CFAR Loss Research Articles

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.

An Improved VI-CFAR Detector Based on GOS

In combination with the advantages of CA-CFAR, GO-CFAR and SO-CFAR algorithm, the VI-CFAR has strong adaptability both in homogeneous and non-homogeneous environment. However, if the interfering targets are present in both the halves of the reference sliding windows, the use of the window with the smallest mean is affected by them and therefore results in a performance degradation. In order to overcome the shortcoming, an improved VI-CFAR detector based on GOS (IVI-CFAR) is proposed in this paper. We introduce the IVI-CFAR detector and make performance simulation and analysis in homogenous and non-homogenous environment. In the homogeneous environment, the IVI-CFAR detector has some CFAR loss relative to the CA-CFAR detector. In the clutter edge environment, the IVI-CFAR detector keeps the good performance of the VI-CFAR detector. In multiple interfering targets environment, the IVI-CFAR detector performs robustly, which is similar to the OS-CFAR detector. In addition, the IVI-CFAR detector shortens the sample sorting time of the OS-CFARR detector.

효율적인 CFAR 알고리듬 연구

Abstract This paper proposes a new efficient CFAR algorithm. The structure of the proposed CFAR is relatively simple as compared with the OS-CFAR or ML-CFAR which are considered to deal with the nonhomogeneous environment such as clutter and multiple targets. The proposed algorithm is effectively applied to the radar signal processor with reduced computation burden. The relationship between the threshold and PFA of the proposed CFAR is derived analytically. The CFAR loss of the proposed CFAR algorithm is compared with CA-CFAR and OS-CFAR based on both SNR and ADT(Average Detection Threshold). Key words: CFAR, Threshold, PFA hISijCkl*(mhcnLMo6p)(Technical Support Center for National Defense Industry of Agency for Defense Development)qManuscript received April 7, 2014 ; Revised August 14, 2014 ; Accepted August 18, 2014. (ID No. 20140407-036)qCorresponding Author: Sang-Jin Shin (e-mail: radarsjs@add.re.kr) r. 서 론  Zs6 )š

Improved switching CFAR detector for non-homogeneous environments

In order to mitigate the excessive false alarm rate of the switching constant false alarm rate (S-CFAR) detector at the clutter edge, an improved switching CFAR (IS-CFAR) detector is proposed in this paper. In IS-CFAR, a comparison threshold is generated by multiplying the amplitude of the test cell by a scaling factor. Then in the leading and lagging reference windows, the numbers of reference cells whose amplitudes are smaller than the comparison threshold are countered and compared with a threshold integer. Based on the comparison result, the detection threshold is generated by selecting appropriate reference cells for background noise/clutter power estimation. The detection probability of IS-CFAR in various environments is derived in a closed-form expression. The performance is evaluated and compared with other CFAR detectors. It is shown that IS-CFAR exhibits a low CFAR loss in a homogeneous environment and almost the same detection performance as S-CFAR in a multiple targets situation; at a clutter edge, the false alarm rate of IS-CFAR is much lower than that of S-CFAR and approximates to the greatest-of CFAR (GO-CFAR). Experimental results from a linear frequency modulated continuous wave (LFMCW) radar system are given to demonstrate the efficiency of IS-CFAR.

A new CFAR detector with greatest option

This paper presents a new CFAR detector based on Ordered Statistics (OS) and Cell-Averaging (CA) forming local estimates, and using Greatest-Of selection (GO) to form clutter power level estimate Z in test cell(OSCAGO). Under the Swerling II assumption, the analytic expressions of Pfa, Pd and ADT of this detector are derived, its detection performance in homogeneous background and in strong interfering targets environment are analyzed and compared it with OS, GOSGO detectors. The results show that the detection performance of OSCAGO in homogeneous background and in multiple-target situations are obviously better than those of OS and GOSGO. When the number of interfering targets is equal to certain value, the CFAR loss of OSCAGO is about 3dB less than that of GOSGO.

Cell-averaging CFAR gain in spatially correlated K-distributed clutter

The detection performance of a cell-averaging CFAR detector is analysed for spatially correlated K-distributed clutter. In thermal noise or uniform clutter, the performance of the cell-averaging CFAR can be compared with that of the ideal fixed threshold, but with an associated ‘CFAR loss’, quantified by the increase in average signal-to-clutter ratio required to achieve a given probability of detection and probability of false alarm. In some types of clutter, knowledge of the overall amplitude distribution alone is not sufficient to assess the performance of a cell-averaging CFAR. In particular, sea clutter may exhibit significant spatial correlation, often associated with the sea swell. In such spatially varying clutter, the cell-averaging CFAR may be able to follow the local fluctuations, giving a considerable improvement in performance compared to the ‘ideal’ fixed threshold. This is quantified as a ‘CFAR gain’ with the limit of such improvement given by the performance of the ‘ideal CFAR’ detector. The CFAR gain or loss is illustrated in different clutter conditions for a range of cell-averager configurations. It is shown that significant CFAR gain, compared with the commonly accepted CFAR loss, can be achieved in some circumstances.

Hybrid clutter-map/L-CFAR procedure for clutter rejection in nonhomogeneous environment

The author introduces a new CFAR procedure, which relies on a hybrid clutter-map/L-CFAR strategy, aimed at improving the system robustness against possible nonhomegeneities, while preserving target detectability in a homogeneous environment. After presenting the procedure, guidelines for the design of the relevant system subblocks are given, from both quantitative and qualitative points of view. As to the performance analysis, closed-form formulas are given for the detection and false alarm probabilties in case of a single point-like target, showing that proper system design yields near-optimum performance. Moreover, approximated expressions of the detection probability are derived for the case of multiple targets entering the map cell, and possibly persisting in there for several scans: it is shown that data censoring prevents the masking effect which is typical of clutter-map systems, at the price of a very limited incremental CFAR loss.

Maximum-likelihood CFAR for Weibull background

A low-loss CFAR algorithm for Weibull background is discussed. The two parameters (shape and scale) of the background statistics are estimated using a maximum-likelihood algorithm. A CFAR threshold based on parameters estimated in this way exhibits a smaller variance, and hence a smaller CFAR loss, than thresholds based on other estimation algorithms such as moments or order statistics. The analysis covers both complete and censored background sample sets.

Order statistcs CFAR for Weibull background

The paper shows how to adopt Rohling's order statistics CFAR algorithm, developed for a Rayleigh background, to the case of a Weibull background with a known shape parameter. When the shape parameter is unknown, Weber and Haykin have proposed a two-parameter algorithm in which the threshold is obtained from two ranked background samples. In the paper the CFAR loss of the Weber-Haykin CFAR algorithm is analysed and compared with another CFAR processor for Weibull clutter suggested by Hansen. It is found that, with the optimal choice of representative ranks, the Weber-Haykin CFAR processor yields a smaller, but still very excessive, detection loss. Finally it is shown that CFAR is maintained when more than two ranked samples are used in setting the threshold, but without improvement in performance.

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.