For jointly estimating angle-Doppler information, two-dimensional MUSIC is often applied, which requires an exhaustive grid-search over angle-Doppler domain. To mitigate the computational complexity and enhance the estimation performance we modify two-dimensional MUSIC in three steps. First, instead of using two-dimensional spectral search, it is converted to two consecutive polynomial root-finding problem. Second, the degree of the polynomial in the first root-findingstage is undesirably high in general, hence we approximate it to a lower-order polynomial through DFT. Lastly further enhancement can be achieved by having the closest approximation of null spectrum function by assigning unequal weight coefficients. Cramer-Rao-bound is derived for joint angle-Doppler-estimation as a benchmark in our performance analysis.

Radar mounted on a moving vehicle returns timevarying detections corresponding to unstable scattering points, unlike optical sensors which produce relatively stable detections. We present an efficient extended Kalman filter-based simultaneous localization and mapping (EKF-SLAM) algorithm for radar, utilizing new techniques of clustering and sifting the timevarying detections. Velocity bias and yaw rate bias, which are inherent in any odometer are also estimated and compensated using the same EKF. For theoretical performance evaluation, the posterior Cramér-Rao bound (PCRB) for SLAM with odometer bias estimation is derived and compared with the root mean squared errors (RMSEs), with and without bias estimation. The simulation results show that the RMSE for SLAM with bias estimation is the closest to the PCRB. The proposed algorithm is also verified experimentally with field data. The comparison with the ground truth trajectory obtained from a differential global positioning system (DGPS) shows that the proposed algorithm yields an accurate trajectory estimate, even under large odometer bias. Also, the real-time capability of the proposed algorithm is verified.

To improve target tracking performance, it is necessary not only to model target dynamics well, but also to employ high accuracy sensors. In addition to this, the geometry between the sensors and the target also significantly affects the tracking performance. In this letter, we give a closed-form formula for the optimal range sensor placement problem, by minimizing the errors both in the position and velocity estimates when there are two sensors. Using the formula, the effects of errors in a prior position and velocity estimates of a target and tracking filter parameters on the optimal sensor placement are analyzed.

Previous researches regard the signals from other users as interfereces to be avoided in wireless ad-hoc network systems (e.g, IEEE 802.11/16). However, if the purpose of ad-hoc communications is relaying information from one node to another node through the third node, we can apply the network coding (NC) at the physical layer to increase a capacity by regarding a received signal from different nodes as one added signal. In this papar, we combine NC for MIMO relay systems with a linear pre-equalizer. For examples, zero-forcing (ZF) or lattice reduction-aided ZF pre-equalizers is used at each transmitter. We have evaluated the proposed system by comparing uncoded BER performance with those of several one-way relaying scheme focused on phase 1 mainly, because the overall performance is upperbounded by that of phase 1. The proposed system increases a multiplexing gain twice, maintaining a same diversity order, compared to one-way relaying. Finally, we combine the lattice encoder and the decoder to achieve an optimal capacity of the proposed scheme.

Direction of arrival (DOA) in MIMO radar is estimated from the phase differences between its channels. Hence, the phase difference vector, called a virtual array response vector, needs to be accurately known. We propose a new model of the virtual array response with a wide-band interleaved orthogonal frequency-division multiplexing (OFDM) signal. The angle is estimated by maximum likelihood estimation (MLE), and we show that the estimation error is lower than the one when using the systematic phase correction method which compensates for the range-dependent phase term. Also, the root mean square error (RMSE) of angle estimation with the proposed method is closer to the Cramér-Rao bound (CRB).