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Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error.

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Three stage in-flight alignment method which estimates error state vector of a strapdown inertial navigation system(SDINS) based on the GPS is newly presented. The three stage in-flight alignment consists of a coarse horizontal alignment, coarse azimuth alignment, and adaptive Kalman filter which shapes error covariances in real time. In the coarse horizontal alignment stage, the horizontal attitude is estimated using the acceleration differences between a SDINS and GPS as damping coefficients. In the coarse azimuth alignment stage, the filter composed of heading increments of a SDINS and GPS tracking angle solves the SDINS heading. At the last stage, the adaptive Kalman filter estimates the optimal error covariances and compensates the error state variables. With this three stage in-flight alignment, it is proposed that the performance enhancement of in-flight alignment and stability increment of filter can be achieved. The error covariance of an adaptive Kalman filter is divided into two parts. The one is the periodically updated covariance starting with the fixed initial statistical values, and the other is statistical weighed gain which is calculated to minimize the cost function of an error covariance derived from measurement error residuals. Errors from the statistical process and measurement noise affect directly the error covariance. So they cause an estimation accuracy and stability degradation. This paper also shows that the estimation precision degree and stability of the filter can be improved by compensating the displacement of an error covariance.

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