Constant False Alarm Rate Processor Research Articles

An improved CFAR algorithm for multiple environmental conditions

AbstractThe primary objective of radar digital signal processing is to detect and identify targets in complicated situations, such as those involving clutter or several closely positioned targets. The constant false alarm rate (CFAR) method is more effective for target recognition and has better control over the false alarm rate. Many studies have been conducted on the design of CFAR, but previous CFAR algorithms have not been effective in all or most environmental fields and target scenarios. In this study, an algorithm called Censored Mean Clutter Map CFAR (CM-CM CFAR) has been developed and tested for various environmental conditions. When compared to a fixed false alarm rate, the suggested CFAR algorithm’s Monte Carlo simulation results demonstrated a high detection probability in a variety of environments. This work designs a real-time CM-CM CFAR processor using field-programmable gate array (FPGA) technologies. Xilinx ARTIX 7 FPGA technology is used to develop and map a scalable parallel framework. Consequently, the implementation required 23,741 LUTs and 1825 FF. It is verified that the complexity and operating speed of the suggested CFAR processor are extremely appropriate for real-time implementation when compared to the results of the previously proposed FPGA implementation.

Improving SAR wind retrieval through automatic anomalous pixel detection

Satellite observations are increasingly in demand and valuable for monitoring the ocean and coastal winds in near-real-time and analyzing the data historically. However, soared up human activities, man-made constructions, and marine traffics negatively interfere and affect the reliability of observations and raise concerns about the trustworthiness of using satellite winds for many applications, such as offshore wind energy, even though Sentinel synthetic aperture radar (SAR) data is freely and easily accessible.In this paper, we presented a novel methodological framework for localization and suppression of anomalous pixels (APs), which deteriorate the confidence and accuracy of the retrieved SAR wind speeds. The backbone of the implementation relied on the constant false alarm rate (CFAR) algorithm, one of the automatic target recognition algorithms. We used a large dataset of SAR subscenes to model different sea clutter conditions after removing the potential targets. We selected the suitable distribution model that maintains a constant probability of false alarm (pfa) for the entire SAR scene. The proper pfa was optimized in various SAR scenes ranging from scenes with a considerable number of APs, such as ships and wind park clusters, to scenes with no visible APs under different wind speed conditions. The pfa optimization process prevented real wind pixels from being misidentified as APs.Applying CFAR to the logarithmic scale of raw digital numbers (log-DNs) domain with a pfa of about 3.17 × 10-5, we extracted SAR wind speeds around each ocean buoy before and after CFAR process, comparing them with their corresponding in situ wind measurements. This led to significant improvement in the quality and accuracy of SAR wind data retrieval, especially in regions with numerous man-made constructions and high marine traffics. We tested and validated our approach in the North Sea and two regions along the eastern coast of the U.S. The enclosed region, serving as a marine route for harbors in Delaware Bay, showed a substantial improvement in RMSE and BIAS after applying our approach, reducing RMSE from 2.4 m/s to 1.6 m/s and the BIAS from −1.5 m/s to −0.3 m/s. The open ocean region demonstrated stable RMSE and BIAS, indicating preservation of real wind data, as only APs affecting SAR wind speed were considered during the process.

FPGA Implementation of Efficient CFAR Algorithm for Radar Systems

The constant false-alarm rate (CFAR) algorithm is essential for detecting targets during radar signal processing. It has been improved to accurately detect targets, especially in nonhomogeneous environments, such as multitarget or clutter edge environments. For example, there are sort-based and variable index-based algorithms. However, these algorithms require large amounts of computation, making them difficult to apply in radar applications that require real-time target detection. We propose a new CFAR algorithm that determines the environment of a received signal through a new decision criterion and applies the optimal CFAR algorithms such as the modified variable index (MVI) and automatic censored cell averaging-based ordered data variability (ACCA-ODV). The Monte Carlo simulation results of the proposed CFAR algorithm showed a high detection probability of 93.8% in homogeneous and nonhomogeneous environments based on an SNR of 25 dB. In addition, this paper presents the hardware design, field-programmable gate array (FPGA)-based implementation, and verification results for the practical application of the proposed algorithm. We reduced the hardware complexity by time-sharing sum and square operations and by replacing division operations with multiplication operations when calculating decision parameters. We also developed a low-complexity and high-speed sorter architecture that performs sorting for the partial data in leading and lagging windows. As a result, the implementation used 8260 LUTs and 3823 registers and took 0.6 μs to operate. Compared with the previously proposed FPGA implementation results, it is confirmed that the complexity and operation speed of the proposed CFAR processor are very suitable for real-time implementation.

In Vivo Quantitative Ultrasound on Dermis and Hypodermis for Classifying Lymphedema Severity in Humans.

This study investigated the ability of in vivo quantitative ultrasound (QUS) assessment to evaluate lymphedema severity compared with the gold standard method, the International Society of Lymphology (ISL) stage. Ultrasonic measurements were made around the middle thigh (n=150). Radiofrequency data were acquired using a clinical scanner and 8-MHz linear probe. Envelope statistical analysis was performed using constant false alarm rate processing and homodyned K (HK) distribution. The attenuation coefficient was calculated using the spectral log-difference technique. The backscatter coefficient (BSC) was obtained by the reference phantom method with attenuation compensation according to the attenuation coefficients in the dermis and hypodermis, and then effective scatterer diameter (ESD) and effective acoustic concentration (EAC) were estimated with a Gaussian model. Receiver operating characteristic curves of QUS parameters were obtained using a linear regression model. A single QUS parameter with high area under the curve (AUC) differed between the dermis (ESD and EAC) and hypodermis (HK) parameters. The combinations with ESD and EAC in the dermis, HK parameters in the hypodermis and typical features (dermal thickness and echogenic regions in the hypodermis) improved classification performance between ISL stages 0 and ≥I (AUC=0.90 with sensitivity of 75% and specificity of 91%) in comparison with ESD and EAC in the dermis (AUC=0.82) and HK parameters in the hypodermis (AUC=0.82). In vivo QUS assessment by BSC and envelope statistical analyses can be valuable for non-invasively classifying an extremely early stage of lymphedema, such as ISL stage I, and following its progression.

Multichannel adaptive signal detection: basic theory and literature review

Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks.

Target length estimation based on quadratic statistical CFAR processing

Estimating the radial size of targets using radar high resolution range profile (HRRP) can provide important feature criteria for target classification and recognition. In practice, the bi-directional constant false alarm rate (CFAR) threshold method is often used to discriminate the signal support area and noise area of targets. Under the condition of low signal-to-noise ratio, the size estimation accuracy of existing methods is low, and it is easy to miss detection when the edge signal of target support area is weak. In order to solve the above problems, this paper judges the target-noise boundary by multiple detections with low threshold, and further improves the size estimation accuracy by edge optimization after searching the target-noise boundary. Experimental verification and comparative analysis show that the average estimation error of this method is less than 10%, which is significantly lower than the existing CFAR threshold size estimation method, and can effectively avoid missing detection of edge weaknesses and strong noise interference.

Design and implementation of proposed pipelined adaptive recovery CAMP algorithm for LFMCW radar

Recently, Compressive Sensing (CS) theory based on the traditional CAMP reconstruction algorithm has applied in radar systems to achieve the benefits of CS such as low sampling rate, small memory size, less complexity in hardware and consequently reduces the required processing time as using a low speed Analog-to-Digital Converter. A modified reconstruction Complex Approximate Massage Passing (CAMP) algorithm designed as an adaptive recovery algorithm is introduced. The adaptive recovery algorithm depends on selecting the comparison threshold in an adaptive manner resembling the Constant False Alarm Rate processing. In this paper, design and implementation of the adaptive algorithm for Linear Frequency Modulated Continuous Wave radar signals are achieved using Field Programmable Gate Array (FPGA). A proposed pipelined process-based scheme is used in the implementation that leads to a more redundant in complexity and processing time. A generic methodology based on segmenting the acquired radar signal and applying the adaptive recovery algorithm on each segment is proposed. This method enables the investigation of the adaptive recovery algorithm on any signal length with fixed hardware resources. A suggested processing method is implemented called pipelined adaptive recovery CAMP algorithm, which enhances the performance of reconstructing the radar signal better than the traditional CAMP algorithm. Hardware implementation using FPGA and experiments are met, and their results agree very well with the simulation outcomes.

Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments.

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

Application of Beamforming Technology in Ionospheric Oblique Backscatter Sounding with a Miniaturized L-Array

In this paper, a new dedicated multi-channel ionospheric oblique backscatter sounder is described. A miniaturized L-shaped antenna array was employed for the receiving of the oblique backscatter echoes in the present system. Firstly, two typical adaptive beamforming algorithms were introduced to improve the anti-jamming ability. Then, simulations were carried out to verify the beamforming performance in azimuth and elevation simultaneously. Furthermore, the experimental results by the present sounding system were used to test the performance of the adaptive beamformers. Results show that the radio frequency interference and the interference of the vertical echoes can be effectively suppressed by the adaptive beamformers. In particular, the use value of the beamforming in the receiving of the ionospheric oblique backscatter sounding is described in detail through the analysis of the signal sequences in several typical frequencies. And after the constant false-alarm rate processing, the oblique backscatter ionograms processed by the adaptive beamforers have clearer and continuous leading edge compared with the original ionograms. As a result, the adaptive beamformers of great significance to improve the detection ability of the ionospheric oblique backscatter sounding.

Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning

For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

Dual‐station radar‐based living body detection and localisation

In this study, a dual-station radar signal processing algorithm based on vital signal correlation is presented to deal with the problem of detection and localisation of living body with low echo power in the multi-target scenario. The algorithm is mainly divided into the following steps. First, a cross-correlation operation is performed on the echo signal matrices, respectively, received by two radars at different positions to obtain the correlation matrix. Then constant false alarm rate processing is used to detect the targets and extract the distance of every target relative to the two radars. Finally, the position of the target is calculated by the triangulation algorithm to achieve the localisation of living bodies. The simulation result of MATLAB software shows that this algorithm can effectively detect and locate multiple targets with different echo intensities.

Pietra index based processor for a heterogeneous Pareto background

This study addresses the problem of automatic target detection in a heterogeneous Pareto background. To achieve this, the Pietra index based and constant false alarm rate processor (CFAR) is conceived. Specifically, assuming a non-stationary Pareto background with the presence or not of any clutter edge or interfering targets, the Pietra index and the log geometric mean ratio statistic tests are concomitantly used to allow the proposed processor to switch dynamically to the appropriate detector; i.e. the geometric mean-CFAR, the greatest of-CFAR or the trimmed mean-CFAR, where all of these detectors assume a priori unknown scale parameter. That is, according to the outcomes of the Window Selection Probability, the background level is systematically estimated through the preselected detector. The detection performances of the proposed processor are assessed, via Monte Carlo simulations, in multiple target and clutter edge situations.

Research on a New Comprehensive CFAR (Comp-CFAR) Processing Method

Since the clutter statistics of marine radar are non-stationary and difficult to ascertain, the constant false-alarm rate (CFAR) processor based on some clutter statistical characteristics is hard to obtain the CFAR performance. Especially, for clutter with long smearing effect characteristics, such as lognormal distribution, Pareto distribution, and K distribution, it is difficult to obtain CFAR characteristics using conventional CFAR processing techniques. The main consideration of this paper is to improve the robustness of CFAR, and the Comp-CFAR method is proposed according to the central limit theorem and the logarithmic compression principle of the signal. This method mainly includes clutter two-parameter logarithmic compression processing and accumulation of the magnitudes' average comprehensive CFAR processing. The experimental verification of CFAR characteristics and target detection performance with CFAR in four typical clutter environments shows that this method has better detection ability compared with the NCI-CFAR.

Ship target tracking based on a low-resolution optical satellite in geostationary orbit

ABSTRACTBased on the low-resolution geostationary optical satellite for maritime surveillance, a complete processing algorithm including ship detection and ship tracking is proposed. Potentially ships are first detected by a constant false alarm rate processing and a morphological filter from the area of water. Image coordinates of ships are then corrected and transformed by using rational polynomial coefficients and high-resolution shoreline database. In the ship-tracking procedure, a convenient motion model based on geographical coordinates is established, and the multiple hypothesis tracking method is used to track ships for further removing false targets and getting motion states of targets ultimately. Our algorithm has been tested using GaoFen-4 (GF-4) satellite image sequences and the automatic identification system messages of ships. The results of the experiment show that the proposed algorithm can effectively detect and track ships in complex scenes and estimate ships’ motion information accurately with the help of a low-resolution geostationary optical satellite.

Data-Dependent Clustering-CFAR Detector in Heterogeneous Environment

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection.

Detection Capability and CFAR Loss Under Fluctuating Targets of Different Swerling Model for Various Gamma Parameters in RADAR

Target detection of RADAR deals with different and manifold problems over few decades. The detection capability is one of the most significant factors in RADAR system. The main aim of detection is to increase probability of detection while decreasing rate of false alarm. The threshold of detection is modified as a function of the receiver noise level to keep a fixed rate of false alarm. Constant False Alarm Rate (CFAR) processors are used to maintain the amount of false alarm under supervision in a diverse background of interference. In Signal to Noise Ratio (SNR) level, a loss can be occurred due to CFAR processor. Gamma function is used to determine the probability of false alarm. It is assumed in adaptive CFAR that the interference distribution is familiar here. This type of CFAR also approximates the unknown parameters connected with various interference distributions. CFAR loss depends on gamma function. Incomplete gamma function plays an important role in maintaining threshold voltage as well as probability of detection. Changing the value of gamma function can improve the probability of detection for various Swerling Models which are proposed here. This paper has also proposed a technique to compare various losses due to CFAR in terms of different gamma function in presence of different number of pulses for four Swerling Models.

АДАПТИВНОЕ ОБНАРУЖЕНИЕ СТАЦИОНАРНОГО ГАУССОВОГО СИГНАЛА НА ФОНЕ НОРМАЛЬНОГО ШУМА С ПОСТОЯННЫМ УРОВНЕМ ЛОЖНОЙ ТРЕВОГИ

PACS number: 84.40.Xb Purpose: Efficiency analysis of the Cell-Averaging Constant False Alarm Rate processor (CA CFAR-processor) as applied to detection of stationary Gaussian signals against a normal noise background with unknown and/or varying from scan to scan power. Design/methodology/approach: Standard methods of the theory of optimal filtration and statistical signal processing are used to calculate the true detection probability and false-alarm rate. Findings: Analytical expressions have been derived for the scaling factor which ensures a constant level of the false-alarm rate, as well as for the true detection probability in dependence on the number of the reference cells and signal-to-noise ratio. It is shown that for efficient application of the given algorithm, the number of the reference cells should be 20 to 30, depending on the signalto-noise ratio μ. In this case, the amount of loss in the signal-tonoise ratio does not exceed 1 to 2 dB as compared with the situation where the noise power is a priori known and invariable. With μ≥30 dB the amount of loss proves to be negligibly small and the need in adaptation vanishes. Conclusions: The results obtained testify to the efficiency of application of the CA CFAR processors to detection of targets corresponding to Swerling model 1 against a normal noise background with unknown power associated with clutter and/or scattering from irregularities of the propagation medium. Key words: target detection, false alarm rate, detection threshold, signal-to-noise ratio, cell averaging Manuscript submitted 26.06.2017 Radio phys. radio astron. 2017, 22(3): 231-237 REFERENCES 1. LEVIN, B. R., 1968. Theoretical fundamentals of statistical radio engineering, Volume 2. Moscow, USSR: Sov. Radio Publ. (in Russian). 2. KAY, S. M., 1998. Fundamentals of Statistical Signal Processing , Vol. II: Detection Theory. New Jersey: Prentice Hall. 3. BARTON, D. K., 1998. Modern Radar System Analysis . Boston, London: Artech House Books. 4. SKOLNIK, M. I., 2008. Radar Handbook . New York et al.: McGraw Hill Professional. 5. BAKULEV, P. A., BASISTOV, Y. A. and TUGUSHI, V. G., 1989. Signal processing with a constant false alarm rate. Izv. Vyssh. Uchebn. Zaved. Radioelektronika . vol. 32, no. 4, pp. 4 15 (in Russian). 6. HAYKIN, S., 2007. Adaptive Radar Signal Processing . New Jersey: John Wiley & Sons Inc. 7. EL MASHADE, M. B., 2014. Performance enhancement of conventional CFAR processors in ideal and multitarget environments. Radioelectron. Commun. Syst . vol. 57, is. 7, pp. 287 305. DOI: https://doi.org/10.3103/S0735272714070012 8. LONG CAI, XIAOCHUAN MA, QI XU, BIN LI and SHIWEI REN, 2011. Performance Analysis of Some New CFAR Detectors under Clutter. Journal of Computers . vol. 6, no. 6, pp. 1278 1285. DOI: https://doi.org/10.4304/jcp.6.6.1278-1285 9. FINN, H. M. and JOHNSON, R. S., 1968. Adaptive detection mode with threshold control as a function of spatially sampled clutter level estimates. RCA Rev . vol. 29, no. 3, pp. 414 464. 10. SWERLING, P., 1960. Probability of detection for fluctuating targets. IRE Trans. Inf. Theory. vol. 6, is. 2, pp. 269 308. DOI: https://doi.org/10.1109/TIT.1960.1057561 11. SIDOROV, Y. V., FEDORYUK, M. V. and SHABUNIN, M. I., 1989. Lectures on the theory of functions of complex variable . Moscow, USSR: Nauka Publ. (in Russian).

Fuzzy statistical normalization CFAR detector for non-rayleigh data

A new constant false-alarm rate (CFAR) detector for non-Rayleigh data, based on fuzzy statistical normalization, is proposed. The proposed detector carries out the detection with two stages. The first stage of the fuzzy statistical normalization CFAR processor is background level estimation, based on fuzzy statistical normalization. The second stage is signal detection, based on the original data and the defuzzification normalized data. Performance comparisons are carried out to validate the superiority of the proposed CFAR detector.

Maneuvering target tracking from nautical radar images using particle-Kalman filters

In this paper, a new combined particle-Kalman filter (PF-KF)-based visual tracking approach is designed for maneuvering target tracking from X-band nautical radar images. Unlike existing target tracking approaches used by nautical radar, this approach incorporates a histogram-based visual tracking strategy to estimate the target position and velocity. It applies a sampling importance resampling (SIR) particle filter to obtain preliminary target positions, and then a Kalman filter to derive refined target position and velocity. A Bhattacharyya coefficient-based similarity function is employed to compare the reference target and candidate target models, which are constructed by a kernel-based histogram in radar images. An enhanced reference target model construction method that employs constant false alarm rate (CFAR) processing to enable automatic determination of reference region is proposed to improve the tracking stability and accuracy. Comparison of the target information obtained by the proposed PF-KF method from various field X-band nautical radar image sequences with those measured by GPS shows the proposed approach can provide a reliable and flexible online target tracking for nautical radar application. It is also shown that, in the scenario of strong sea clutter, the proposed approach outperforms the PF-only-based approach and the classical tracking approach which combines order-statistics (OS) CFAR processing and the Kalman filter.

Two Types of Distributed CFAR Detection Based on Weighting Functions in Fusion Center for Weibull Clutter

Two types of distributed constant false alarm rate (CFAR) detection using binary and fuzzy weighting functions in fusion center are developed. In the two types of distributed detectors, it was assumed that the clutter parameters at the local sensors are unknown and each local detector performs CFAR processing based on ML and OS CFAR processors before transmitting data to the fusion center. At the fusion center, received data is weighted either by a binary or a fuzzy weighting functions and combined according to deterministic rules, constructing global test statistics. Moreover, for the Weibull clutter, the expression of the weighting functions, based on ML and OS CFAR processors in local detectors, is obtained. In the binary type, we analyzed various distributed detection schemes based on maximum, minimum, and summation rules in fusion center. In the fuzzy type, we consider the various distributed detectors based on algebraic product, algebraic sum, probabilistic OR, and Lukasiewicz t-conorm fuzzy rules in fusion center. The performance of the two types of distributed detectors is analyzed and compared in the homogenous and nonhomogenous situations, multiple targets, or clutter edge. The simulation results indicate the superiority and robust performance of fuzzy type in homogenous and non homogenous situations.