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access icon free Frequency-hopping signals sorting based on underdetermined blind source separation

© The Institution of Engineering and Technology
Received 10/12/2012, Accepted 28/06/2013, Revised 10/05/2013, Published

Most of the earlier approaches for frequency-hopping (FH) signals sorting with a single sensor do not adapt to synchronous network, whereas the multiple-sensor-based algorithms request the number of sensors must be more than that of signals. Since the number of sensors is limited in many practical applications, it is important to sort synchronous or asynchronous FH networks with as little as possible sensors. This study introduces the underdetermined blind source separation (UBSS) algorithm to solve this problem. Considering the time–frequency (TF) sparsity of FH signals, the problem is formulated as one of UBSS based on sparse TF representation. First, an improved k-means clustering algorithm is developed to estimate the mixing matrix, according to the TF properties of FH signals. Secondly, to separate more signals overlapped in the TF domain using given sensors, an improved subspace-based algorithm utilising the information of the comparative power is proposed. In the proposed method, the sparsity condition of the sources in the TF domain is relaxed, and the number of FH signals that exist at any TF point simultaneously is allowed to equal that of the sensors. Simulations demonstrate that the proposed method can separate FH signals efficiently and outperforms the previous methods.

Inspec keywords: estimation theory; frequency hop communication; blind source separation

Other keywords: time-frequency domain; mixing matrix estimation; comparative power; K-means clustering algorithm; frequency hopping signal; UBSS algorithm; source sparsity condition; time-frequency sparsity; underdetermined blind source separation; time-frequency point; sparse time-frequency representation

Subjects: Other topics in statistics; Multiple access communication; Radio links and equipment; Signal processing and detection

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