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In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction.

A new method for measuring the pressure reflection coefficient in a buffer rod configuration is presented, together with experimental results for acoustic measurements of the liquid density, based on the measurement of the liquid's acoustic impedance. The method consists of using 2 buffers enclosing the liquid in a symmetrical arrangement with a transducer fixed to each buffer. One of the transducers is used in a pulse-echo mode while the other transducer operates as a receiver. The echo amplitudes leading to the pressure reflection coefficient as found by this method possess advantages such as reduced attenuation due to a shorter liquid transmission path and reduced interference, as compared with the ABC method. Measurements with distilled water and with special density calibration oil qualities have been performed using both the new method and the ABC method and are shown for the new method to give a density span within +/- 0.15% of the reference values. A comparison of the measured densities based on both a time-domain and a l(2)-norm frequency domain integration signal processing approach is given, along with a recommendation as to how the signal processing should be performed.

High peak-to-average-power ratio (PAPR) is a particular disadvantage of orthogonal frequency-division multiplexing (OFDM) systems and can significantly degrade the power efficiency at the transmitter. The aim of this paper is to propose a post-inverse fast Fourier transform (IFFT) PAPR reduction method based on intelligent linear symbol combining with the objective to minimize the PAPR at the transmitter. Unlike the methods so far reported in the literature, the proposed method has a relatively low complexity and does not require external randomization sequences. This paper will also investigate the impact of the proposed method on the system's bit error rate (BER) performance and provide a closed-form analytical expression that takes into account the BER dependency on the side information bits and multiblock combination. It will be shown that the proposed technique can provide significant PAPR reductions at manageable levels of signal processing requirements while the BER degradation is relatively small. © 2008 IEEE.

This paper presents a distributed approach for time- domain focusing, which significantly enhances the overall efficiency by distributing the computational load across a (potentially large) number of networked computers. The system described includes the so-called master, responsible for pre-processing, the distribution of fragments of raw-data data and the collection of processed image fragments. Fragments of raw-data are passed to so-called slaves, any number of which can be connected to the master, which are responsible for the focusing itself. Master and slave actively communicate over the network to organise the entire process in a scalable manner. In this way, time-domain processing can be accelerated by a factor that is virtually linear in the number of participating slaves. This paper summarises the current status of software development, realised in a platform-independent way using the IDL and Java languages. Additionally, some preliminary evaluations of performance, scalability and the required network infrastructure are given. Some examples of SAR data, acquired by the airborne sensor E-SAR of DLR, and processed with the system described are shown.