Signal-dependent Interference Research Articles

Waveform design in signal-dependent interference and application to target recognition with multiple transmissions

The authors present illumination waveforms matched to stochastic targets in the presence of signal-dependent interference. The waveforms are formed by SNR and mutual information (MI) optimisation. We also use these waveforms in cognitive radar (CR) target identification application. In this application, the radar system attempts to identify a deterministic or random target using multiple transmissions. These transmissions are adaptively modified in response to previously received echoes. In addition, the authors present a new multi-band application of the CR platform.

Optimal transmit-receiver design in the presence of signal-dependent interference and channel noise

Optimal detection of a target return contaminated by signal-dependent interference, as well as additive channel noise, requires the design of a transmit pulse f(t) and a receiver impulse response h(t) jointly maximizing the output signal to interference plus noise ratio (SINR). Despite the highly nonlinear nature of this problem, it has been possible to show that f(t) may always be chosen minimum-phase. A full analysis concludes with the construction of an effective numerical procedure for the determination of optimal pairs (f,h) that appears to converge satisfactorily for most values of input SINR. Extensive simulation reveals that the shape of f(t) can be a critical factor. In particular, the performance of a chirp-like pulse is often unacceptable, especially when clutter and channel noise are low-pass dominant and comparable.

A theoretical study of linear and nonlinear equalization in nonlinear magnetic storage channels

We present methods to systematically design a feedforward neural-network detector from the knowledge of the channel characteristics. Its performance is compared with the conventional linear equalizer in a magnetic recording channel suffering from signal-dependent noise and nonlinear intersymbol interference. The superiority of the nonlinear schemes are clearly observed in all cases studied, especially in the presence of severe nonlinearity and noise. We also show that the decision boundaries formed by a theoretically derived neural-network classifier are geometrically close to those of a neural network trained by the backpropagation algorithm. The approach in this work is suitable for quantifying the gain in using a neural-network method as opposed to linear methods in the classification of noisy patterns.

Error probabilities on fading channels with intersymbol interference and noise

Sums of infinite sequences of weighted binary random variables arise in communications problems involving signal-dependent interferences. In many cases of practical importance, the distribution functions of these sums are singular and often of Cantor type; they are continuous but do not have a density function. For this reason, special methods of calculating expectations are needed. Results of this type are derived. The method is used to compute error probabilities for differential detection of minimum shift keying, and for noncoherent detection of frequency shift keying. In each case the model assumed is a Rician fading channel. >

Decision threshold optimization for binary transmission in the presence of signal dependent noise and distortion

An expression is derived for the optimum binary decision threshold in the presence of signal dependent intersymbol interference (isi) and/or distortion, and Gaussian noise. The expression obtained for the threshold is more generally applicable than that provided by inner-eye or Gaussian approximations and is more appropriate in some circumstances in defining tighter bounds on error probability and thus on system performance.

A Class of Distribution-Free Detectors for Pulse Amplitude Modulated Signals

A class of distribution-free detectors for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</tex> -ary pulse amplitude modulated digital signals in the presence of certain signal dependent interference and additive noise of unknown distribution is formulated, evaluated in terms of probability of error and compared to a matched filter detector in Gaussian noise. The noise distribution is unknown except that it satisfies certain broad assumptions and that its value at the median is known. A decision method based on the value of a test statistic is developed. An analytical expression for the average probability of error and a method of calculating the optimum decision thresholds that minimize this probability of error are presented. The probability of error is independent of the form of the noise distribution but depends on the value of the noise density function at the median. An example of a ternary signal set in white Gaussian noise is worked out for illustration. The optimum decision thresholds are calculated and the resulting average probability of error is plotted as a function of signal-to-noise ratio. Average probability of error of a matched filter detector is also evaluated under similar conditions and a comparison is made with that of the proposed detector.