The paper investigates the impact of SAR image enhancement on the performance of small target detection in SAR images. Three SAR image enhancement algorithms are evaluated on large SAR image data-sets. The evaluation results show that image enhancement can greatly improve the performance of false alarm mitigation, and that the level of performance improvement is correlated with the resolution and background suppression of the enhanced images. The higher the resolution and the level of background suppression, the higher the level of performance improvement.

This paper investigates the impact of SAR image enhancement on the performance of small target detection in SAR images. Three SAR image enhancement algorithms are evaluated on large SAR image data-sets. The evaluation results show that image enhancement can greatly improve the performance of false alarm mitigation, and that the level of performance improvement is correlated with the resolution and background suppression of the enhanced images. The higher the resolution and the level of background suppression, the higher the level of performance improvement.

The use of median and averaging filters is fairly routine in signal processing applications. One problem in using such algorithms is the lack of objective criteria by which to decide whether an averager or a median filter is more appropriate. We formulate an L/sub p/ (1/spl les/p/spl les/2) normed filter where p is chosen as a function of the kurtosis of the residual vector; we restrict attention in this work to a mean filter (p=2) and a median filter (p=1). In order to highlight the effectiveness of this filtering algorithm we demonstrate reduced sum squared error by adaptively filtering a sinusoid and a test image in the presence of both additive white Gaussian noise and an impulsive noise component.

Normal form digital filters are attractive due to their desirable properties when implemented in finite wordlength arithmetic. These filters are free from all overflow limit cycles and quantization limit cycles when magnitude truncation is used. However, when two's complement truncation (TCT) quantization is used, limit cycles can still exist. It is shown that when block structures are used, normal form digital filters can be made free of limit cycles due to TCT quantization. It is shown that this can be done with a small block size. An algorithm is also presented to find the minimum block size required for a given filter. Some examples are given to illustrate the results.

A countermeasure technique to a follower tone-jammer is evaluated for frequency-hopped spread spectrum communications. This technique utilizes randomized decisions by the transmitter and the receiver to lure the jammer into helping the communicator's performance part of the time. It is shown that the proposed technique reduces the bit error rate of the system.>

Model based digital filter design may be an attractive technique if the desired impulse response, perhaps measured in the field, closely matches a simple time series model, such as an autoregressive model. For autoregressive model based filters, a least squares solution is convenient for computational reasons, but is adversely affected by data outliers, such as a severe noise spike. Previously, we have shown that an Lp (p=1) may generate a robust solution in certain cases, however, such an estimates, although more robust than least squares methods, suffers breakdown when the data outliers are too frequent or occur at end points of the data record. We demonstrate the increased robustness of a model based filter design via choosing model coefficients by minimizing the median of the square of the residuals.>

Model-based digital filter design may be an attractive technique if the desired impulse response, perhaps measured in the field, closely matches a simple time series model, such as an autoregressive model. For autoregressive model based filters, a least squares solution is convenient for computational reasons, but is adversely affected by data outliers, such as a severe noise spike. Previously, the authors have shown that an Lp(p=1) may generate a robust solution in certain cases, however, such an estimator, although more robust than least squares methods, suffers breakdown when the data outliers are too frequent or occur at end points of the data record. The present paper demonstrates the increased robustness of a model based filter design via choosing model coefficients by minimizing the median of the square of the residuals.>

It a relatively short frequency shift keyed (FSK) sinusoidal burst signal is available for analysis, transient effects within the analysis window may be detrimental to linear predictive model based frequency estimation methods such as the forward-backward linear predictive technique. Signal transients are manifested as spikes within the residual vector produced from a linear predictive based approach to frequency estimation. For such cases, it is demonstrated that a robust estimator, such as an estimator generated via an L/sub 1/ normed error criterion, can produce less biased frequency estimates. Additionally, it is shown that sinusoidal frequency estimates generated via an L/sub 1/ normed solution are insensitive to initial signal phase in contrast to least squares based estimates. It is demonstrated that a robust Wigner distribution can be estimated based upon the ideas presented in this paper.>

In recording high-frequency biological signals such as neural activity, noise may be difficult to distinguish from useful signal content. Conventional noise-removal techniques such as autoregressive modeling do not perform well on impulsive signals such as neural activity. Impulsive data can be more effectively extracted using p-normed error models, where p=2 corresponds to the least squares model, and p=1 corresponds to the least absolute value case. The least absolute value model is best when the model error is Laplace distributed. Thus, a judicious choice of p-normed model will allow outliers, such as the spikes from neural activity, to be passed through the algorithm while other types of noise are suppressed.>

It has been shown that an L/sub p/ normed solution to linear prediction equations may be advantageous in certain time-series modeling applications. Specifically, in the context of sinusoidal frequency estimation from data contaminated by impulsive noise, choosing p=1 may result in less biased frequency estimates. Computing L/sub p/ normed solutions requires iterative methods that are computationally expensive. By plotting prediction error filter root migration in the complex z-plane as a function of iteration step one is able to construct a suboptimal weighted least-squares solution. This solution, though not an L/sub p/ normed solution, is still robust against impulsive noise and is not as computationally expensive as iterating to a true L/sub p/ normed solution.>

The authors describe how the complex kurtosis of the residual vector can be used as a test statistic for deciding if impulsive noise is present in sonic log data and consequently whether to incur the additional computational expense for calculating an L/sub 1/ normed solution. The robustness of the method is demonstrated by a Monte Carlo simulation. The FBLP (forward-backward linear prediction) spectral estimator has been found to be essentially unbiased in the presence of moderate amounts of additive Gaussian noise. The addition of an impulse to the data did not significantly increase the bias of the FBLP frequency estimator.>

Previous work has shown that increased frequency resolution is possible in the presence of impulsive noise utilizing an L1solution to a set of two-dimensional linear prediction equations. This research considers spectral estimation via an L1solution to a set of overdetermined linear prediction equations (1-D) for Gaussian and non-Gaussian noise. The resulting spectral estimator is evaluated for the case of two sinusoids in noise for a variety of noise types: Gaussian, uniform, Rayleigh, and impulsive.