Non-homogeneity Detector Research Articles

Covariance‐free non‐homogeneity STAP detector in compound Gaussian clutter based on robust statistics

Space-time adaptive processing (STAP) detects targets by computing adaptive weight vectors for each cell under test using its covariance matrix, as estimated from surrounding secondary cells. In this study, the non-homogeneity detector (NHD) excludes the anomalous secondary cells that adversely affect the detection performance. The existing robust NHDs require estimating the covariance matrix of each secondary cell, which hinders their implementation in modern radars with large-dimensional range cells. In this study, the authors propose a new low-complexity NHD that is suitable for highly correlated clutter environments with both Gaussian and non-Gaussian heavy-tailed distributions. The proposed detector, which is based on the projection depth function from the field of robust statistics, features a non-parametric and covariance-free test statistic. As a result, its computational complexity is much lower than that of current NHDs, such as the widely used normalised adaptive matched filter (NAMF) detector, especially for large-dimensional range cells. In Monte Carlo simulations with different clutter distributions and radar system configurations, the proposed detector shows comparable performance to that of NAMF. The low complexity and robust performance of the new detector make it particularly attractive for real-time applications.

Eigensubspace method for space–time adaptive processing in the presence of non‐i.i.d. clutter and array errors

This study examines space–time adaptive processing in the presence of non-independent and identically distributed (i.i.d.) clutter and array errors. The authors propose a clutter rank estimation method by exploring the spatial–temporal steering vectors of clutter. The proposed method is independent of clutter statistics and direction-independent array errors. They prove that when the proposed clutter rank estimation is used, the estimate of the clutter subspace is asymptotically independent of clutter statistics. This enables an eigensubspace method to acquire the asymptotic independence on clutter statistics. In addition, they prove that the eigensubspace method can suppress the clutter regardless of direction-independent array errors. They also suggest a geometrical non-homogeneity detector for the eigensubspace method. Simulation and experimental results with multi-channel airborne radar measurement (MCARM) data confirm that the eigensubspace method can suppress non-i.i.d. clutter such as discrete clutter as well as correlated clutter regardless of array gain-phase errors. The ability to suppress clutter regardless of clutter statistics and direction-independent array errors makes the eigensubspace method unique and feasible to the practical scenario when clutter is non-i.i.d. and the direction-independent array errors are present.

Robust non‐homogeneity detector based on reweighted adaptive power residue

Space time adaptive processing (STAP) is an excellent technique for improving ground moving target detection performance of airborne radar. However, covariance matrix estimation required by STAP is commonly corrupted by the presence of target-like signals (i.e. outliers), and thus resulting severe performance degradation. To overcome this problem, a robust non-homogeneity detector based on reweighted adaptive power residue is developed, where an adaptively reweighted scheme is employed to training data set. Therefore, the deleterious effect of outliers on the covariance estimation is eliminated and the robustness of the non-homogeneity detector is guaranteed. Performance analysis using the simulated and measured data validates that the proposed method can effectively remove outliers from the training data and improves the radar detection performance in a dense target environment.

Detection of heterogeneous samples based on loaded generalized inner product method

Non-homogeneity detector (NHD) is known for improving the detection performance of space time adaptive processing (STAP) in heterogeneous environment. To mitigate the finite sample effect in the generalized inner product (GIP) based NHD, a new type of GIP detector based on diagonal loading (LGIP) is proposed in this paper. We derive the theoretical mean value of LGIP detector based on random matrix theory. Moreover, an approximation of the theoretical mean value is provided for practical considerations. We then apply the derived theoretical mean value of LGIP to the detection of heterogeneous samples. Finally, by theoretical analysis and numerical simulations, we show the effectiveness of the proposed detector.

Analysis of space–time adaptive processing performance using K-means clustering algorithm for normalisation method in non-homogeneity detector process

This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space–time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the K-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors’ simulation results demonstrate that the K-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

중위수를 이용한 새로운 간섭 공분산 행렬의 예측이 적용된 Space-Time Adaptive Processing에 대한 연구

본 논문은 space-time signal processing(STAP)의 신호 모델과 불균일한 클러터 환경을 제시하고 이를 극복하고자 연구된 nonhomogeneity detector(NHD)의 사용 이후에도 일어나는 심각한 성능 저하를 극복하고자 중위수(median)를 이용한 간섭 공분산 행렬의 새로운 예측 방법을 제시하였다. 또한 대각 로드(diagonal loading)를 적용하여 고유값(eigen value) 특성을 개선시켰으며 signal to interference plus noise ratio(SINR) 손실을 계산하여 비교하였다. 마지막으로 modified sample matrix inversion(MSMI) 통계량으로 목표물 검출 능력을 비교한 결과 제시된 방법이 평균값(average)으로 정의된 기존의 방법보다 우수한 성능을 보임을 확인하였다. In this paper, we presented a signal model of STAP and actual environment of clutter. The novel estimation method of clutter covariance matrix using median value is proposed to overcome serious performance degradation after NHD in nonhomogeneous clutter. Eigen value characteristic is improved through diagonal loading. Target detection ability and SINR loss of the proposed method though MSMI statistic is also compared with conventional method using average value. The simulation results, confirm the proposed method has better performance than others.

STUDY STAP ALGORITHM ON INTERFERENCE TARGET DETECT UNDER NONHOMOGENOUS ENVIRONMENT

In conventional statistical STAP algorithms, the existence of interference target in training samples will lead to signal cancellation, resulting in the output SCR falling and the moving target detection performance degrading. The nonhomogeneity detector is an efiective way to restrain the outlier, which can improve the covariance matrix estimation by detecting the samples containing outliers and rejecting them, and improve the STAP performance. A new interference target detection algorithm is proposed in this paper, the outlier detection is realized by using the samples' data phase information. Compared with traditional method, the improved algorithm is more sensitive to interfering target with difierent azimuth and intensity. Simulation results demonstrate the validity of this improved method.

Comments on "Statistical Analysis of the Nonhomogeneity Detector for Non-Gaussian Interference Backgrounds

Rangaswamy [ldquoStatistical Analysis of the Nonhomogeneity Detector for Non-Gaussian Interference Backgrounds,rdquo IEEE Trans. Signal Process., vol. 53, no. 6, pp. 2101-2111, Jun. 2005] presented a statistical analysis with respect to the nonhomogeneity detector (NHD) for non-Gaussian interference scenarios. However, except for one case, Rangaswamy did not provide explicit expressions for the statistical quantities of interest. In this Comment, exact and elementary expressions are derived for all of the quantities considered by the statistical analysis.

Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds

We derive the nonhomogeneity detector (NHD) for non-Gaussian interference scenarios and present a statistical analysis of the method. The non-Gaussian interference scenario is assumed to be modeled by a spherically invariant random process (SIRP). We present a method for selecting representative (homogeneous) training data based on our statistical analysis of the NHD for finite sample support used in covariance estimation. In particular, an exact theoretical expression for the NHD test statistic probability density function (PDF) is derived. Performance analysis of the NHD is presented using both simulated data and measured data from the multichannel airborne radar measurement (MCARM) program. A performance comparison with existing NHD approaches is also included.

Statistical analysis of the non-homogeneity detector for STAP applications

We present a statistical analysis of the recently proposed non-homogeneity detector (NHD) for Gaussian interference statistics. Specifically, we show that a formal goodness-of-fit test can be constructed by accounting for the statistics of the generalized inner product (GIP) used as the NHD test statistic. The normalized-GIP follows a central-F distribution and admits a canonical representation in terms of two statistically independent chi-squared distributed random variables. Moments of the GIP can be readily calculated as a result. These facts are used to derive the goodness-of-fit tests, which facilitate intelligent training data selection. We then address the issue of space–time adaptive processing (STAP) algorithm performance using the NHD as a pre-processing step for training data selection. Performance of the adaptive matched filter (AMF) method is reported using simulated as well as measured data.

Robust space-time adaptive processing for airborne radar in nonhornogeneous clutter environments

Space-time adaptive processing (STAP) holds tremendous potential for the new generation airborne surveillance radar, in which the phased array antennas and pulse Doppler processing mode are adopted. A new STAP approach using the multiple-beam and multiple Doppler channels is presented here for airborne phased array radar. The approach with space-time multiple-beam (STMB) architecture is robust to array errors and has very low system degrees of freedom (DOFs). Hence, it has low sample support requirement and it is very suitable for the practical planar phased array radar under nonhomogeneous clutter environments. Meanwhile, a new nonhomogeneous detector (NHD) based on the correlation dimension (CD) is also proposed here, which is used as an effective method to screen tracing data prior to detection processing. It can further improve the performance of the STAP approach in the severely nonhomogeneous clutter environments. Therefore, a scheme that incorporates the correlation dimension nonhomogeneity detector (CD-NHD) with the STMB is recommended, which we term CD-NHD-STMB. The experimental simulation results indicate that: 1) the STMB processor is robust to array element error and has high performance under nonhomogeneous clutter environments; 2) the CD-NHD is also effective on the nonhomogeneous clutter. As a result, the CD-NHD-STMB scheme is robust to array element error and nonhomogeneous clutter, and therefore available for airborne phased array radar applications.