Clutter Map Constant False Alarm Rate Research Articles

An improved CFAR algorithm for multiple environmental conditions

The 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.

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.

Ideological and political theory teaching model based on artificial intelligence and improved machine learning algorithms

In the era of artificial intelligence, traditional teaching models can be replaced by intelligent teaching models, thereby effectively improving the efficiency of ideological and political teaching. This paper proposes a multi-frame sliding window double-threshold clutter map CFAR algorithm and analyzes its detection probability and false alarm probability formula. Moreover, the ideological and political teaching system based on artificial intelligence and improved machine learning is designed based on the B/S model. In addition, this article analyzes the practical teaching performance of the model combined with actual teaching and analyzes the teaching effect of the model in ideological and political education. Through experimental research, it can be seen that the performance of the experimental group is significantly higher than that of the control group, which verifies that the algorithm constructed in this article has a certain practical effect.

An Anti-FOD Method Based on CA-CM-CFAR for MMW Radar in Complex Clutter Background.

Traditional constant false alarm rate (CFAR) methods have shown their potential for foreign object debris (FOD) indication. However, the performance of these methods would deteriorate under the complex clutter background in airport scenes. This paper presents a threshold-improved approach based on the cell-averaging clutter-map (CA-CM-) CFAR and tests it on a millimeter-wave (MMW) radar system. Clutter cases are first classified with variability indexes (VIs). In homogeneous background, the threshold is calculated by the student-t-distributed test statistic; under the discontinuous clutter conditions, the threshold is modified according to current VI conditions, in order to address the performance decrease caused by extended clutter edges. Experimental results verify that the chosen targets can be indicated by the t-distributed threshold in homogeneous background. Moreover, effective detection of the obscured targets could also be achieved with significant detectability improvement at extended clutter edges.

Foreign object debris detection method based on fractional Fourier transform for millimeter-wave radar

Foreign object debris (FOD) denotes any unwanted objects on an airport runway, which must be removed before aircraft take off or land. Millimeter-wave (mm-wave) radar is widely utilized to locate small FODs due to its high-range resolution. However, the main difficulty faced by mm-wave radar is to detect stationary little FODs in heavy ground clutter. We propose a layered FOD detection algorithm using clutter map constant false alarm rate (CFAR) and fractional Fourier transform (FrFT) for a mm-wave radar system working at 77 GHz. In the first stage, we utilized the traditional clutter map CFAR to suppress ground clutter while FOD returns and some false alarms were detected using an adaptive threshold. Then, we propose an FrFT-based pattern classification method to distinguish FODs and false alarms, where a two-dimensional feature vector is extracted and a one-class minimax probability machine classifier is trained to accomplish FOD and false alarm classification. Finally, the effectiveness of the proposed method is verified using some measured data obtained via the 77-GHz mm-wave radar.

Distributed Clutter-Map Constant False Alarm Rate Detection Using Fuzzy Fusion Rules

The performance of distributed adaptive clutter map constant false alarm rate (CMAP-CFAR) detection system using fuzzy fusion rules with homogeneous and non-homogeneous background is considered in this paper. We assume that the sensors are identical and the target is fluctuating according to Swerling I model embedded in a white Gaussian noise with unknown variance. Each detector computes the value of the membership function to the false alarm space from the previous samples of the cell under test and transmits it to the fusion center. These values are combined according to fuzzy fusion rules to produce a global membership function to the false alarm space. The obtained results showed that the best performance was obtained while using the “algebraic product” fuzzy rule and the probability of detection increases significantly with the number of detectors.

Adaptive Clutter-Map CFAR detection in distributed sensor networks

In this paper, the problem of Clutter Map Constant False Alarm Rate (CMAP-CFAR) detection is considered for a distributed detection with two and three sensors and a data fusion centre. We assume that the sensors are identical and that the target is a fluctuating according to the Swerling I model, embedded in a white Gaussian noise of unknown variance. Closed form expressions of the global probabilities of detection and false alarm for the ‘AND’, ‘OR’ and ‘MAJORITY’ rules and the Adaptive Detection Threshold are determined and the performance of the system is investigated and analyzed.

CFAR Detection in the Framework of Time-Frequency Analysis

This paper deals with the resolution improvement of metric radars and proposes a new architecture that contains the constant false alarm rate (CFAR) technique conjointly with time-frequency analysis. The clutter map (CMAP) CFAR is used to detect the existence of targets; in this case, we search if there are one or more targets inside this cell using Gabor-based detector. Each target is considered to be fluctuating according to Swirling I model. The performance of the proposed system is evaluated and tested on simulated data using Monte-Carlo simulations under various clutter scenarios. In all cases, the obtained results show clearly that the new proposed detector outperforms the conventional OS-CFAR detector in terms of azimuth and range resolution.

Clutter-map constant false alarm rate detection for foreign object debris on runways

Heavy land clutter with antenna scan is the main interference for Foreign Object Debris( FOD) detection.However, traditional Constant False Alarm Rate( CFAR) in space-domain is ineffective to detect targets on runways. To solve this problem, a cell-average clutter-map CFAR was proposed. First of all, an echo model based on the characteristics of FOD surveillance radar system was built. Then, the range-bearing two-dimensional CFAR detection could be realized by using clutter-map cells dividing, cell averaging and recursive filtering. Further analysis of the main factors that affected the detection performance of this method was studied in the end. The simulation results show that, the proposed algorithm can effectively detect the weak target and obtain high detection probability with a low signal-to-clutter ratio.

Clutter map CFAR detector based on maximal resolution cell

In order to improve the detection performance of clutter map constant false alarm rate (CFAR) detectors in multiple persisting targets situation, a clutter map CFAR (CM-CFAR) detector based on the maximal resolution cell (CM/MRC-CFAR) is proposed in this paper. In the CM/MRC-CFAR detector, at each scan, a comparison threshold is computed by multiplying the amplitude of the maximal resolution cell (MRC) in the map cell by a scaling factor. Then, the number of the left resolution cells, whose amplitudes are smaller than the comparison threshold, is counted and compared with a threshold integer. Based on the comparison result, proper resolution cells are selected to update the detection threshold. The detection probability of CM/MRC-CFAR in both homogeneous and multiple persisting targets situations is derived in a closed-form expression. The detection performance of CM/MRC-CFAR is evaluated in various environments and compared with other CM-CFAR detectors. CM/MRC-CFAR exhibits a very low CFAR loss in a homogeneous environment and achieves a robust detection performance in multiple persisting targets situations. Since no ranking is required except searching for the MRC, the computation load of CM/MRC-CFAR is low, and it is easy to implement the detector in radar systems in practice.

A Bi-Parametric Clutter-Map CFAR Detection Method in Non-Gaussian Environment for Foreign Objects Debris on Runways

Time-varying land clutter is primary interference for Foreign Objects Debris (FOD) detection on airport runways. Traditional clutter-map CFAR (CM-CFAR) algorithms were ineffect-ive to detect targets in non-Gaussian clutter. In this paper, a Bi-parametric CM-CFAR (Bi-CM-CFAR) algorithm based on bi-parameters estimation is proposed. Clutter-level estimation is obtained with video integrator, parameters estimator and recursive filter; and updated in each scanning period. Moreover, simulations verify effectiveness of this method for FOD detection.

Analysis of the clutter map CFAR in Weibull clutter

Non-Rayleigh clutter statistics result when sea or ground clutter are viewed with resolution radars (pulse widh<0.5 μs) at low grazing angle ( ϕ<5°). In this paper, we consider the problem of Clutter Map Constant False Alarm Rate detection (CMAP-CFAR) in Weibull distribution with a shape parameter known. The target is assumed to be fluctuating according to Swerling I model. Closed-form expressions for the probabilities of detection and false alarm in terms of shape and scale parameters are determined and the performance of the system is investigated and analyzed.

Clutter-map CFAR detection for range-spread targets in non-Gaussian clutter. I. System design

The problem of constant false-alarm rate (CFAR) detection of possibly range-spread targets in clutter-dominated environment, whether Weibull or lognormal is presented. The proposed scheme is a member of the clutter-map CFAR family, which relies on a combination of space and time processing as well as on the relevant properties of the location-scale distributions; it ensures CFAR against noise with biparametric variations without incurring masking effects from slow, extended targets or multiple interferers. We also give guidelines for designing the system parameters, along with approximate formulas for setting the appropriate threshold multipliers to achieve a given false-alarm rate (FAR) under Weibull and lognormal clutter.

Clutter-map CFAR detection for range-spread targets in non-Gaussian clutter. II. Performance assessment

The performance assessment of the clutter-map constant false-alarm rate (CFAR) procedure presented in Part I [1] is introduced. At first, the performance in the presence of point targets is investigated, subject to both Weibull and lognormal clutter, so as to elicit the influence of the system parameters. The study essentially relies on Monte-Carlo simulations, but approximations for the detection rate are also developed. Admittedly, a global figure of performance, the Average Threshold Deviation (ATD), is introduced and its worth as a design tool is validated. Next, the effect of extended as well as multiple targets is considered, showing that the system can be designed so as to avoid self-masking from either range-spread targets persisting on the same map cell, or spurious targets in close spatial proximity to the cell under test. Finally, the system robustness is demonstrated through a sensitivity study, aimed at investigating the effect of a possible mismatch between the design and the actual clutter distribution, that of the clutter correlation and of a thermal noise component in the overall disturbance.

Performance Analysis of the Clutter Map CFAR Detector with Noncoherent Integration

Nitzberg has analyzed the detection performance of the clutter map constant false alarm rate (CFAR) detector using single pulse. In this paper, we extend the detection analysis to the clutter map CFAR detector that employs M-pulse noncoherent integration. Detection and false alarm probabilities for Swerling target models are derived. The analytical results show that the larger the number of integrated pulses M, the higher the detection probability. On the other hand, the analytical results for Swerling target models show that the detection performance of the completely decorrelated target signal is better than that of the completely correlated target.

Clutter Map CFAR Analysis

An analysis of the probability of target detection for a clutter map CFAR using digital exponential filtering has been performed. General performance equations are derived. The probability of detection versus signal-to-noise ratio is plotted for a false alarm probability of 1.E-06 for several weight values. The CFAR loss is plotted for a detection probability of 0.9 and false alarm probabilities of 1.E-06 and 1.E-08.