ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.

Our goal is to use a single passive moving sensor to determine the locations of multiple radio stations. The conventional method uses only direction-of-arrival (DOA) measurements, and its performance is poor when emitters are located closely in the lateral direction, even if they are not close in the range direction, or in the far field from the moving sensor, resulting in similar DOAs for several emitters. This paper proposes a new method that uses the power of the received signals as well as DOA. The received signal power is a function of the inverse of the squared distance between an emitter and the moving sensor. This has the advantage of providing additional information in the range direction; therefore, it can be used for data association as additional information when emitter ranges are different from each other. Simulations showed that the success rate of the conventional method is 73%, whereas that of the proposed method is 97%, an overall 24-percentage-point improvement. The localization error of the proposed method is also reduced to half that of the conventional method. We further investigated its performance with different emitter and sensor configurations. In all cases, the proposed method proved superior to the conventional method.

In this study, we propose a method for localizing an unknown moving emitter by measuring a sequence of the frequency-of-arrival using a single moving observation platform. Furthermore, we introduce the position and velocity errors of the moving observation platform into the theoretical localization error equation to analyze the effect of these errors on the localization accuracy without Monte-Carlo simulations. The proposed theoretical error equation can propagate toward the time direction; therefore, the theoretical localization error can be evaluated at an arbitral time. We demonstrate that the localization error value obtained by the proposed equation and the RMSE evaluated by the Monte-Carlo simulation sufficiently coincide with one another.

Temporal Decorrelation source SEParation (TDSEP) is a blind separation scheme that utilizes the time structure of the source signals, typically, their periodicities. The advantage of TDSEP over non-Gaussianity based methods is that it can separate Gaussian signals as long as they are periodic. However, its shortcoming is that separation performance (SEP) heavily depends upon the values of the time shift parameters (TSPs). This paper proposes a method to automatically and blindly estimate a set of TSPs that achieves optimal SEP against periodic Gaussian signals. It is also shown that, selecting the same number of TSPs as that of the source signals, is sufficient to obtain optimal SEP, and adding more TSPs does not improve SEP, but only increases the computational complexity. The simulation example showed that the SEP is higher by approximately 20dB, compared with the ordinary method. It is also shown that the proposed method successfully selects just the same number of TSPs as that of incoming signals.