6 results about "Likelihood-ratio test" patented technology

This invention discloses a method for detecting multi-channel constant-mode signals without prior electromagnetic interference, belonging to the field of radar signal processing technology. The method includes modeling multi-shot data received by a multi-channel antenna array to obtain high-dimensional multi-channel data; introducing weight vectors to project the high-dimensional multi-channel data onto a single channel for dimensionality reduction, resulting in single-channel weighted data; constructing a generalized likelihood ratio test detector without prior spatial electromagnetic interference information based on the probability density functions under two assumptions: the presence and absence of multi-channel constant-mode signals; and building an optimization model based on the generalized likelihood ratio test detector without prior spatial electromagnetic interference information. A hybrid optimization framework combining alternating optimization and alternating direction multiplier methods is used to solve the optimization model, obtaining the detection parameters and completing the multi-channel constant-mode detection.

Embodiments of the application disclose a kind of distributed communication signal detection method, device and system based on generalized likelihood ratio, wherein the method is applied to fusion center, comprising the following steps: S1: receiving the observation data transmitted by multiple receiving nodes; S2: under the assumption that radiation source does not exist and radiation source exists, respectively construct the joint conditional probability density function of all observation data of all receiving nodes within the observation time; S3: adopt NP criterion to construct the expression of generalized likelihood ratio test statistic; S4: according to maximum likelihood criterion, estimate the covariance matrix in the joint conditional probability density function under the assumption of two kinds; S5: the estimated value of the covariance matrix under the assumption of two kinds is substituted into the expression of generalized likelihood ratio test statistic, and compared with the decision threshold set according to false alarm probability, to complete distributed communication signal detection. The application can improve the detection probability of communication signal under low signal-to-noise ratio, and has good noise resistance when there is noise fluctuation in different receiving nodes.

A method and system for sea surface target recognition based on range-time images, belonging to the field of measurement image recognition and processing, firstly maps the actually observed sea surface RT image into sea surface RT image data with amplitude following a standard Rayleigh distribution. The covariance matrix of the mapped sea clutter data is constructed. A binary hypothesis testing method is used on the mapped sea clutter data to construct a generalized likelihood ratio test statistic. The optimal estimate is substituted into the log-likelihood ratio expression to obtain the standard form of the mapped generalized likelihood ratio detector. The detector is then used to identify whether a target exists in the range cell to be detected. This invention can convert arbitrary non-Gaussian sea surface RT image data into RT image data with amplitude following a standard Rayleigh distribution, thereby achieving unified processing in complex sea clutter environments and effectively improving the accuracy of target recognition under complex non-Gaussian sea clutter backgrounds.

The present application relates to spaceflight measurement and control technical field, especially relate to a kind of generalized data detection and simplification method, system and equipment for improving the precision of space target positioning, comprising: obtaining the observation data of space target, construct extended Gaussian-Markov model containing potential modeling error, generate multiple hypothesis set by different combination of modeling error parameter;Utilize likelihood ratio test criterion to construct the test statistic between different hypotheses, according to the observation environment requirement self-adaptive selection executes generalized data detection process or generalized data simplification process;Determine the optimal model hypothesis of current observation by multiple hypothesis testing, based on the optimal model hypothesis parameter adaptive estimation is carried out, and the position parameter of space target is solved.This application guarantees high-precision requirement by forward selection strategy, enhances robustness under complex environment by backward elimination strategy, realizes the effective identification and elimination of multiple modeling error, significantly improves the precision and reliability of space target positioning.

The application provides a bearing fault automatic detection method and system based on iterative likelihood ratio test, relates to the technical field of bearing fault periodic signal modeling and period automatic detection, and comprises the following steps: S1, collecting a noisy periodic signal from a fault bearing by using a sensor; S2, constructing a constrained linear model, performing parameter estimation and calculating a likelihood function after segmenting the signal by the constrained linear model; S3, obtaining a likelihood function waveform graph by scanning a period parameter; S4, obtaining an accurate period from the likelihood function waveform graph by iterative likelihood ratio test; and S5, performing fault diagnosis on the bearing by using the period estimation result. The application can make the regression model more stable, the period estimation effect more accurate, effectively eliminate false positives and false negatives of the fault period when the signal is long enough through iterative hypothesis testing.

A system identifies degraded positional information by combining data from Global Navigation Satellite System (GNSS) and Alternative Positional Sources (APS) such as inertial measurement units. GNSS sources generate both location data and associated metadata, while APS provide additional location reports. An error modeling module computes an APS error covariance correction to indicate the accuracy of APS data. A primary classifier receives GNSS and APS reports along with the covariance correction and performs a generalized likelihood ratio test to assess GNSS data degradation. Simultaneously, a secondary classifier analyzes GNSS metadata to produce an independent degradation assessment. A degradation aggregation module then validates the primary assessment using the secondary one, ultimately generating a comprehensive GNSS positional degradation report that identifies and confirms instances of degraded location accuracy.