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Many modern radar systems are overcoming the need for high-power transmitters by utilizing low peak-power, high duty-cycle waveforms, making noncooperative detection methods by traditional electronic surveillance a difficult task. This technological difficulty is driving a need for computationally tractable detection and characterization algorithms. Here, a practical method for detecting and fully characterizing an arbitrary number of low-power linear frequency modulated continuous wave (LFMCW) radar signals is achieved by dividing the time-domain signal into contiguous segments and treating each signal segment as a sum of harmonic components corrupted by noise with an unknown, time-varying power spectral density. This method is developed analytically and evaluated experimentally, revealing that the practicality of the method comes at the ex-pense of a loss in estimation accuracy when compared to the Cramer–Rao lower bound. Experimental results indicate that the parameters of two simultaneous LFMCW signals can be estimated to within $10\%$ of their true values with probability greater than $90\%$ when input signal-to-noise ratios are $-$ 10 dB and above with a 25 MHz bandwidth receiver.

This paper develops an algorithm that can be used to solve the data association problem faced by a surveillance aircraft using Direction of Arrival angle measurements to locate a stationary RF signal source. The algorithm is based on statistical clustering of measurements with clusters being formed using a Mahalanobis distance association criterion. This approach accounts for angle measurement error statistics and avoids the computational complexity of an exhaustive combinatorial assignment. The optimal cluster is the one that maximized the target position log-likelihood function. This cluster is used to compute a target position estimate then removed from the set of measurements. The process is repeated until no additional clusters can be formed. Simulation results are shown where 100 measurements are distributed randomly across 7 target signal sources.

In this paper we overview the theory of Linear Time-Varying (LTV) filters and investigate via simulation their application to buried target classification in challenging nonstationary environments; in particular, environments where noise is not only nonstationary but exhibits statistical properties that are not known a priori. We then propose an extension of the Minimum Probability of Error (MPE) classifier (a/k/a Minimum Distance Receiver) by pre-processing the received data through a bank of LTV filters before the calculation of each test statistic via the MPE classifier. The proposed augmented MPE classifier is shown to outperform the conventional MPE classifier via simulation.

Some marine mammals as well as bats are known to emit sophisticated waveforms while searching for objects or hunting prey. Some dolphins have been observed to change their sonar pulse depending on the environment. Incorporating these strategies into sonar waveform and receiver design has become an active area of research. In this paper, we explore the application of an optimal waveform design scheme recently given by Kay, to the detection of elastic objects. We examine the benefits of optimal waveform design versus transmitting a linear FM waveform, as well as performance loss suffered by assuming a point target. The optimization approach designs the magnitude spectrum of the transmit waveform and, accordingly, there is an unlimited number of "optimal" transmit waveforms with the same magnitude spectrum. We propose a time domain optimization criterion to obtain the transmit waveform with the optimal magnitude spectrum and the smallest possible duration, as well as the waveform with the optimal magnitude spectrum and the longest possible duration. The former waveform allows for higher ping rates, but necessarily has higher time domain peak power, while the latter waveform has lower time domain peak power and lower ping rates. A method to obtain waveforms that are a blend of these two extremes is also presented, allowing a smooth trade-off between ping rate and peak power.

Idiopathic intracranial hypertension (IIH) is a syndrome of unknown etiology characterized by elevated intracranial pressure (ICP). Although a stenosis of the transverse sinus has been observed in many IIH patients, the role this feature plays in IIH is in dispute. In this paper, a lumped-parameter model is developed for the purpose of analytically investigating the elevated pressures associated with IIH and a collapsible transverse sinus. This analysis yields practical predictions regarding the degree of elevated ICPs and the effectiveness of various treatment methods. Results suggest that IIH may be caused by a sufficiently collapsible transverse sinus, but it is also possible that a stenosed sinus may persist following resolution of significant intracranial hypertension.

Classification of submerged objects has traditionally been performed using high frequency sonars and imaging techniques. While this permits fine matching of target templates to images acquired in the field, HF methods are necessarily limited in range due to absorption of sound by the water. LF sonars, while offering increased detection range, come with some significant challenges related to the limited bandwidth available. Nonetheless, we show that it is feasible to estimate object size using nonimaging techniques. There are a number of low‐frequency phenomena that can be exploited to this end. Among these are edge diffraction in which sharply angled facets of objects (“edges”) act like independent, radiating point sources, and helical waves, which can be set up in cylindrical objects. We show that with appropriate postprocessing of these returns, object edges can be localized thus allowing object extent to be assessed. In this paper, we describe our processing system, and then give results when this syst...