Automatic Censoring CFAR Detector Based on Ordered Data Difference for Low-Flying Helicopter Safety

Radar usually employs several signal processing methods successively for detection. Each processing step influences the radar detection performance. Thus, analysis of the radar detection performance considering the complete signal processing flow provides significant reference for radar detector design. In this paper, with the assumption that matched filtering, square-law detection, non-coherent integration, cell averaging constant false alarm rate detection and double-threshold detection are employed sequentially for radar detection, radar detection performance in K distributed sea clutter plus Rayleigh distributed thermal noise is analyzed. Mathematical expressions of radar false alarm probability and detection probability are derived. Radar detection performances for four types of Swerling fluctuating targets in various circumstances are presented by simulation. And comprehensive effect of K distributed sea clutter plus Rayleigh distributed thermal noise on radar detection performance is analyzed.

In radar signal processing distinction of false targets from real targets and fixing their rate in different radar environments is desirable. In this paper, at first, Switching Constant False Alarm Rate (S-CFAR) processor has been improved to achieve Improved SCFAR in order to fix the false alarm rate not only in homogenous environment only with thermal noise but also in non-homogenous environment, consisting clutter edge and multiple targets in the background Gaussian noise. Then in continue, a new CFAR detector which is composed of an excisior and a Switching CFAR detector, in an environment with K distribution, has been introduced. The new detector is named an Excision Switching CFAR (EXS-CFAR). The equations have been achieved by assuming the targets in Swerling I and closed form. The simulation results confirm that the introduced processors can fix the false alarm rate in both homogenous and non-homogenous environment with less detection loss. In addition, this method is simpler in implementation comparing with other samples ordering processors.

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.

In this paper, we consider moving target detection using a distributed multiple-input multiple-output (MIMO) radar on stationary platforms in nonhomogeneous clutter environments. Our study is motivated by the fact that the multistatic transmit-receive configuration in a distributed MIMO radar causes nonstationary clutter. Specifically, the clutter power for the same test cell may vary significantly from one transmit-receive pair to another, due to azimuth-selective backscattering of the clutter. To account for these issues, a new nonhomogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients (and hence different clutter power) for different transmit-receive pair, is introduced and the relation to a general clutter model is discussed. Following the proposed clutter model, we develop a generalized-likelihood ratio test (GLRT) for moving target detection in distributed MIMO radar. The GLRT is shown to be a constant false alarm rate (CFAR) detector, and the test statistic is a central and noncentral Beta variable under the null and alternative hypotheses, respectively. Simulations are provided to demonstrate the performance of the proposed GLRT in comparison with several existing techniques.

The performance of space–time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial–temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial–temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

This work presents a new CFAR detector with greatest of selection based on best linear unbiased method (BLUGO-CFAR). This CFAR detector has the ability of CFAR algorithms with greatest of selection to control the rise of false alarm rate at clutter edge, it also has the advantage of CFAR algorithms based on order statistics in multiple targets situation. The analytic results show that the performance of BLUGO is evidently superior to that of OSGO both in homogeneous background and in multiple targets situations, resulting by an increase in the number of order statistics to estimate the noise power level. Since the reference window is split into two sub-windows, the sample sorting time is reduced as half as that of OS. If Ml=M2=0, Nl=N2=0, BLUGO reduces to GO, if M1=N1=O, BLUGO reduces to MX-CMLD.

In this paper, we focus on the performance improvement of constant false alarm rate (CFAR) detector in heterogeneous Compound-Gaussian background. This paper is motivated by the fact that the detectors' performance degradation when an unknown located clutter edge exists in the reference window that divide the data samples into two different independent and identically distributed (IID) Compound-Gaussian distribution. To account for this issue, we propose an automatic clutter edge estimation algorithm based on goodness of fit (GoF) which can select IID data with the cell under test (CUT), and we also suggest a CFAR detector (CFARD) uses this clutter edge estimation algorithm as preprocessing to enhance the detection performance around clutter edges. Simulations are provided to demonstrate the performance of the proposed CFARD in comparison with Ordered-Statistic-CFARD (OS-CFARD).

Performance analysis of some CFAR detectors in homogeneous and non-homogeneous Pearson-distributed clutter

A new constant false alarm rate (CFAR) detection processor is presented that exhibits a robust CFAR performance in the condition of non-homogenous environment. Its performance would be independent of background noise power. Since the processor can track noise power variation and adopt itself to the new background conditions. The new CFAR algorithm, called as Adaptive Switching CFAR (AS-CFAR) is analysed and simulated CFAR to demonstrate its improved robustness in non-homogenous environments. (3 pages)

During radar operation, changes in the radar cross section (RCS) of a target have an adverse effect on the radar detection performance. In particular, the RCS of a target with high maneuvering characteristics can be changed rapidly. Therefore, a precise prediction of the RCS change is essential for satisfactory radar detection performance. In this study, a modeling and simulation (M&S) tool was developed for the estimation of radar detection performance. The M&S tool comprises a radar model, target model, and wave propagation model. In particular, pre-obtained RCS simulation data were utilized to consider the effect of the RCS change of the target. Using the developed tool, the RCS based on the relative attitude is extracted from the RCS simulation data and the RCS processed data using the moving average method. The M&S model is verified by comparing the two test results with the radar received signal power. Consequently, we verified that when using the RCS manufacturing data, the signal power is more similar to the test data than the other RCS simulation methods. It is expected that the radar performance on the high-maneuvering target can be estimated using the developed M&S tool.

Polarization cancellation is an effective method to improve the radar target detection performance under the main lobe interference environment. Interference suppression ratio is widely used to assessment polarization filter performance. However, the radar target detection performance is determined by the Signal Interference plus Noise Ratio (SINR). It is important to analyze the polarization filter's effect on the improvement of SINR. The paper proposes a mathematical model of the relationship between the changes of SINR with the relevant factors under different interference conditions. The relevant factors conclude the degree of polarization (DoP) of interference and the polarization differences between the target with interference. Based on this model, the effect of relevant factors on radar target detection performance is described. Finally, the model is tested and verified by radar target detection experiments under the jamming conditions. The results of the paper will provide a reference to design the anti-jamming configuration for the polarimetric radar.

Localization is one of the most important research subjects in terms of the wireless sensor networks (WSNs), because most of the information measured and distributed by the sensors are useful when sensors locations are well known. In this paper, a range free localization algorithm for sensor positioning is proposed. It is based on sensor distributed network model, coverage radius, hop count between each sensor and anchor, and improvement in the minimum mean square error, which computes coefficients for distance estimation between sensors and anchors. In the proposed method, the best coefficient for each hop count is computed with offline processing and Monte Carlo method. Then, these coefficients are stored in each sensor database and they are used for localization in the practical environment. Unlike some existent positioning methods, in this algorithm, it is not required to compute distance estimation coefficient by sensors. In the proposed method, it is assumed that all sensors use the omnidirectional antenna for their usual information transmission. High precision in geographical coordinate determining, less traffic load, and especially good performance in the homogeneous and non-homogeneous environment are the most important features of this algorithm. Simulation results show that the proposed algorithm has a good position determination and reduced traffic load for WSNs, as compared with some existent positioning schemes. Indeed, the proposed method improves localization precision and reduces traffic load simultaneously.

Improvement of the defect detection capability of ultrasonic inspection of coarse-grained austentic materials by means of a combination of signal enhancement methods. Final report (In German): Kapitz, H. Frauhnefer-Inst. fuer Zerstoerungsfreie Pruefverfahren, Saarbruecken (Germany). TIB/B88-81362/GAR, 73 pp. (27 Sep. 1986)

Detection algorithms in a nonhomogeneous clutter environment have to contend with the problem of excessive false alarm rates when the clutter is not sufficiently suppressed. Prior to signal detection, adaptive receivers must either "null" or "whiten" the clutter. The former approach requires estimates of the basis vectors of the clutter subspace while the latter requires estimates of the covariance matrix of the clutter. It may be preferable to null the clutter when the dimensionality of the clutter subspace (M) is small in comparison to the length of the data vector coherently processed (N) and also when the signal to be detected is not substantially within the clutter-plus-noise subspace. Depending on the scenario, radar clutter data may have a variety of effects such as clutter discretes, data dropouts due to terrain masking that cause shadowing, multipath effects, and non-Gaussian amplitude statistics. We consider a statistical model which can account for some of these effects and apply the model to design an algorithm to obtain a maximum likelihood estimate (MLE) of the orthonormal basis vectors of the clutter-plus-noise subspace. We show that the problem can be reformulated to develop a recursive approach for MLE based on the expectation-maximization (EM) algorithm. Results of the analysis show that each training vector must be scaled by a nonnegative scale factor from the interval [0,1] prior to subspace estimation. The scale factor applied to each training vector increases monotonically with the subspace clutter-plus-noise power of the vector. Conditions for the existence and uniqueness of the solution are considered. A statistical interpretation of the solution is provided along with sample simulation results.

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed in this paper, which only uses a single snapshot data of a cell under test (CUT) to suppress the clutter and has fast computational speed. Firstly, three hyper-parameters required to obtain the sparse solution are derived. Secondly, the comparative analysis of their iterative formulas is made, and the piecewise iteration of hyper-parameter that has an obvious influence on the computational complexity of obtaining sparse solution is presented. Lastly, with the approximate prior information of the target, the clutter sparse solution is given and its covariance matrix is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposal can dramatically decrease the computational burden while keeping the superior heterogeneous clutter suppression performance.