Constant False Alarm Rate Behavior Research Articles

Adaptive Radar Detection in Low-Rank Heterogeneous Clutter via Invariance Theory

This paper addresses adaptive detection of a range distributed target in the presence of dominant heterogeneous clutter, which is (possibly) low-rank and lies in a known subspace, plus Gaussian thermal noise. First, this problem is transformed into an equivalent binary hypothesis test with observations having block-diagonal covariance matrices. Then an invariance analysis is conducted on the resulting hypothesis test. Data and unknown parameters are compressed into a maximal invariant and an induced maximal invariant, respectively, w.r.t. a suitable transformation group. This suggests to focus attention on invariant detectors and to establish the relationship between invariance and constant false alarm rate (CFAR) property. According to this guideline, two tunable invariant detectors exploiting the aforementioned covariance structure are devised, and they are shown to ensure bounded CFAR and standard CFAR properties, respectively. Finally, the CFAR behavior of the proposed detectors as well as their detection performance is assessed via numerical simulations.

Impropriety-Based Multiantenna Spectrum Sensing With I/Q Imbalanced Radios

Direct conversion radios are widely recognized as the most appealing approach for reducing the hardware cost as well as power consumption in upcoming communication systems. However, such radios are known to entail gain and phase uncertainties along the analog inphase/quadrature (I/Q) paths. In this paper, we address the effects of the transmitter (TX) and receiver (RX) I/Q errors or mismatches on the improperness of the transmitted and received signals, respectively. We analytically show how the properness of a transmitted signal and the receiver thermal noise can be destroyed, respectively, under the transmitter and receiver I/Q errors, given that the corresponding ideal signals are proper under perfect I/Q balance. Then, we address the spectrum sensing problem in cognitive radio systems through modeling it as a composite binary hypothesis testing task, and apply the likelihood ratio test approach to solve it. To this end, we propose three impropriety-based multiantenna spectrum sensing algorithms under the transmitter and receiver I/Q uncertainties. The principle of invariance is exploited to examine the potential constant false alarm rate (CFAR) behavior of the proposed detectors. We analytically prove that all the proposed sensing methods possess CFAR behavior against the noise variance uncertainty, while only two of them have CFAR property against the receiver I/Q mismatch values. The achievable sensing performance of the proposed methods is then analyzed through extensive numerical experiments, and the devised alternative detectors are mutually compared. Finally, analytical solutions are derived to quantify the improvement/degradation in the effective received signal SNR under I/Q imbalanced radios.

Radar Detection Architecture Based on Interference Covariance Structure Classification

This paper proposes a new radar detection architecture, which is composed of an interference covariance structure classifier and a bank of adaptive radar detectors. The classifier is based on model order selection theory and the Bayesian information criterion to determine the covariance structure that is deemed to be suitable for a specific set of radar data [1] . This decision stage drives the choice of the radar detector within a specific class of adaptive detectors to establish the possible target presence. The critical issue concerning the constant false alarm rate behavior of the architecture is discussed and two techniques for the threshold setting process are proposed. Finally, the detection performance analysis, conducted on both simulated and measured data, shows that the proposed architecture can guarantee better performance than classic radar decision schemes in scenarios where the interference covariance exhibits structural properties.

Compressive cyclostationary spectrum sensing with a constant false alarm rate

Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. To that end, a number of different spectrum sensing method have been developed in the literature. Cyclostationary detection is a particular sensing approach that takes use of the built-in periodicities characteristic to most man-made signals. It offers a compromise between achievable performance and the amount of prior information needed. However, it often requires a significant amount of data in order to provide a reliable estimate of the cyclic autocorrelation (CA) function. In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing. Particularly, we propose two compressive spectrum sensing algorithms that exploit further prior information on the CA structure. In the first one, we make use of the joint sparsity of the CA vectors with regard to the time delay, while in the second one, we introduce structure dictionary to enhance the reconstruction performance. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature.

Knowledge-based adaptive polarimetric detection in heterogeneous clutter

The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based (KB) adaptive polarimetric detector in heterogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.

Covariance matrix estimation via geometric barycenters and its application to radar training data selection

This study deals with the problem of covariance matrix estimation for radar signal processing applications. The authors propose and analyse a class of estimators that do not require any knowledge about the probability distribution of the sample support and exploit the characteristics of the positive-definite matrix space. Any estimator of the class is associated with a suitable distance in the considered space and is defined as the geometric barycenter of some basic covariance matrix estimates obtained from the available secondary data set. Then, the authors introduce an adaptive detection structure, exploiting the new covariance matrix estimators, based on two stages. The former consists of a data selector screening among the training data, whereas the latter is a conventional adaptive matched filter taking the final decision about the target presence. At the analysis stage, the authors assess the performance of the proposed two-stage scheme in terms of probability of correct outliers excision, constant false alarm rate behaviour and detection probability. The analysis is conducted both on simulated data and on the challenging KASSPER datacube.

On the CFAR Property of the RX Algorithm in the Presence of Signal-Dependent Noise in Hyperspectral Images

In this paper, we investigate the constant false-alarm rate (CFAR) property of the RX anomaly detector which is widely used for the analysis of hyperspectral data. The RX detector relies on an adaptive scheme where the mean vector and the covariance matrix of the background are locally estimated from the image pixels themselves. First, demeaning is accomplished by removing the estimated local background mean value, and then, the covariance matrix is estimated in a homogeneous neighborhood of each pixel. In principle, if the local mean is perfectly removed and the covariance matrix is estimated from background pixels sharing the same covariance matrix, the RX algorithm has the CFAR property, which is highly desirable in practical applications. The CFAR behavior of the algorithm also requires the spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new-generation sensors, such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal level, is not negligible. This has motivated us to analyze the behavior of the RX algorithm with respect to the CFAR property in data affected by signal-dependent (SD) noise. In this paper, we show both theoretically and experimentally that the SD noise is one of the causes of the non-CFAR behavior of the RX detector that we have experienced in many practical situations. We propose a strategy to enhance the robustness of the anomaly detection scheme with respect to the CFAR property based on an adaptive nonlinear transform aimed at reducing the dependence of the noise on the signal level. Experiments on simulated data and real data collected by a new hyperspectral camera are also presented and discussed.

Performance of the adaptive generalised matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis

The authors deal with the performance analysis of an adaptive version of the generalised matched subspace detector (GMSD) in compound-Gaussian clutter with unknown covariance matrix. The original GMSD was proposed to detect subspace signals in compound-Gaussian noise with known covariance matrix and ensures the constant false alarm rate (CFAR) property. In real situations, this assumption is unrealistic, which means that the covariance matrix must be estimated from training data. The authors use a robust estimate of the covariance matrix called the fixed-point estimate, recently proposed in the literature. The performance of the obtained adaptive detector, in terms of CFAR behaviour and probability of detection, is evaluated in the presence of real sea clutter data, collected by the McMaster IPIX radar.

Rao Test for Adaptive Detection in Gaussian Interference With Unknown Covariance Matrix

This paper deals with the problem of detecting a signal known up to a scaling factor in the presence of Gaussian disturbance with unknown covariance matrix. A new detector based on the Rao test criterion is introduced and its invariance properties and constant false alarm rate (CFAR) behavior are studied. At the analysis stage, the performance of the new receiver is assessed, also in comparison with some classical adaptive radar detectors, both in the matched as well as in the mismatched signal case. Remarkably, the Rao test may achieve a matched detection performance which is commensurate with that of the generalized likelihood ratio test (GLRT)-based detectors if a sufficient number of training data is available. Moreover, it also exhibits better rejection capabilities of mismatched signals than the counterparts. In the last part of the work, a two-stage detector whose second stage coincides with the Rao test is devised. It represents a suitable means to restore the detection performance of the plain Rao test in the presence of a small number of training data. Finally, the performance of the aforementioned two-stage processor is analyzed in closed form and the CFAR behavior is proved.

CFAR behavior of adaptive detectors: an experimental analysis

We conduct an experimental analysis for assessing the constant false alarm rate (CFAR) behavior of four coherent adaptive radar detectors in the presence of experimentally measured clutter data. To this end we exploit several data files containing both land, lake, and mixed land and sea clutter, collected by two radar systems (the MIT Lincoln Laboratory Phase-One radar and the McMaster IPIX radar) at different polarizations, range resolutions, and frequency bands. The results show that all the receivers, in the presence of real data, don't respect their nominal probability of false alarm (P/sub fa/), namely they exhibit a false alarm rate higher than the value preassigned at the design stage. Nevertheless one of them, the recursive persymmetric adaptive normalized matched filter (RP-ANMF) is very robust, in the sense that it presents an acceptable displacement from the nominal P/sub fa/, in correspondence of all the analyzed scenarios.

Mitigation Techniques for Non-Gaussian Sea Clutter

This paper deals with the problem of coherent radar detection of targets embedded in clutter modeled as a compound-Gaussian process. We first provide a survey on clutter mitigation techniques with a particular emphasis on adaptive detection schemes ensuring the constant false-alarm rate (CFAR) property with respect to all of the clutter parameters. Thus, we propose a novel decision rule based on a recursive covariance estimator, which exploits the persymmetry property of the clutter covariance matrix. Remarkably, the devised receiver is fully CFAR in that its threshold can be set independently of the clutter distribution as well as of its covariance, even if the environment is highly heterogeneous; namely, the disturbance distributional parameters vary from cell to cell. At the analysis stage, we compare the performance of the novel detector with some classical radar receivers such as that of Kelly and the adaptive matched filter both in the presence of simulated as well as on real radar data, which statistical analysis has shown to be compatible with the compound-Gaussian model. The results show that the new receiving structure generally provides higher detection performance than the others and, for a fluctuating target, it uniformly outperforms the counterparts. We also provide a discussion on the CFAR behavior of the analyzed receivers as well as on their computational complexity.

Sub-optimum coherent radar detection in a mixture of K-distributed and Gaussian clutter

The author introduces two sub-optimum procedures for the coherent detection of a radar target signal, in the presence of a mixture of K-distributed and Gaussian distributed clutter. As a comparison, the optimum Neyman–Pearson and the whitening matched filter strategies to detect coherent pulse trains against the above mentioned disturbance are also presented. The optimum detection scheme is heavy to implement: it involves a numerical integration with respect to the texture variable of the K distribution. It strongly depends on the parameters of the clutter distribution, thus no predetermined threshold can be assigned to achieve a given probability of false alarm if such parameters are unknown. The preferred sub-optimum approach is based on the estimation of the texture variable, which is then used to determine the likelihood ratio. Applying the maximum likelihood estimate the resulting detection strategy is a linear quadratic functional of the observed vector and is clutter distribution free. The performance of the proposed detector is close to optimal and much better than the whitening matched filter detector; moreover, it guarantees approximately constant false alarm rate behaviour, regardless of the clutter distribution.

Clutter Residues of a Coherent MTI Radar Receiver

In moving target indicator radar receivers a saturation is always present in the IF stages in order to compress the dynamic range of large clutter inputs. This nonlinearity greatly affects the performances of the cancelling circuits, because of the spreading of the spectra. A quantitative analysis of this effect has been conducted, with an analytical expression chosen for the limitation characteristic. A high degradation of both single-and double-canceller improvement factors has been obtained. Losses of 20 dB for the double canceller are not uncommon. Clutter residue levels at the receiver output have also been computed in order to see how the performance compares with ideal constant false alarm rate behavior.