In order to solve the sensitive eavesdropping problem outside the target region, we propose a novel sidelobe randomization method called random frequency offset increment combination technique (RFOICT) based fixed region beamforming (FRB) using frequency diverse subarray. The FRB-based RFOICT consists of a conventional phased-array followed with frequency offset modulate at each branch. The main idea of FRB-based RFOICT is to generate an angle-and-range double confined beam and convert the leakage signal in the sidelobe into the artificial noise by exploiting the degree of freedom of frequency while keeping the mainlobe a constant. The statistical features of mean and variance of the received beampattern gain is summarized using a simplified statistical model. An expression of secrecy rate is derived as a function of the frequency offset increment. Simulation results show that the proposed FRB-based RFOICT provides robust PLS performance with lower transmission power compared with the current sidelobe randomization techniques.

A sparse partial update (SPU) algorithm and its improved version improved SPU (ISPU) algorithm, are proposed in this paper for sparse system identification. The SPU first categorizes its filter coefficients into active and inactive coefficients. Then all the active coefficients are included in each adaptation, while only a small portion of the inactive coefficients is periodically chosen to be included in the adaptation. The SPU emphasizes convergence of the active coefficients by updating them at every adaptation, while the periodical adaptation of the inactive coefficients ensures its robustness and tracking capability. By eliminating most of the inactive coefficients from adaptation, the SPU significantly reduces the number of adapting coefficients in each adaptation. The decline in the number of adapting coefficients eventually leads to improvement in both computation and convergence speed. To avoid performance degradation in the case of identifying a dispersive system, an ISPU is further proposed by making modifications to the SPU. The simulation results demonstrate that the ISPU not only outperforms other sparse adaptive algorithms in identifying a sparse system but also performs at least as well as the NLMS algorithm in identifying a dispersive system.

A novel scheme is proposed to locate the dispersive region, whose location is essential for parallel echo cancellation. In the scheme, a first filter adapts to a subsampled version of the input signal to roughly identify the impulse response. After each adaptation, a squaring function and a moving window integration procedure are performed on the first filter, and the region with the maximum integration value is considered to be the dispersive region. Finally, a second short filter is used to precisely identify the active coefficients belonging to the located dispersive region to implement the actual echo cancellation. Simulation results suggest that the parallel structure improves its convergence speed 3 times and its computation can be reduced by 3/8 compared with the traditional NLMS algorithm by decreasing the filter length. Due to the more accurate estimate of the location, the misalignment noise of the proposed algorithm is at least 10 dB lower than that of the conventional dispersive region locating algorithm. Moreover, the proposed algorithm based on the parallel structure outperforms other sparse adaptive algorithms in all aspects.