The UWB (ultra-wideband) pulse radar is a promising candidate as an environment measurement method for rescue robots. Radar imaging to locate a nearby target is known as an ill-posed inverse problem, on which various studies have been done. However, conventional algorithms require long computational time, which makes it difficult to apply them to real-time operations of robots. We have proposed a fast radar imaging algorithm, the SEABED algorithm, for UWB pulse radars. This algorithm is based on a reversible transform, BST (Boundary Scattering Transform), between the target shape and the observed data. This transform enables us to estimate target shapes quickly and accurately in a noiseless environment. However, in a noisy environment the image estimated by the SEABED algorithm is degraded because BST utilizes differential operations. We have also proposed an image stabilization method, which utilizes the upper bound of the smoothness of received data. This method can be applied only to convex objects, not to concave ones. In this paper, we propose a fractional BST, which is obtained by expanding the conventional BST, and an image stabilization method by using the fractional BST. We show that the estimated image can be stabilized regardless of the shape of target.

Imaging techniques for robots are important and meaningful in the near future. Pulse radar systems have a great potential for shape estimation and locationing of targets. They have an advantage that they can be used even in critical situations where optical techniques cannot be used. It is thus required to develop high-resolution imaging algorithms for pulse radar systems. High-resolution imaging algorithms utilize the carrier phase of received signals. However, their estimation accuracy suffers degradation due to phase rotation of the received signal because the phase depends on the shape of the target. In this paper, we propose a phase compensation algorithm for high-resolution pulse radar systems. The proposed algorithm works well with SEABED algorithm, which is a non-parametric algorithm of estimating target shapes based on a reversible transform. The theory is presented first and numerical simulation results follow. We show the estimation accuracy is remarkably improved without sacrificing the resolution using the proposed algorithm.

Environment measurement is an important issue for various applications including household robots. Pulse radars are promising candidates in a near future. Estimating target shapes using waveform data, which we obtain by scanning an omni-directional antenna, is known as one of ill-posed inverse problems. Parametric methods such as Model-fitting method have problems concerning calculation time and stability. We propose a non-parametric algorithm for high-resolution estimation of target shapes in order to solve the problems of parametric algorithms.