It is demonstrated through simulations that a combination of the vector auto-regressive (VAR) filtering technique and a maximum likelihood (ML) parameter estimation method is an effective means for carrying out clutter suppression and parameter estimation, and being more robust against system mismatches than conventional displaced-phase-centre-antenna (DPCA) processing.

A new automatic target recognition (ATR) scheme based on oncdimensioaal high range resolution profiles is presented, which features joint range alignment and classification This meThod CKI also be used to form mean-or eigen-templates to fianher improve the ATR performance Experimental results are provided to demonstcate the performance of the new algorithm.

Proximity ranging is very important in many applications. Ultrasonic sensors have proven to be very cost effcctive tools for this purpose. Most of the currently available time-of-flight based acoustic proximity ranging systems use the conventional matched filter based time delay estimation approaches to measure short distances between prox-irnity objects. However, in the presence of strong and closely spaced secondary echoes, the aforernentioned matched filter based algorithms tend to fail or suffer from severe pertformanee degradations due to their poor resolutions. In this paper, a computation-ally efficient time delay estimation algorithm, referred to as Multi-PEARS (Multi-echo Parameter Estimation for Acoustic Ranging Systems), is described for the joint proxim-ity range estimation and secondary echo mitigation. The effectiveness of the proposed algorithm is demonstrated by both numerical and experimental examples.

To control high speed underwater vehicles, a proximity ranging system is needed to monitor the cavity thickness. In this paper, we study a time-of-ight (TOF) principle based acoustic proximity ranging system. By taking into account the acoustically hard boundary at the air-water interface, werst present a two-stage computationally e�cient time delay estimation algorithm, referred to as the PEARS (Parameter Estimation for Acoustic Ranging Systems) algorithm, which is applicable to arbitrary transmitted waveforms. Numerical results based on a simulated waveform demonstrate that the PEARS estimates can approach the Cramer-Rao bound as the signal-to-noise ratio increases. We then present experiments performed by using commercially available acoustic transducers to further verify our method. To update TOF estimates quickly, a specially designed continuous wave (CW) is applied to the transducer. Experimental results show that PEARS can achieve high measurement accuracy for ranging distances less than 100mm with an achievable parameter update rate of approximately 1.5kHz.