Averaging Constant False Alarm Rate Research Articles

Adaptive filter entropy monitoring method for scalar magnetic detection using optically pumped magnetometers

In this paper, an adaptive filtering minimum entropy detector (AF-MED) method is proposed, which is an improvement on the minimum entropy detector (MED) method. The improvement can be divided into two parts. Firstly, in view of the low detection rate of the MED method in the case of low signal-to-noise ratio (SNR), an adaptive filtering technology is added, which can accurately and dynamically determine the frequency range of the magnetic field according to the moving speed of the magnetic target, and effectively restrain the influence of environmental noise on the magnetic field. Compared with the MED method, the detection rate is increased by 48%. Secondly, using the kernel density estimation method to calculate the probability density value, the advantage is that there is no need to make any predictions or assumptions about the target, which makes the method more convenient to achieve. Furthermore, in this study, numerous simulations and experiments were performed, and the cell averaging constant false alarm rate detector was used to calculate the detection rate. The average value of detection rates of AF-MED, MED and the orthogonal basis function (OBF) are about 69%, 21% and 43% respectively when SNR is low (SNR from −25 dB to −50 dB). The results demonstrated the superiority of the proposed method over the MED and the OBF.

An improved denoising method for eye blink detection using automotive millimeter wave radar

With the development of radar technology, the automotive millimeter wave radar is widely applied in the fields including internet of vehicles, Artificial Intelligence (AI)-based autonomous driving, health monitoring, etc. Eye blink, as one of the most common human activities, can effectively reflect the person’s consciousness and fatigue. The contacted eye blink detection often leads to uncomfortable experience and the camera-based eye blink detection has privacy issues. As an alternative, the non-contacted eye blink detection based on automotive millimeter wave radar resolves the aforementioned issues and has been received much attention. This paper proposes an eye blink detection method using the frequency modulated continuous wave radar. Firstly, the position of the person’s head is estimated by carrying out fast Fourier transform on the intermediate frequency signal, and the signals of the range bins at the head are extracted. Then, the complete ensemble empirical mode decomposition with adaptive noise algorithm is applied to decompose the eye signals into a series of intrinsic mode functions (IMFs), and the singular value decomposition is adopted to constrain the selection and reconstruction of the useful IMFs related to the eye blink signal. Finally, the short-time Fourier transformation and cell average constant false alarm rate are applied to detect the eye blink behavior. Experiments are carried out to validate the effectiveness of the proposed eye blink detection method.

Detection of sea‐surface target of coastal defense radar based on Stacked Autoencoder (SAE) algorithm

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea-surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors’ coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two-dimensional (2-D) images through a sliding window, mapping the cell under test (CUT) and the 2-D images one by one and performing classification or detection from the perspective of 2-D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA-CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2-D sea surface targets based on the SAE algorithm in coastal defense radar.

Granular Fertilizer Mass Flow Measurement and Vehicle Experiments Based on Microwave Doppler Method

HighlightsA microwave Doppler-based fertilizer mass flow measurement system was developed.The measurement system was applied in vehicle experiments.Power spectra of mass flow signal and vibration signal were obtained and analyzed.Interference suppression algorithm based on CA-CFAR reduced vibration interference.Fertilizer mass flow measurement accuracy was improved by interference suppression.Abstract. The fertilizer mass flow measurement system is typically installed on the fertilizer applicator. However, vehicle vibrations are inevitable during field operation of fertilizing equipment, and can interfere with mass flow signals, thereby affecting the accuracy of mass flow measurements. In this article, a mass flow measurement system based on the microwave Doppler method was introduced. The dominant frequency (fdot) related to fertilizer velocity and the power spectral density (PSD) related to fertilizer concentration were obtained from the Doppler signal of the granular fertilizer processed using fast Fourier transform. The product of fdot and PSD is defined as the sensor output value (SOV). The relationship between SOV, PSD, and fertilizer mass flow (FMF) was studied under vehicle conditions. The linear regression models of 22-8-10 (MOP) high-nitrogen poly-?-glutamic acid fertilizer were established using the least squares method, and the vibration signals were measured. Based on analysis of the power spectra of the mass flow and vibration signals, an interference suppression algorithm based on cell average constant false alarm rate (CA-CFAR) was presented to reduce vibration interference. To verify the performance of the novel vibration interference reduction algorithm, vehicle experiments were conducted at different tractor velocities. The results show that mass flow measurements ranged from 1300 to 3000 g min-1. It was found that using only the PSD related to concentration for flow measurement gave better measurement accuracy compared with SOV, and relative errors of the system were within 8.1%. Correlation between the PSD and FMF improved with interference suppression algorithm based on CA-CFAR. The determination coefficient increased from 0.01 to 0.92, 0.97 to 0.99, and 0.96 to 0.98 at three different tractor velocities, respectively. From the results, it was evident that the algorithm effectively eliminated vibration interference signals in vehicle conditions. In future work, the conditions of the soil will be considered and the performance of the mass flow measurement system will be tested in the field. Keywords: Fertilizer mass flow, Microwave Doppler radar, Tractor vibration, Vehicle experiment, Vibration interference suppression.

Application of Polarimetric-SAR Decompositions on RADARSAT-2 Fine Quad-Pol Images to Enhance the Performances of Ships Detection Algorithms

Remote sensing of vessels is an important tool for ship safety and security at sea. In this work, we are interested in improving ships detection using polarimetric Synthetic Aperture Radar (SAR). To develop the appropriate method, different processing techniques are applied on Pol-SAR images such as fusion and polarimetric decompositions and we use adaptive threshold detectors to assess the performances of the processing techniques. The data exploited in this work were acquired on a port area of the city of Vancouver by using RADARSAT-2 satellite. In this paper it is shown first that when exploiting single polarization, the HH channel provides the highest score of detection probability (PD) of 87.2% for a false alarm probability (PFA) of 0.05%, and this while using the cell averaging constant false alarm rate (CA-CFAR) detector. The result is obtained comparatively with other polarizations (HV, VV) and detection algorithms. Second, the fusion of polarimetric channels achieves its best performances with the CA-CFAR detector, comparatively with the two parameters (2P)-CFAR and generalized likelihood ratio test (GLRT). Third, we find that among the conventional polarimetric techniques, the singular value decomposition (SVD) combined with CA-CFAR detector gives the best results and achieves a detection probability of 91% for a false alarm of 0.05%. This result was obtained by comparing the performances of other combinations of decompositions (Pauli, Freeman, Yamaguchi), fusion and ships detection algorithms. In this paper, we obtain with the proposed approach an increase of 3.8% in detection probability for false alarm probability of 0.05%.

Novel CA-CFAR Approach for Improvement of Doppler Sensor-based Heart Rate Variability Estimation.

Heart rate variability is one of major physiological parameters to reflect our stress, which has motivated researchers to investigate a Doppler sensor-based non-contact heartbeat interval estimation algorithm. As one of such methods, we have previously proposed a spectrogram-based method. In this method, the spectrum that might be due to heartbeats is integrated over a spectrogram, and then heartbeat interval is estimated by detecting peaks over the integrated spectrum. However, when a subject moves, undesired peaks with large amplitude appear, which causes the incorrect peak detection. As one of the technique to eliminate the undesired peaks with large amplitude, there is CA-CFAR (Cell Average-Constant False Alarm Rate). CA-CFAR is the technique to detect a signal, when the amplitude of a signal exceeds a threshold calculated with average amplitude of signals before and after the investigated one. However, depending on the duration of body movements, the influence of body movements might be included within the signals used for the threshold calculation, which might results in the detection failure of undesired peaks. This is because the length of GT (Guard Time) is fixed, where GT is the time to prevent the signal used for the threshold calculation from including the investigated signal components. To solve this problem, we propose a novel CA-CFAR in which the length of GT is set as the latest peak interval and only the signal before the investigated one is used so that the influence of body movements does not affect the threshold calculation. Through the experiments where a subject moves, i.e., typing, we confirmed that our spectrogram-based heart rate variability estimation method with the proposed CA-CFAR outperformed the one with CA-CFAR based on fixed GT by the RMSE (Root Mean Squared Error) between the estimated heartbeat interval and the ground truth value of the one.

Radar Detection of Small Target in Sea Clutter Using Orthogonal Projection

Sea clutter submerges the small target echo, which is disadvantageous for radar target detection. In this letter, we propose to suppress the sea clutter by orthogonal projection (OP). We construct the clutter subspace directly by the observed data vectors of neighboring range cells of the cell under test (CUT), and then suppress the sea clutter of the CUT by projecting the observed signal of the CUT to the clutter orthogonal subspace. We design a new detector which combines the cell averaging constant false alarm rate (CFAR) detector with the OP. The clutter suppression performance and computation complexity of OP are compared with those of singular value decomposition (SVD), respectively. Theoretical analysis and experimental results with real sea clutter demonstrate that the radar CFAR detection performance improves by using OP. And the CFAR detector based on OP is convenient to implement. The CFAR detection performance based on OP is the same as that based on SVD, whereas the computation complexity of OP is much less than that of SVD.

A Novel Carrier Leakage Cancellation Algorithm for Multiple Target Detection

False alarms and misdetections caused by the carrier leakage problem attract increasing attention for resource-limited platforms. To tackle the problem, we propose a target protected carrier leakage cancellation algorithm to rebuild the leakage signal from reference cells with those cells randomly updated based on each detection result to exclude the targets. The proposed algorithm eliminates the carrier leakage effectively without introducing false target due to the non-involvement of the target information in the reference cells based on a random update strategy. The noise level of our proposed algorithm is less than that of the commonly used algorithms such as the Cell average constant false alarm rate (CA-CFAR) detection method. As verified in the simulations, the proposed algorithm performs better than the CA-CFAR method.

Study on CA-CFAR Algorithm Based on Normalization Processing of Background Noise for HI of Optical Fiber

Optical fiber pre-warning system (OFPS) is often used to monitor the occurrence of disasters such as the leakage of oil and natural gas pipeline. It analyzes the collected vibration signals to judge whether there is any harmful intrusion (HI) events. At present, the research in this field is mainly focused on the constant false alarm rate (CFAR) methods and derivative algorithms to detect intrusion signals. However, the performance of CFAR is often limited to the actual collected signals distribution. It is found that the background noise usually obeys non-independent and identically distribution (Non-IID) through the statistical analysis of acquisition signals. In view of the actual signal distribution characteristics, this paper presents a CFAR detection method based on the normalization processing for background noise. A high-pass filter is designed for the actual Non-IID background noise data to obtain the characterization characteristic. Then, the background noise is converted to independent and identically distribution (IID) by using the data characteristic. Next, the collected data after normalization is processed with efficient cell average constant false alarm rate (CA-CFAR) method for detection. Finally, the results of experiments both show that the intrusion signals can be effectively detected, and the effectiveness of the algorithm is verified.

Target Detection in Sea-Clutter Using Stationary Wavelet Transforms

Small target detection in the maritime environment remains a challenging problem. Maritime radars traditionally use non-coherent processing methods due to the time-varying and range-varying nature of the Doppler spectra. However, the radar backscatter may contain sea-spikes, which can last for seconds and resemble targets. This paper presents an application of stationary wavelet transforms (SWT) to improve target detection in medium grazing angle X -band sea-clutter. Since different components (sub-bands) of the SWT decomposition reveal different spectral characteristics of the returned signal, a method of selecting the appropriate sub-band is presented using an entropy-based metric. A Monte Carlo simulation using a cell averaging constant false alarm rate detector is then used to demonstrate the improvement of the SWT scheme against unfiltered data.

Two-Dimensional Object Detection Using Accumulated Cell Average Constant False Alarm Rate

The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of the seafloor. This paper proposes an approach for developing an efficient system for automatic object detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR2D), where the optimization of the computational effort is achieved. This approach employs image segmentation as preprocessing step for object detection, which have provided similar results with other approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour techniques. The SSS sea bottom images are segmented for the 2D object detection using these four techniques and the segmented images are compared along with the experimental results of the proportion of segmented image (P) and runtime in seconds (T) are presented.

레이저 거리계를 이용한 차량 전장 측정 방법에 관한 연구

Determination of type of a vehicle is being used in various areas such as collecting tolls, collecting statistical traffic data and traffic prognosis. Because most of the vehicle type classification systems depend on vehicle length indirectly or directly, highly reliable automatic vehicle length measurement system is crucial for them. This study makes use of a pencil beam laser rangemeter and devises a mechanical device which rotates the laser rangemeter. The implemented system measures the range between a point and the laser rangemeter then indicates it as a spherical coordinate. We obtain several silhouettes of cross section of the vehicle, the rate of change of the silhouettes, signs of the rates then squares the rates to apply cell averaging constant false alarm rate (CA-CFAR) technique to find out where the border is between the vehicle and the background. Using the border and trigonometry, we calculated the length of the vehicle and confirmed that the calculated vehicle length is about 94% of actual length.

Joint Interference Detection Method for DSSS Communications Based on the OMP Algorithm and CA-CFAR

The existing direct sequence spread spectrum (DSSS) communications interference detection algorithms are confined to the high sampling rate. In order to solve this problem, algorithm for DSSS communications interference detection was designed based on compressive sensing (CS). First of all, the orthogonal matching pursuit (OMP) algorithm was applied to the interference detection in DSSS communications, the advantages and weaknesses of the algorithm were analyzed; Secondly, according to the weaknesses of the OMP algorithm, a joint interference detection method based on the OMP algorithm and cell average constant false alarm rate (CA-CFAR) was proposed. The theoretical analyze and computer simulation all proved the effectiveness of the new algorithm. The simulation results show that the new method not only could achieve the interference detection, but also could estimate the interference quantity effectively.

Accumulated CA–CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data

This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average–constant false alarm rate (CA–CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA–CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average–constant false alarm rate in 2-D (ACA–CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental comparison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented.

Detection performance analysis of recurrence quantification analysis measures for low observable target within sea clutter under different sea conditions

An approach based on recurrence quantification analysis (RQA) measures is proposed to detect the low observable target within sea clutter in this study. Based on the criterion named the fixed amount of nearest neighbours, recurrence plots (RPs) are constructed. RQA measures are used to describe the line structures of the RPs. According to the differences between the RQA measures of sea clutter with and without a target, the low observable target within sea clutter can be detected. Four sea clutter datasets under different sea conditions in the IPIX OHGR database are selected to analyse the target detection performance of RQA measures. Compared with the multifractal correlation spectrum approach and the cell average constant false alarm rate detector, the author's approach can achieve a higher detection probability. Furthermore, the authors analyse the receiver operating characteristic curves of RQA measures in all of polarisations under different sea conditions. The experimental results show that the horizontal–horizontal polarisation dataset and one of the horizontal–vertical and vertical–horizontal polarisation datasets should be selected for the target detection, and the significant wave height has a greater influence on the target detection performance than the wind speed does.

TOA Estimation Technique for IR-UWB Based on Homogeneity Test

This paper deals with the estimation of the time of arrival (TOA) of ultra-wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses a threshold that is derived analytically according to a preset false alarm probability. To highlight the effectiveness of the developed approach, simulation setups as well as real data experiments are conducted to assess the performance of the new TOA estimation algorithm. Thereby, the proposed method is compared with the cell averaging constant false alarm rate technique, the threshold comparison algorithm, and the technique based on maximum energy selection with search back. The obtained results are promising, considering both simulations and collected real-life data.

Detection of Ships in Marine Environments by Square Integration Mode and Multilayer Perceptrons

A novel method for detecting ships in marine environments is presented in this paper. For this purpose, the information contained in the marine images obtained by a measuring and monitoring marine system is used. The ship detection is done by multilayer perceptrons (MLPs). In the first approach, the MLP processes the information extracted from the images using horizontal or vertical integration modes. However, if a suitable combination of these integration modes is done, better detection performances are achieved. Therefore, the use of an improved integration mode is proposed, which is based on a square shape. These modes are also used in a commonly used detector, the cell averaging constant false alarm rate (CA-CFAR) detector, which is taken as reference in our experiments. The comparison of the performances of both detectors shows how the MLP-based detector outperforms the CA-CFAR detector in all the cases under study. This comparison is based on objective (probabilities of false alarm and detection) and subjective estimations of their performances. The MLP-based detector also presents another advantage, particularly when the square integration mode is considered: high-performance robustness against changes in the marine environmental conditions.

Adaptive CA-CFAR threshold for non-coherent IR-UWB energy detector receivers

In the present letter, a new adaptive threshold comparison approach for time-of-arrival (TOA) estimation in ultra wideband (UWB) signals is proposed. This approach can be used with non-coherent energy detector receivers in UWB systems. It exploits the idea of cell averaging constant false alarm rate (CA-CFAR) used in radar systems, where the threshold changes every energy block. The performance of several approaches are compared via Monte Carlo simulations using the CM1 channel model of the standard IEEE 802.15.4a. Both simulation results and comparisons are provided highlighting the effectiveness of the proposed approach.

Tracking of Divers using a Probabilistic Data Association Filter with a Bubble Model

Detection and tracking of divers have become an important factor in port protection against underwater intruders. A problem arises from divers with open breathing systems because detections of the air bubbles they produce can mislead the tracking filter and sometimes result in a lost track. In this paper a probabilistic model is developed which reflects the probability that a false measurement originates from the bubbles. The novel contribution of this paper is the integration of this model in the probabilistic data association filter (PDAF) to improve the track continuity. The bubble detections may also cause confusion in the track initiation. To prevent this problem, a clustering method is proposed based on morphological operators which allows tracks to be initialized based on two-point differencing of the cluster centroids from succeeding scans. This morphological clustering method is included in a cell averaging constant false alarm rate (CA-CFAR) detector in such a way that both the point detections and their corresponding clusters can be fed to the tracking filter. These techniques are implemented and applied to real data of two divers, one with an open breathing system and the other with a closed breathing system, operating simultaneously in a coastal area. The real data were recorded from an active 90 kHz narrowband multibeam imaging sonar.

Field programmable gate array-based design and realisation of automatic censored cell averaging constant false alarm rate detector based on ordered data variability

The design and field programmable gate array (FPGA)-based realisation of automatic censored cell averaging (ACCA) constant false alarm rate (CFAR) detector based on ordered data variability (ODV) is discussed here. The ACCA-ODV CFAR algorithm has been recently proposed in the literature for detecting radar target in non-homogeneous background environments. The ACCA-ODV detector estimates the unknown background level by dynamically selecting a suitable set of ranked cells and doing successive hypothesis tests. The proposed detector does not require any prior information about the background environment. It uses the variability index statistic as a shape parameter to accept or reject the ordered cells under investigation. Recent advances in FPGA technology and availability of sophisticated design tools have made it possible to realise the computation intensive ACCA-ODV detector in hardware, in a cost-effective way. The architecture is modular and has been implemented and tested on an Altera Stratix II FPGA using Quartus II software. The post place and route result show that the proposed design can operate at 100-MHz, the maximum clock frequency of the prototyping board and for this frequency the total processing time required to perform a single run is 0.21--s. This amounts to a speedup for the FPGA-based hardware implementation by a factor of -110 as compared to software-based implementation, which takes 23--s to perform the same operation.