Cell-averaging Constant False Alarm Rate 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.

Exact Closed-Form P fa Expressions for CA- and GO-CFAR Detectors in Gamma-Distributed Radar Clutter

In this correspondence, two exact closed-form expressions for the probability of false alarm have been derived assuming a homogeneous Gamma distributed clutter. These expressions concern both the cell averaging CFAR (CA-CFAR) and the greatest of CFAR (GO-CFAR) detectors. As it will be shown, the proposed expressions are given in terms of the Gauss hypergeometric function and the second Appell function. Our expressions are then examined and validated numerically, by comparing them to their counterparts computed using numerical integrals and Monte-Carlo simulations, considering various scenarios.

CA-CFAR Performance in K-Distributed Sea Clutter With Fully Correlated Texture

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\text {D}}$ </tex-math></inline-formula> ) and the probability of false alarm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\text {FA}}$ </tex-math></inline-formula> ) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings.

Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conven-tional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter dis-tribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the trimmed-mean CFAR (TM-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

Application of CFAR detection to multiple pulses for gamma distributed clutter

ABSTRACT This paper deals with radar constant false alarm rate (CFAR) detection for multiple pulses for gamma-distributed clutter. In this work, some CFAR detectors are extended to cover the case of multiple pulses. Moreover, using the proprieties of the gamma distribution, the expressions of the false alarm probability are also generalized to multiple pulses transmission. The obtained results show the detection performance of multiple pulses generalized detectors – cell averaging-CFAR, greatest of-CFAR and order statistic-CFAR – compared with their counterpart in single-pulse detectors.

Convolutional Neural Network-Based Target Detector Using Maxpooling and Hadamard Division Layers in FM-Band Passive Coherent Location

The constant false alarm rate (CFAR) has been widely used in radar systems to detect target echo signals because of its simplicity. With the recent development of different types of neural networks (NNs), NN architecture-based target detection methods are also being considered. Several studies related to NN-based target detectors have introduced multi-layer perceptron-based and convolutional neural network (CNN)-based structures. In this paper, we propose a CNN-based target detection method in frequency modulation (FM)-band passive coherent location (PCL). We improved the detection performance using a maxpooling layer and a Hadamard division layer, which are parallelly placed with a CNN layer. Moreover, in our method there is no need to determine the specific cell configuration (e.g., cell under test, reference cells, and guard cells) because the proposed method obtains the trained kernels by end-to-end learning. We show that the trained kernels help in the extraction of either signal or noise components. Through the simulations, we also prove that the proposed method can yield an improved receiver operating characteristic compared to that of a cell-averaging CFAR detector for FM-band PCL in a homogeneous environment.

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&#x2014;regime of paramount importance in many practical applications&#x2014;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.

Non-Uniform Warping Sampling for Data Reduction in Planar Array Diagnostics

The problem of detecting defective turned-off elements in antenna arrays from near-field measurements is addressed. In particular, the focus here is to reduce the number of measurements in order to positively affect the acquisition time. Such an issue is achieved by adopting the recently developed warping sampling method. Two commonly antenna diagnostics methods, i.e, the Back Transformation Method (BTM) and the Matrix Method (MM), are considered in view of this new sampling strategy and compared to the usual half-wavelength sampling. In particular, in order to identify the fault locations, outcomes returned by BTM and MM undergo a detection step based on a cell-averaging CFAR (CA)-CFAR technique borrowed from the radar literature. It is shown that the warping sampling method provides performance close to the uniform half-wavelength one with a reduced number of data. Numerical simulations are carried out in order to verify the results with different fault layouts and tapered currents.

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.

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.

Weighted distance iteration matrix CFAR detector in Sea clutter

High-frequency surface wave radar (HFSWR) is an effective method for detecting and monitoring the targets on the ocean surface and above. To improve the detection performance of HFSWR, a new detector is proposed by applying weighted distance iteration (WDI) processing to the joint domain localised (JDL) matrix constant false alarm rate (CFAR) detector. Based on information geometry theory and Riemannian manifold, the proposed detector uses multi-dimensional information (azimuth, Doppler velocity and range information) of the signal to effectively find the target that has drowned in sea clutter and uses WDI-processing technique to suppress the clutter. The experimental results are given to show the superiority of the proposed detector. Compared with cell average CFAR detector and JDL matrix CFAR detector, the proposed detector improves the detection performance effectively.

Rank Sum Nonparametric CFAR Detector in Nonhomogeneous Background

The parametric CFAR detectors maintain a constant false alarm rate (CFAR) with an assumption of a known clutter distribution form. The representative ones of them are the cell-averaging (CA) CFAR, the greatest-of (GO) CFAR, and the ordered statistic (OS) CFAR, etc. In this work, we consider the rank sum (RS) nonparametric CFAR, which has an advantage that it can maintain CFAR even the distribution form of clutter becomes a different one. A closed form of the false alarm rate for the RS detector at clutter edges is derived, and a comparison of the performance of the RS detector in multiple targets situation and at clutter edges to that of the CA-CFAR, the GO-CFAR, and the OS-CFAR with noncoherent integration is made.