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     

Optimization of multiframe target detection schemes

We optimize the performance of multiframe target detection (MFTD) schemes under extended Neyman-Pearson (NP) criteria. Beyond the per-track detection performance for a specific target path in conventional MFTD studies, we optimize the overall detection performance which is averaged over all the potential target paths. It is shown that the overall MFTD performance is limited by the mobility of a target and also that optimality of MFTD performance depends on how fully one ran exploit the information about the target dynamics. We assume a single target situation and then present systematic optimization by formulating the MFTD problems as binary composite hypotheses testing problems. The resulting optimal solutions suggest computationally efficient implementation algorithms which are similar to the Viterbi algorithm for trellis search. The optimal performances for some typical types of target dynamics are evaluated via Monte-Carlo simulation     

Adaptive detection schemes in compound-Gaussian clutter

Radar detection of coherent pulse trains embedded in compound-Gaussian disturbance with partially known statistics is discussed. We first give a thorough derivation of two recently proposed adaptive detection structures. Next, we derive a different detection scheme exploiting the assumption that the clutter is wide-sense stationary. Resorting to the theory of circulant matrices, in fact, we demonstrate that the estimation of the structure of the clutter covariance matrix can be reduced to the estimation of its eigenvalues, which in turn can be (efficiently) done via fast Fourier transform codes. After a thorough performance assessment, mostly carried on via computer simulations, the results show that the newly proposed detector achieves better performance than the two previously introduced adaptive detectors. Moreover, a sensitivity analysis shows that, even though this detector does not strictly guarantee the constant false alarm rate property with respect to the clutter covariance matrix, it is robust, in the sense that its performance is only slightly affected by variations in the clutter temporal correlation     

Multistage partially adaptive STAP CFAR detection algorithm

A new method of partially adaptive constant false-alarm rate (CFAR) detection is introduced. The processor implements a novel sequence of orthogonal subspace projections to decompose the Wiener solution in terms of the cross-correlation observed at each stage. The performance is evaluated using the general framework of space-time adaptive processing (STAP) for the cases of both known and unknown covariance. It is demonstrated that this new approach to partially adaptive STAP outperforms the more complex eigen-analysis approaches using both simulated DARPA Mountain Top data and true pulse-Doppler radar data collected by the MCARM radar     

Spatial adaptive subspace detection in OTH radar

The work presented here addresses the problem of target detection against spatially structured interference composed of jamming plus noise, where for practical reasons, the received target wavefront may also deviate from the traditional plane wave model. This detection problem arises in over-the-horizon (OTH) radar systems where spatially distributed targets often compete for detection against directional interference that is spread over the entire range-Doppler search space. Conventional detection processing schemes are compared with a recently proposed adaptive subspace detector (ASD) that takes both the spatial structure of the interference and the possibility of target wavefront distortions into account. Experimental array data recorded by the Jindalee sky-wave and Iluka surface-wave OTH radar systems, located in central and northern Australia respectively, is used to evaluate detection performance.     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.     

On adaptive multiband signal detection with GLR algorithm

An adaptive multiband detector based on the principle of the generalized likelihood ratio test (GLR) is presented. Its detection performance is studied and compared with that of the corresponding single-band GLR detector. The multiband detector is shown to significantly outperform the single-band under the chosen system constraint, especially when the amount of data available from a single frequency band is severely limited by the environment     

Novel single-phase AC/DC converter with two PWM control schemes

A novel single-phase AC/DC converter with two pulsewidth modulation (PWM) schemes is proposed to draw a sinusoidal line current with nearly unity power factor, achieve balanced neutral point voltage and regulate the DC bus voltage. With the aid of neutral point clamped scheme, a three-level voltage pattern is generated on the AC side of the proposed rectifier. To track the tine current command derived from a voltage controller and a phase-locked loop circuit, a hysteresis current control scheme is used in the inner loop control. A capacitor voltage compensator is employed to achieve the balanced neutral point voltage. To investigate the effectiveness of the proposed control scheme, the simulation and experimental results based on a laboratory prototype circuit are performed.     

Performance of an adaptive detection algorithm; rejection ofunwanted signals

The analysis of an adaptive detection algorithm described previously by the author (1985, 1986) is extended. Previously, the performance was evaluated for the case of a signal corresponding exactly to the steering vector used in the derivation of the algorithm. Here the performance for signals arriving from other directions is evaluated. It is shown that these signals are rejected much more strongly than would be suggested by the sidelobe levels of the adapted patterns themselves     

Covariance matrix estimation for adaptive CFAR detection incompound-Gaussian clutter

We address the estimation of the structure of the covariance matrix and its application to adaptive radar detection of coherent pulse trains in clutter-dominated disturbance modeled as a compound-Gaussian process. For estimation purposes we resort to range cells in spatial proximity with that under test and assume that these cells, free of signal components, can be clustered into groups of data with one and the same value of the texture. We prove that, plugging the proposed estimator of the structure of the covariance matrix into a previously derived detector, based upon the generalized likelihood ratio test (GLRT), leads to an adaptive detector which ensures the constant false alarm rate (CFAR) property with respect to the clutter covariance matrix as well as the statistics of the texture. Finally, we show that this adaptive receiver has an acceptable loss with respect to its nonadaptive counterpart in cases of relevant interest for radar applications     

On adaptive multiband signal detection with the SMI algorithm

In a nonstationary and/or nonhomogeneous interference environment, an adaptive system for target detection may suffer a severe performance degradation due to the lack of a sufficient amount of data from which the system can learn (estimate) the statistics of the environment. The detection performance of an adaptive system, which employs a frequency diversity (multiband) signaling waveform and a multiband sample matrix inversion algorithm (SMI), is analyzed. By comparison with the corresponding single-band system under the chosen system constraint, it is shown that the multiband system can significantly outperform the single band when the amount of data available from a single frequency band is severely limited by the environment     

Unification and comparison between two concepts of radar targetangular glint

This paper provides a general proof of unification between two concepts of radar target angular glint. The analysis of a two-point target model, consisting of an electrically and magnetically ideal dipole, shows the relation, difference, and equivalent condition between them more clearly. Angular glint has been synthesized from the measured data of radar echo phase, and compared with the theoretical result     

On distributed detection with correlated sensors: two examples

The authors study the effect of correlated noise on the performance of a distributed detection system. They consider a suboptimal scheme by assuming that the local sensors have the same operating point, and that the distribution of the sensor observation is symmetric. This implies that the joint distribution of the sensor decisions, and therefore the fusion rule, are symmetric functions of the sensor decisions. The detection of a known signal in additive Gaussian noise and in Laplacian noise are considered. In both cases, system performance deteriorates when the correlation between the sensor noises is positive and increasing, whereas the performance improves considerably when the correlation is negative and increasing in magnitude     

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.<>     

Sensor/actuator failure detection in the Vista F-16 by multiplemodel adaptive estimation

Multiple model adaptive estimation (MMAE) is applied to the Variable-In-flight Stability Test Aircraft (VISTA) F-16 flight control system at a low dynamic pressure flight condition (0.4 M at 20000 ft). Single actuator and sensor failures are addressed first, followed by dual actuator and sensor failures. The system is evaluated for complete or “hard” failures, partial or, “soft” failures, and combinations of hard and soft actuator and sensor failures. Residual monitoring is discussed for single and dual failure scenarios. Performance is enhanced by the application of a modified Bayesian form of MMAE, scalar residual monitoring to reduce ambiguities, automatic dithering where advantageous, and purposeful commands     

A modified adaptive Kalman filter for real-time applications

A modified adaptive Kalman filtering algorithm is derived for the standard linear problem under an irregular environment where all variances of the zero-mean Gaussian white (system and observation) noises are unknown a priori. This algorithm has certain merits over various existing adaptive schemes in that it is simple, efficient, and suitable for real-time applications. An illustrative numerical example is presented     

A mean level adaptive detector using nonconcurrent data

Convergence results for a mean level adaptive detector (MLAD) are presented. The MLAD consists of an adaptive matched filter (for spatially correlated inputs) followed by a mean level detector (MLD). The optimal weights of the adaptive matched filter are estimated from one batch of data and applied to a statistically independent batch of nonconcurrent data. The threshold of the MLD is determined from the resultant data. Thereafter a candidate cell is compared against this threshold. Probabilities of false alarm and detection are derived as a function of the threshold factor, the order of the matched filter, the number of independent samples per channel used to calculate the adaptive matched filter weights, the number of samples used to set the MLD threshold, and the output signal-to-noise power ratio of the optimal matched filter. A number of performance curves are shown and discussed     

A comparison of static and rotordynamic characteristics for two types of liquid annular seals with and without helical grooved stator

Less leakage is a benefit of parallel grooved liquid seals(labyrinth seals). But researches show that the liquid seal with parallel grooves on the rotor harms the rotor stability. The seal with helical grooves on stator performs well in terms of rotordynamics, and its leakage is sensitive to the rotating speed. To make use of the advantages of both seals and improve seal stability, based on the Smooth-stator/Parallel Grooved-rotor(SPG) liquid seal, a Helical Grooved-stator/Parallel Grooved-rotor(HGPG) liquid seal is designed. To evaluate two liquid seals' leakage, rotordynamic characteristics and drag power loss, a transient computational fluid dynamics-based method is employed. This method is based on the multi-frequency elliptical-orbit rotor whirling model and the mesh deformation technique. The published experimental data of the leakage and rotordynamic force coefficients for an SPG liquid seal are used to validate the accuracy and dependability of the current method. Seal leakage and force coefficients are presented and compared for the SPG liquid seal and the HGPG liquid seal at various pressure drops. The influences of parallel groove depth on the leakage and rotordynamic properties for the HGPG liquid seals at two rotational speeds(2000,6000 r/min) are analyzed. The numerical findings demonstrate that the novel HGPG liquid seal has a lower leakage flow rate(by $22.3%) than the traditional SPG liquid seal. There is an optimal parallel groove depth that minimizes leakage. The presented novel HGPG liquid seal significantly improves rotordynamic stability, due to the similar effective stiffness and the obviously larger positive effective damping. Reducing parallel groove depth can increase the positive effective damping.In terms of leakage and rotordynamic characteristics, the novel HGPG liquid seal is a better seal design for liquid turbomachinery.