Spectrally-shaped random FM (RFM) waveforms have recently been shown to greatly expand radar design freedom, provide good spectral containment, and are amenable to physical implementation in high-power transmitters. A key design factor involves ways of enforcing a Gaussian spectral density via optimization, which leads to a Gaussian autocorrelation that theoretically has no range sidelobes. However, the gradual roll-off for this spectrum means that, for fixed transmitter bandwidth, the passband component must be narrower than for classical linear FM (LFM), thereby trading achievable range resolution. Here we examine the impact of using the family of super-Gaussian spectra to serve as alternative design templates. Using the temporal template error (TTE) RFM design scheme to generate physical waveforms in this context, radar performance trade-offs are examined to assess practical viability.

The radio-frequency (RF) electromagnetic spectrum, extending from below 1 MHz to above 100 GHz, represents a precious resource. It is used for a wide range of purposes, including communications, radio and television broadcasting, radionavigation, and sensing. Radar represents a fundamentally important use of the electromagnetic (EM) spectrum, in applications which include air traffic control, geophysical monitoring of Earth resources from space, automotive safety, severe weather tracking, and surveillance for defense and security. Nearly all services have a need for greater bandwidth, which means that there will be ever-greater competition for this finite resource. The paper explains the nature of the spectrum congestion problem from a radar perspective, and describes a number of possible approaches to its solution both from technical and regulatory points of view. These include improved transmitter spectral purity, passive radar, and intelligent, cognitive approaches that dynamically optimize spectrum use.

Low probability of intercept (LPI) communication generally relies on the presence of noise to obfuscate a covert signal through the use of spectral spreading or hopping. In contrast, this paper addresses the use of ambient interference from other man-made emissions as a means to mask the presence of covert communication. Specifically, the high power, wide bandwidth, and repeating structure of pulsed radar systems provide an advantageous framework within which to embed a communication signal. The operating paradigm considered here is that of an RF tag/transponder that is illuminated by the radar and intends to covertly communicate with the radar or some other desired receiver while being masked by the ambient radar backscatter to avoid detection by an intercept receiver. Communication takes place on an intra-pulse (or individual pulse) basis to maximize the data rate. The impact of multipath, and its exploitation using time reversal to achieve spatio-temporal focusing, is considered. The processing gain for the destination receiver and intercept receiver are derived analytically and subsequently used to optimize the parameterization of communication symbol design.

This paper proposes an iterative algorithm for multiuser detection in uncoded synchronous code division multiple access communication systems based upon an approximate maximum a posteriori (MAP) formulation. For symbol estimation in the continuous domain, direct use of the a priori symbol distribution in the estimation process is prohibited because it is not differentiable. The proposed algorithm approximates the discrete finite alphabet symbol distribution by a sum of continuous Gaussian distributions centered at the true values of the symbol constellation. This approximation allows the development of a gradient-based iterative MAP estimator that employs the structure of the particular symbol constellation to improve estimation accuracy. Although iterative, the proposed method does not use a tentative solution from another multiuser detector. Simplifications of the proposed algorithm for constant modulus modulation and its special forms for M-ary phase-shift keying are given. Also, an optimum step size is derived to achieve fast convergence. The performance of the proposed algorithm is shown to outperform other well-known multiuser detectors such as minimum mean square error, the decorrelator, and the multistage hard-decision parallel interference canceller, especially for the near/far scenario.

The paper generalizes our previous work on BPSK (Blunt, S.D. and Ho, K.C., Proc. IEEE ICASSP, p.2313-16, 2002) to multiuser detection for constant modulus constellations in synchronous CDMA communication systems based on maximum a posteriori (MAP) estimation. The known finite alphabet of discrete transmitted symbols is approximated as a stochastic parameter with Gaussian distributions centered at the true values of the symbol constellation. This allows for the development of a MAP estimator that takes a priori knowledge of the symbol constellation into account in order to improve estimation accuracy. We examine the performance of the proposed algorithm for BPSK, QPSK, and 8PSK and compare the results with other well-known multiuser detection techniques.

This paper proposes an iterative algorithm for multiuser detection in synchronous CDMA communication systems based on maximum a posteriori (MAP) estimation. This approach performs better than the maximum likelihood (ML) estimator due to the use of probabilistic knowledge regarding the distribution of the received signal amplitudes. We assume that for every user the PDF of the signal level value in a bit time is the average of two Gaussian distributions centered at plus and minus the mean signal amplitude. The result is a gradient-based algorithm containing a ML term that determines the “direction” of bit estimation convergence, and a prior knowledge term that improves the accuracy of the bit estimate. Due to the prior knowledge term, the algorithm reaches a minimum closer to the true received bit values and therefore realizes more accurate bit estimates and a lower bit error rate (BER) than ML alone.

This paper considers the problem of adaptive identification of a sparse system such as in echo cancellation application. A sparse system has a small number of coefficients that are different from zero. We propose the use of the Haar transform to locate non-zero coefficients such that only the non-zero coefficients will need to be adapted. The consequence is an increase in convergence speed and a decrease in computation. The performance of the proposed algorithm is evaluated through simulations.