Cell Averaging Constant False Alarm Research Articles

Adaptive Trajectories’ Constant False Alarm Rate Mirror Filters and Elevation Angle Evaluation for Multiple-Input Multiple-Output Radar-Based Hand Gesture Recognition

Gesture recognition technology has been quickly developed in the field of human–computer interaction. The multiple-input multiple-output (MIMO) radar is popular in gesture recognition because of its notable spatial resolution. This work proposes a MIMO radar-based hand gesture recognition algorithm with low complexity. We leverage low-complexity adaptive signal processing to extract trajectory information and minimize noise to create a system that can be applied in real-world applications with small training datasets. First, a spectrum analysis is utilized on range-Doppler maps (RDMs), and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness of noise. Then, the features related to the distance, speed, direction, and elevation angle of the moving object are determined using the proposed adaptive signal analysis techniques. For classification, the random forest algorithm is implemented. The proposed system can precisely distinguish and identify eight gestures, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anti-clockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

Monitoring of sow postural activity from 3D millimeter-wave radar imaging

This paper presents a novel approach to monitor the postural activity of sows in farrowing pen using a millimeter-wave radar imaging system. Three-dimensional images of the scene are obtained from a 77 GHz Multiple-Input Multiple Output radar and the mechanical scanning of the radar beam. We show that the processing of radar images based on the Cell-Averaging Constant False Alarm Rate allows detecting the standing and lying postures of sows despite the electromagnetic clutter. The experimental study reported here is performed from the monitoring of 16 sows from the Large White breed for a total of 46 h. 3D positions of the sow are recorded and heatmaps of the sow locomotor activity are proposed. With few training radar data, we perform a classification of standing and laying postures with a mean balanced accuracy greater than 90%. The radar-based technique applied here in a cluttered environment inside farrowing pens may be extended to monitor pigs under other housing condition.

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.

A General CA-CFAR Performance Analysis for Weibull-Distributed Clutter Environments

In this letter, we provide a general cell-averaging constant false alarm rate (CA-CFAR) performance analysis considering homogeneous Weibull-distributed clutter environments. To do so, we provide generalized expressions for the probability of detection (PD) and probability of false alarm (PFA). Notably, these expressions permit arbitrary values for the shape parameter of the Weibull interference. So far, all CA-CFAR performance analyses were carried out by setting the shape parameter of the Weibull samples within the CFAR window equal to one, which narrows the overall evaluation spectrum. In this study, we allow the shape parameter to take values greater than or equal to one—regime of paramount importance in many practical applications—and still manage to derive manageable and easy-to-implement solutions. All our expressions are confirmed via Monte-Carlo simulations. Numerical results indicate that radar detection is improved as the shape parameter increases.

Performance Evaluation of SOCA-CFAR Detectors in Weibull-Distributed Clutter Environments

In this letter, we derive a novel exact expression for the probability of false alarm (PFA) and an approximate closed-form solution for the probability of detection (PD) of a smallest of cell-averaging constant false alarm rate (SOCA-CFAR) detector operating over Weibull-distributed clutter. For the analysis, we consider an exponentially distributed target and allow arbitrary values for the shape parameter of clutter interference. To the best of our knowledge, there are no exact or approximate performance evaluations for an SOCA-CFAR detector considering arbitrary values for the shape parameter of the Weibull interference samples (i.e., different from 1) contained within the CFAR window. Therefore, our analytical derivations generalize previous performance evaluation studies and take a small step toward a better understanding of more realistic SOCA-CFAR detectors. Moreover, we obtain exact formulations for the probability density function (PDF) and the cumulative distribution function (CDF) for a minimum of two sums of independent and identically distributed (i.i.d.) Weibull random variables. Numerical results indicate that the system performance improves as the shape parameter of the Weibull interference increases. The validity of all our expressions is confirmed via Monte Carlo simulations.

Incoherent Integration Detection Method of Airborne Phased Array Radar in a Multipath Environment

Airborne phased array radar (PAR) suffers from multipath problems when flying over a calm sea surface. The existence of a multipath phenomenon will cause the electromagnetic echo of the same target to be reflected back to the airborne PAR from two paths, namely, direct path (DP) and multipath. Compared with the ground-based radar, the target echo received by airborne PAR in the multipath environment has two important characteristics: one is that the DP signal and the multipath signal exist in different range bins, and the other is that the radar cross section (RCS) in the DP direction may be smaller than that in the multipath direction. Considering these two characteristics, this paper first proposes a target pairing algorithm for matching the DP range and multipath range of the same target in signal detection, and then, combined with the cell-averaging constant false alarm rate (CA-CFAR) detection model, an incoherent integration detection method for airborne PAR in the multipath environment is proposed. In the target pairing process, the geometric structure relationship of the airborne PAR model can be fully utilized. After a successful target pairing process, the energy of the multipath signal will be incoherently accumulated into the corresponding DP range bin, so as to improve the probability of DP range bin data passing the detection threshold. In essence, the proposed method makes full use of multipath energy to improve the detection capability of airborne PAR in the multipath environment. Finally, the detection probability of the proposed method is given, and the detection performance is analyzed.

High-precision 1D distance monitoring with radar data

ABSTRACT A measurement setup based on a high-precision, low-cost FMCW radar sensor has been developed for radar-based 1D deformation monitoring of engineering structures. The method enables contactless and time-continuous scans, but has lower acquisition costs compared to commercially available terrestrial laser and radar technologies. The article contains the instrument-related description of the sensor with accuracy tests for 1D distance measurements on a laboratory scale as well as a case study for monitoring the movements of a headframe over the course of the day. The detectability of deformations was statistically verified by means of a hypothesis test and applied to the case study under real conditions. With a defined target object (corner reflector), measuring accuracies of up to 10 μm with a 99,7% confidence level could be achieved. The case study shows absolute movements of the headframe during the day of 1 mm. Changes of 0,2 mm can be detected in time intervals of 9 min with a statistical significance level of 95%. Abbreviations : CA-CFAR: Cell-averaging constant false alarm rate; CR-I, CR-II: Corner reflectors; CUT: Cell Under Test; DFT: Discrete Fourier transformation; EXP: Experiment; FMCW: Frequency-modulated continuous-wave; iSDR-C:indurad ScanningDynamicRadar-Compact; LOD: Limit of detection; PI: Peak interpolation

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.

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

Computer Simulation of Extended Neyman–Pearson Detector in Monostatic Pulse Radar

An extended Neyman–Pearson detector based on the combination of a traditional matched and wavelet filter in pulsed monostatic radar was proposed in this paper. Its properties were compared with properties of the cell average constant false alarm radar detector in the presence of white noise. The properties of detectors were examined in the receiver operating characteristic curves, parameter space. It consisted of false detection rate and the rate of true positive detected targets. In order to investigate the influence of temperature, the new parameter space was constructed: the temperature versus the rate of true positive detected targets. The traditional Neyman–Pearson detector based on a matched filter was fully operational and superior in the temperature range between 290[Formula: see text]K and 350[Formula: see text]K. Below this temperature range, its performance is completely degraded. It has been shown that by adding a wavelet filter, it can extend this temperature range between 260[Formula: see text]K and 350[Formula: see text]K. In the temperature range, where all detectors are fully operational, extended and classic Neyman–Pearson detectors had better performance than constant false alarm radar detector in the receiver operating characteristic curves’ parameter space.

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%.

A Target Detection Scheme With Decreased Complexity and Enhanced Performance for Range-Doppler FMCW Radar

In conventional fast-ramp-based target detection schemes for the frequency modulated continuous wave (FMCW) radar systems, all chirp sequences are performed fully on two-dimensional fast Fourier transforms (FFTs) to estimate range and velocity of a target, and all range-Doppler cells are scanned by the two-dimensional (2-D) cell-averaging constant false alarm rate (CA-CFAR) detector to extract valid targets against background noise. This results in an extremely high computational complexity. To decrease the complexity while maintaining a desired performance, this article proposes a target detection scheme based on distributed one-dimensional (1-D) CA-CFAR and region-of-interest (ROI) preprocessing, which performs Doppler-FFT only for range ROI cells and scans only Doppler ROI cells to extract pretargets. Simulation results indicate that the proposed scheme significantly decreases complexity and simultaneously improves detection performance in terms of detection probability, figure of merit (FoM), and CFAR loss, as compared with the conventional schemes. The advantages of the proposed scheme over the conventional ones are further verified by field tests under multitarget scenario, using our recently developed FMCW radar with high angular resolution.

Detection of maneuvering target with motion compensation errors based on α log-determinant divergence and symmetrized α log-determinant divergence

Long-term coherent integration is an effective method for target detection. However, the inevitable motion compensation errors and complex background clutter seriously limit the detection performance of a cell-averaging constant false alarm rate (CFAR) detector. A generalized CFAR detector on the Riemannian manifold of Hermitian positive-definite matrix is proposed. Based on the α log-determinant divergence (α-LDD) and symmetrized α-LDD, the mean matrix of the reference cells is first estimated. Then the dissimilarity between the covariance matrix of the cell under test and the mean matrix is measured to determine the presence of a target. Numerical experiments show that the proposed detectors have better detection performance than other representative methods when detecting a target with motion compensation errors in complex background.

Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination

A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system.

Estimation and Mitigation of Time-Variant RFI Based on Iterative Dual Sparse Recovery in Ultra-Wide Band Through-Wall Radar

This paper focuses on the time-variant radio frequency interference (RFI) issue that ultra-wide band (UWB) through-wall radar (TWR) is faced with, and presents an iterative dual sparse recovery (IDSR) framework to combat it. The framework consists of two stages: 1) RFI estimation and detection and 2) IDSR of scattered echoes from objects and RFI signals. In the first stage, an overlapped short time Fourier transform is employed to construct and update the discrete frequency Doppler spectrum (DFDS). Then, a minimum statistic operation is conducted on the DFDS to estimate RFI signals, followed by detection via a 1-D cell-averaging constant false alarm rate detector to determinate whether RFI signals exist or not. In the second stage, a dual sparse model of the collected signals is set up, based on the fast-time frequency sparsity of RFI signals because of their narrow bands and the Doppler frequency sparsity of scattered echoes from objects because of their limited moving velocities. The alternating direction method of multipliers (ADMM) is introduced to iteratively and alternately recover RFI signals and scattered echoes from objects. Specifically, the iterative hard thresholding (IHT) method is used to complete the two sparse recovery operations. The involved two sparse dictionaries are simple and only made up of inverse discrete Fourier transform basis and independent of the received signals. The IDSR framework is able to relieve the influences of RFI signals and scattered echoes from objects on each other, and thus can reconstruct the two type signals to the maximum limit. Field experiments using a UWB TWR were carried out to verify the proposed method.

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.