Probability of detection for fluctuating targets

Swerling, Peter

doi:10.1109/tit.1960.1057561

Cited by 552 publications

(231 citation statements)

References 0 publications

Supporting

Mentioning

223

Contrasting

Unclassified

Order By: Relevance

“…We test each Doppler bin to within the radar's minimum detectable velocity (MDV), which is set to −15 dB SINR loss for the simulations [15]. We seed 0 dB Swerling 1 targets into the data [16]. The detection statistic is calculated by applying the Extended Factored Algorithm (EFA) for dimension-reduction [1,2,17], followed by an adaptive matched filter (AMF) normalization [18].…”

Section: Numerical Analysismentioning

confidence: 99%

Knowledge-Aided Covariance Matrix Estimation in Spiky Radar Clutter Environments

2017

View full text Add to dashboard Cite

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in clutter-limited environments. Effective STAP implementation is dependent on accurate space-time covariance matrix estimation. Heterogeneous clutter, including spiky, spatial clutter variation, violates underlying STAP training assumptions and can significantly degrade corresponding detection performance. This paper develops a spiky, space-time clutter model based on the K-distribution, assesses the resulting impact on STAP performance using traditional methods, and then proposes and evaluates the utility of the knowledge-aided parametric covariance matrix estimation (KAPE) method, a model-based scheme that rapidly converges to better represent spatial variation in clutter properties. Via numerical simulation of an airborne radar scenario operating in a spiky clutter environment, we find substantial improvement in probability of detection (P D ) for a fixed probability of false alarm (P FA ) for the KAPE method. For example, in the spiky clutter environment considered herein, results indicate a P D of 32% for traditional STAP and in excess of 90% for KAPE at a P FA of 1E-4, with a corresponding difference of 11.5 dB in threshold observed from exceedance analysis. The proposed K-distributed spiky clutter model, and application and assessment of KAPE as an ameliorating STAP technique, contribute to an improved understanding of radar detection in complex clutter environments.

show abstract

Section: Numerical Analysismentioning

confidence: 99%

Knowledge-Aided Covariance Matrix Estimation in Spiky Radar Clutter Environments

2017

View full text Add to dashboard Cite

show abstract

“…Amplitude scintillation and its effects on target detection have been extensively treated from a statistical point of view. Marcum [9] developed the theory of detection probabilities of constant cross section targets in additive white noise for search type radars, considering the effects of pulse integration, scanning effects, etc, Swerling [10,11] advanced the theory by considering fluctuating targets. The four cases originally considered, which encompassed most targets, are as follows:…”

Section: Review Of Past Workmentioning

confidence: 99%

On the Statistical Modeling of Radar Targets

Wright¹,

Haddad²

1972

View full text Add to dashboard Cite

“…This type of noise represents the aggregate radar return from a collection of many small scatterers, e.g., ground return, sea return, reflection from rain, chaff, and decay clouds. Detection and estimation in a clutter environment is a major problem in modern radar [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…This change may be a slow variation and occur on a scanto-scan basis (on successive antenna scans across a target) or it may be on a pulse-to-pulse basis (on successive sweeps). Because the exact nature of the change is difficult to predict, a statistical description is often adopted to characterize the target radar cross section [1,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Cfar Detection of Fluctuating Targets

Mashade¹

2008

PIER C

View full text Add to dashboard Cite

Abstract-Our scope in this paper is to provide a complete analysis of CFAR detection of fluctuating targets when the radar receiver incoherently integrates M returned pulses from a chi-squared fluctuating targets with two and four degrees of freedom and operates in a multitarget environment. Since the Swerling models of fluctuating targets represent a large number of such type of radar targets, we restrict our attention here to this interesting class of fluctuation models. There are four categories of such representation; namely SWI, SWII, SWIII, and SWIV. SWI and SWIII represent scanto-scan fluctuating targets, while SWII and SWIV represent fast pulse-to-pulse fluctuation. Exact expressions are derived for the probability of detection of all of these models. A simple and an effective procedure for calculating the detection performance of both fixed-threshold and adaptive-threshold algorithms is obtained. The backbone of this procedure is the ω-domain representation of the cumulative distribution function of the test statistic of the processor under consideration. In the CFAR case, the estimation of the noise power levels from the leading and the trailing reference windows is based on the OS technique. The performance of this detector is analyzed in the case where the operating environment is ideal and where it includes some of extraneous targets along with the target under test. The primary and the secondary outlying targets are assumed to be fluctuating in accordance with the four Swerling's models cited above. The numerical results show that, for large SNR, the processor detection performance is highest in the case of SWIV model while it attains its minimum level of detection in the case of SWI model. Moreover, SWII model has higher performance than the SWIII representation of fluctuating targets. For low SNR, on the other hand, the reverse of this behavior is occurred. This observation is common either for fixed-threshold or for adaptive-threshold algorithm. El Mashade

show abstract

Probability of detection for fluctuating targets

Cited by 552 publications

References 0 publications

Knowledge-Aided Covariance Matrix Estimation in Spiky Radar Clutter Environments

Knowledge-Aided Covariance Matrix Estimation in Spiky Radar Clutter Environments

On the Statistical Modeling of Radar Targets

Analysis of Cfar Detection of Fluctuating Targets

Contact Info

Product

Resources

About