A variably trimmed mean CFAR radar detector

A variably trimmed mean (VTM) constant false alarm rate (CFAR) detector in which the threshold is determined by processing a linear combination of a group of ordered samples in each window is introduced. Unlike the trimmed mean detector, the number of ordered samples that require further processing is allowed to vary according to a data-dependent rule. It is demonstrated that the VTM detector exhibits performance characteristics that are independent of the total (stationary) noise power. Simulated performance results are presented for regions of clutter power transitions and for multiple target environments to illustrate the possible improvement over the order-statistic detector that can be obtained by using a VTM detector     

An adaptive array detector with mismatched signal rejection

An adaptive detection algorithm with a sensibility parameter for rejecting unwanted signals is presented. This algorithm is a simple modification of the generalized likelihood ratio (GLR) detector (or test) for detecting a signal in zero mean Gaussian noise with unknown correlation matrix. Specifically, the adaptive detection algorithm is obtained by introducing an arbitrary positive scalar, which is called the sensitivity parameter, into the GLR detector as a multiplier of an already existing quadratic term. The GLR detector then becomes a special case of this detector for the unity sensitivity parameter. It is shown that the sensitivity parameter controls the degree to which unwanted signals are rejected. From numerical examples, it is demonstrated how the sensitivity parameter can be chosen such that unwanted signals, can be rejected while maintaining acceptable detection loss for slightly mismatched signals. Further insight into previous work on adaptive detection is also given     

Analysis of an adaptive CFAR detector in non-Gaussian interference

Coherent signal detection in non-Gaussian interference is presently of interest in adaptive array applications. Conventional array detection algorithms inherently model the interference with a multivariate Gaussian random vector. However, non-Gaussian interference models are also under investigation for applications where the Gaussian assumption may not be appropriate. We analyze the performance of an adaptive array receiver for signal detection in interference modeled with a non-Gaussian distribution referred to as a spherically invariant random vector (SIRV). We first motivate this interference model with results from radar clutter measurements collected in the Mountain Top Program. Then we develop analytical expressions for the probability of false alarm and the probability of detection for the adaptive array receiver. Our analysis shows that the receiver has constant false alarm rate (CFAR) performance with respect to all the interference parameters. Some illustrative examples are included that compare the detection performance of this CFAR receiver with a receiver that has prior knowledge of the interference parameters     

A non-contact lie detector using radar vital signs monitor (RVSM)technology

Traditional lie detector testing requires the subject to be physically attached to a variety of sensors. This is impractical for scenarios such as checkpoints where a large number of individuals are entering at a high rate, necessitating the employment of other methods. Currently, checkpoint officers must make a quick decision to determine if an individual is being deceptive, and if, in turn, they should be searched. The remote detection of deception (RDD) concept uses a non-contact sensor to obtain physiological information that can be used to aid the checkpoint officer's decision. Such a device must be able to sense physiological signals from the body that may indicate deception in an unobtrusive and non-contact manner     

Tracking targets using adaptive Kalman filtering

A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly maneuvering target is tracked     

Implementing digital terrain data in knowledge-aided space-time adaptive processing

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance     

A noncoherent adaptive detection technique

An adaptive detection technique suitable for both stationary and nonstationary noise environments based upon a generalized likelihood ratio test (GLRT) formulation is presented. The detector, which is statistically equivalent to a special form of the Wilks's lambda test, noncoherently combines the information contained in a pulse train of arbitrary length for decision-making purposes. The probability density function of the test under the noise only hypothesis is shown to be central χ². Under the signal plus noise hypothesis, an exact statistical characterization of the test cannot be obtained, and, therefore, a Chernoff bound is derived. Results in terms of the probability of detection versus signal-to-noise ratio (SNR) obtained from Monte Carlo simulation, the Chernoff bound, and the optimal matched filter case are examined. The performance of the noncoherent detector is shown to be a function of the covariance matrix estimate and the number of data samples     

Improved clutter mitigation performance using knowledge-aided space-time adaptive processing

This paper presents a framework for incorporating knowledge sources directly in the space-time beamformer of airborne adaptive radars. The algorithm derivation follows the usual linearly-constrained minimum-variance (LCMV) space-time beamformer with additional constraints based on a model of the clutter covariance matrix that is computed using available knowledge about the operating environment. This technique has the desirable property of reducing sample support requirements by "blending" the information contained in the observed radar data and the a priori knowledge sources. Applications of the technique to both full degree of freedom (DoF) and reduced DoF beamformer algorithms are considered. The performance of the knowledge-aided beam forming techniques are demonstrated using high-fidelity simulated X-band radar data     

Nonlinear adaptive spacecraft attitude control using solar radiation pressure

Spacecraft and interplanetary probes orbiting at high altitudes experience forces due to solar radiation pressure, which can be used for maneuvering. The question of large angle pitch attitude maneuvers of satellites using solar radiation torque is considered. For pitch axis maneuver, two highly reflective control surfaces are used to generate radiation moment. The solar radiation moment is a complex nonlinear function of the attitude and parameters of the satellite, the orbital parameters, and the deflection angles of the reflective control surfaces. It is assumed that the parameters of the satellite model are unknown. Based on a backstepping design technique, a nonlinear adaptive control law is derived for the control of the pitch angle. In the closed-loop system, the pitch angle asymptotically tracks prescribed reference trajectories. Simulation results are presented to show that the adaptive control system accomplishes attitude control of the satellite in spite of the parameter uncertainties in the system.     

Fluid film break-up modeling using adaptive mesh refinement models

Numerical modelling of fluid film break-up is carried out using adaptive mesh models. The results obtained are compared with experimental data. It is shown that using local adaptation with hanging nodes allows reaching a sufficient accuracy of fluid film boundaries estimation and definition of droplet dimensions resulted from its atomization. It is noticed that these are not only a turbulence model and adaptation sequence selection that substantially affect the computation results but also a type of initial mesh model.     

A robust exponential mixture detector applied to radar

Exponential mixture probability density functions (pdfs) are shown to be useful models of radar sea clutter. The variability of certain parameters leads to estimation error and degradation in the performance of detection algorithms derived from this model. Robust implementations are introduced by assuming that parameters are known within certain intervals and selecting values to prevent an excessive number of false alarms. An empirical study demonstrates an average 6-9 dB gain in comparison with a constant false-alarm rate (CFAR) processor     

A new distributed constant false alarm rate detector

A new constant false alarm rate (CFAR) test termed signal-plus-order statistic CFAR (S+OS) using distributed sensors is developed. The sensor modeling assumes that the returns of the test cells of different sensors are all independent and identically distributed In the S+OS scheme, each sensor transmits its test sample and a designated order statistic of its surrounding observations to the fusion center. At the fusion center, the sum of the samples of the test cells is compared with a constant multiplied by a function of the order statistics. For a two-sensor network, the functions considered are the minimum of the order statistics (mOS) and the maximum of the order statistics (MOS). For detecting a Rayleigh fluctuating target in Gaussian noise, closed-form expressions for the false alarm and detection probabilities are obtained. The numerical results indicate that the performance of the MOS detector is very close to that of a centralized OS-CFAR and it performs considerably better than the OS-CFAR detector with the AND or the OR fusion rule. Extension to an N-sensor network is also considered, and general equations for the false alarm probabilities under homogeneous and nonhomogeneous background noise are presented.     

Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.     

Super-resolution reconstruction of astronomical images using time-scale adaptive normalized convolution

In this work, we describe a new multiframe Super-Resolution (SR) framework based on time-scale adaptive Normalized Convolution (NC), and apply it to astronomical images. The method mainly uses the conceptual basis of NC where each neighborhood of a signal is expressed in terms of the corresponding subspace expanded by the chosen polynomial basis function. Instead of the conventional NC, the introduced spatially adaptive filtering kernel is utilized as the applicability function of shape-adaptive NC, which fits the local image structure information including shape and orientation. This makes it possible to obtain image patches with the same modality, which are collected for polynomial expansion to maximize the signal-to-noise ratio and suppress aliasing artifacts across lines and edges. The robust signal certainty takes the confidence value at each point into account before a local polynomial expansion to minimize the influence of outliers. Finally, the temporal scale applicability is considered to omit accurate motion estimation since it is easy to result in annoying registration errors in real astronomical applications. Excellent SR reconstruction capability of the time-scale adaptive NC is demonstrated through fundamental experiments on both synthetic images and real astronomical images when compared with other SR reconstruction methods.     

Precise navigation using adaptive FIR filtering and time domainspectral estimation

This paper introduces a new low cost, short range, positioning system based on adaptive finite impulse response (FIR) filtering and time domain spectral estimation. The system can determine absolute positions with a high degree of accuracy and is well suited for real time navigation. The approach is based upon signal processing techniques and a priori knowledge of the system transfer function. The first step is to measure the phase response of the linear transfer function and then using a FIR filter the time response of the system can be determined. The FIR filter computes the time response by performing a deconvolution between the measured phase response, and the complex conjugate of the transfer function. By correlating the known input impulse response with the output of the FIR filter, an error term is generated. The time delay of the system is determined by adjusting the FIR filter coefficients to minimize the error term. Simulated analysis of the system indicates a worst case error of ±16 cm     

A multisensor tracking system with an image-based maneuver detector

Rapid and reliable decisions about the onset and termination of maneuvers are critical for accurate tracking of maneuvering targets. Given the appropriate filter model, the use of multiple sensors of different capabilities and strengths can improve the quality and the reliability of the tracking system A multisensor tracking system where the usage of the image sensor is two fold is presented. First, if is used to perform maneuver detection using minimum computation and storage. Second, its bearing and elevation measurements are used along with 3-D radar observations to improve the tracking quality. The advantages of the proposed multisensor tracking system are discussed and demonstrated via simulations     

Deployable space structure control using adaptive predictive controller with notch filter

The present study addresses the adaptive predictive controller with adaptive notch filter for the tip position control of a deployable space structure model. An adaptive notch filter is designed to estimate multiple bending mode frequencies of the deployable manipulator and to minimize the effect of bending vibration. The results show that the adaptive predictive controller with adaptive notch filter is quite effective controlling the tip position of a deployable space structure under poor modeling information.     

A self-normalizing gradient search adaptive array algorithm

A method is described to normalize the step parameter and perturbation amount used for gradient search adaptive algorithms. The step normalization assures rapid stable convergence. The perturbation normalization also assures rapid stable convergence and limits the output perturbation noise to a level below thermal noise. The normalizations are computationally simple and are consistent with the use of a single receiver at the adaptive array output. Results are presented to verify the robustness of the technique.<>     

A comparison of two adaptive detection schemes

Two schemes for adaptive detection are compared: Kelly's generalized likelihood ratio test (GLRT) and the mean level adaptive detector (MLAD). Detection performance, P_D, is predicted for the two schemes under the assumptions that the input noises are zero-mean complex Gaussian random variables that are temporally independent but spatially correlated; and the amplitude of the desired signal is Rayleigh distributed. P_D is computed as a function of the false alarm probability, the number of input channels, the number of independent samples per channel, and the matched filtered output signal-to-noise (S/N) power ratio. In this analysis the GLRT is shown to have better detection performance than the MLAD. The difference in detection performance increases as one uses fewer input samples. However, the required number of samples necessary to have only a 3 dB detection loss for both detection schemes is approximately the same. This is significant since for the present, the MLAD is considerably less complex to implement than the GLRT