulation results show that a decrease in the spatial spectrum sensing radius of UAVs reduces the coverage probability of UAV communications but improves the ASE of UAV networks. Furthermore, the proposed tools allow obtaining the optimal spatial spectrum sensing radius of UAVs given certain network parameters.

Secure high‐speed spread spectrum transmission system with orbital angular momentum

Besides promotion in the spectrum efficiency, orbital angular momentum (OAM) can bring additional benefits to physical layer security. In this study, the authors will highlight the benefit of covert transmissions to deal with the contradiction between transmission rate and security in the traditional direct sequence spread spectrum (DSSS) system. Based on OAM, a secure high-speed spread spectrum (SS) transmission system is proposed. Specifically, the system architecture and mathematical models are established. Afterwards, the detection method for the proposed OAM SS system is investigated. Meanwhile, the corresponding bit error rate (BER) and performance of anti-interception are analysed. The performance of data anti-interception is evaluated based on computational complexity, while the performance of signal anti-interception is studied by Neyman–Pearson criterion. Furthermore, Monte–Carlo simulations are also implemented to show the effectiveness of the proposed system. Compared with the traditional DSSS system, the higher security can be achieved by the proposed system. Besides, the transmission rates and BERs are the same for the traditional DSSS system and proposed system. Therefore, the proposed OAM SS transmission system successfully handles the contradiction of the transmission rate and security. In other words, the secure high-speed transmission can be achieved by the proposed OAM SS system.

An Efficient NPRACH Receiver Design For NB-IoT Systems

Narrowband Internet of Things (NB-IoT) is a powerful technology for massive machine-type communications, which is imperative in the forthcoming 5G wireless communications. Unlike the long-time evolution (LTE) protocol, in the NB-IoT protocol specified by the third generation partnership project (3GPP), the narrowband physical random access channel (NPRACH) is newly introduced and its receiver performance is critical to the success of an NB-IoT system. In this article, an optimal activity detection scheme is first designed by using the Neyman–Pearson criterion. Then, a low-complexity iterative search algorithm is developed for the joint estimation of residual carrier frequency offset (RCFO) and timing advanced (TA), avoiding the effect of phase ambiguity. Finally, simulation results collaborate on the effectiveness and efficiency of the proposed receiver.

Detection method for the dynamic signal of sky screen-based velocity measurement system using Bayesian Generalized Likelihood Ratio Tests

This paper presents a method using Bayesian Generalized Likelihood Ratio Tests (BGLRT) to detect the dynamic signal of sky screen-based velocity measurement System under poor signal-to-noise ratio (SNR). The characteristics of the dynamic signal are systematically analyzed, and then a Bayesian classification model is formulated based on BGLRT. According to Neyman-Pearson criterion, we construct an adaptive threshold detector, thereby detecting the dynamic signal, effectively, under this SNR (−1 to 5 dB). Through semi-physical simulations and live ammunition experiments, the results demonstrate the proposed method can increase the effective detection height of this velocity measurement system by approximately 20 % compared with the previous methods.

EFFICIENCY OF OPERATIONAL DATA PROCESSING FOR RADIO ELECTRONIC EQUIPMENT

The paper deals with the statistical data processing algorithms in operation system of radio electronic equipment. The main purpose is analysis of data processing algorithm efficiency according to the analytical calculations and simulation results. During radio electronic equipment operation failures are possible. These failures affect on the equipment’s technical condition that can deteriorate. In case of condition-based maintenance, it is necessary to detect the time moment of deterioration beginning. Therefore, in this paper the deterioration detection algorithm was developed according to Neyman-Pearson criterion with a fixed sample size. The initial data are times between failures of radio electronic equipment, and these data can be identified by the exponential probability density function. The step-function model was chosen for failure rate change description. To estimate efficiency the operating characteristic was calculated. The simulation based on Monte-Carlo method confirmed the correctness of theoretical calculations.

Noise-Source Detection in an Oceanic Waveguide Using Interferometric Processing

The signal-to-noise ratio as a result of interferometric processing of noise-source field in an oceanic waveguide is estimated. The minimum duration of noise-source realization, which determines the maximum noise immunity, is evaluated. The noise-source signal detection based on the Neyman–Pearson criterion is considered. The range of input signal-to-noise ratios for the case where a single detector provides acceptable data on the source parameters is analyzed. An expression for the detection curves (dependences of the correct-detection probability on the signal-to-noise ratio at a fixed false-alarm probability) is derived. The corresponding numerical calculations are carried out.

Method for Changepoint Detection With Sample Size Accumulation During Radio Equipment Operation

Abstract The operation system determines the efficiency of the intended use of radio equipment. The operation system includes radio equipment, documentation, operational resources, personnel, processes, data processing algorithms, etc. The operation process is associated with conditions of priori uncertainty of models, parameters, external factors, etc. The main element for monitoring is technical condition of radio equipment. In a general case, this condition can deteriorate. The problems of technical condition deterioration analysis can be considered in terms of changepoint study. The present paper concentrates on the problem of synthesis and analysis of method for changepoint detection during radio equipment operation. The detection algorithm is constructed based on Neyman–Pearson criterion with sample size accumulation. During algorithm synthesis the thresholds were calculated according to Bellman’s approach. The problem of synthesis and analysis was solved by two methods: analytically and using statistical modelling. The efficiency measures for this algorithm are the probability of correct detection and mean time of decision making. The proposed algorithm has advantages in mean time of decision making compared with CUSUM detection method.

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corresponding probability of detection, probability of false alarm, and secondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs, and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; and ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable.

A novel system model for detecting primary user emulation attack in cognitive radio networks

The recent advances in wireless communication have addressed the problem of spectrum scarcity. The available wireless spectrum has become insufficient due to the increasing spectrum demand. The largest portion of the allocated spectrum is periodically used that guides to the underutilisation of the spectrum. The idea of Cognitive Radio (CR) technology is introduced to improve the spectrum efficiency. This concept provides a solution for the issue of spectrum demand in wireless networks. Owing the security issues, the CR network poses new challenges in wireless communications. In this article, a Primary User Emulation (PUE) attack is studied from the Neyman-Pearson criterion point of view and a novel system is proposed to overcome the PUE attack. A novel system model contains a different configuration of Primary Users (PUs) which is proposed and studied. The novel system model with maximum likelihood criterion will solve the PUE attack issues as efficiently as possible. The experimental result shows the characteristics of the false alarm and missed detection probability, during the implementation of the proposed system.

Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change

Given that signal is weakened to a certain extent in the process of noise suppression using mainstream method, and that new noise is introduced by signal processing system, causing the decrease of detection performance, to improve the performance of detection to BPSK signal under the condition of strong noise and no prior information, the detection algorithm of BPSK signal of bistable stochastic resonance model based on scale change is proposed in this study. Using classical bistable stochastic resonance (BSR) system, only low-amplitude and low-frequency periodic signal can be processed. Scale change is first made to BSR in this study, verifying that BSR can be applied to high-frequency BPSK signal under high sampling frequency condition, and nonlinear threshold detection system is designed following Neyman-Pearson criterion to deduce and quantitatively show error rate of detector. Besides, complete flow for signal detection was built by taking it as feedback quantity to adjust the system parameters adaptively. Scale change feasibility and applicability of algorithm proposed in this study were verified through simulation experiment, which lays the theoretical basis for the detection of weak BPSK signal under low signal-to-noise ratio (SNR).