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When overlaying spread spectrum (SS) transmission over a narrowband system, the performance will be significantly degraded by the narrowband signal. This paper presents an adaptive predictor approach for narrowband interference suppression in SS codedivision multiple-access (CDMA) systems. Two adaptive methods are derived, one uses linear predictor and the other employs nonlinear predictor. They both use a predictor to estimate the interference which is then subtracted from the received signal to improve performance. The proposed adaptive methods are blind in the sense that they do not require training data. The proposed methods not only provide faster convergence speed than the case without using a predictor, but also give better BER performance. At a BER of 10 −4 and SIN R = -20dB, the proposed methods yield 1.4dB and 2.4dB improvement in SN R(E b /N0) respectively.

Uncertainty in sensor(receiver) locations can seriously de-general case that both the source and the sensors are moving is teriorate the source localization accuracy. A localization algorithm needs considered. The proposed method jointly estimates both the position to take the sensor location errors into account in order to improve and velocity of the source, as well as the positions and velocities of source location estimate. This paper considers the localization for the position and velocity of a moving source using the time differences the sensors simultaneously. The estimates of the sensors' positions of arrival (TDOAs) and frequency differences of arrival (FDOAs) of a and velocities can help to provide a more accurate estimate of the signal received at a number of sensors that have random location errors. source position and velocity. The simulation results show that with a The proposed method is based on the Taylor-series method that jointly good initial guess to the true solution, the proposed method achieves estimates both the source and sensor locations simultaneously. Simulation results show that with a reasonable initial guess close to the true solution to begin with, the proposed method converges to the true solution and as well as the sensors. achieves the CRLB accuracy for both near-field and far-field sources.The paper is organized as follows. Section 2 provides the location scenario and introduces the symbols used. The proposed solution in