This paper considers the joint design of a MIMO radar with co-located antennas and a MIMO communication system. The degrees of freedom under the designer's control are the waveforms transmitted by the radar transmit array, the filter at the radar array and the code-book employed by the communication system to form its spacetime code matrix. Two formulations of the spectrum sharing problem are proposed. Firstly, the design problem is stated as the constrained maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) at the radar receiver, where interference is due to both clutter and the co-existence structure, and the constraints concern both the similarity with a standard radar waveform and the rate achievable by the communication system, on top of those on the transmit energy. The resulting problem is non-convex, but a reduced-complexity iterative algorithm, based on iterative alternating maximization of three suitably designed sub-problems, is proposed and analyzed. In addition, the constrained maximization of the communication rate is also investigated. The convergence of all the devised algorithms is guaranteed. Finally, a thorough performance assessment is presented, aimed at showing the merits of the proposed approach.

In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.

In this paper, we investigate the spectral coexistence between a multiple-input multiple-output (MIMO) communication system and a MIMO radar which inspects multiple range gates with non-homogeneous interference. We consider two formulations for joint resource allocation, aiming at minimizing the inverse of the harmonic mean of the signal-to-interference-plus noise ratio (SINR) across the range gates inspected by the radar under a constraint on the communication mutual information or at minimizing a properly-weighted difference between the inverse of the harmonic mean of the SINR's and the mutual information, respectively. The degrees of freedom for system design are the radar probing signal, the radar receive filters at each range gate, and the communication codebook, under the usual constraints on the transmit power and on the similarity of the radar waveform to a reference signal. We derive a sub-optimal procedure that allows to decompose the non-convex optimizations at the hand into a sequence of simpler sub-problems. Numerical examples are provided to assess the merits of the proposed solutions.

In this article, in order to improve the performance of the micro inertial measurement unit (MIMU) based on low-accuracy navigation system under the condition of initial large misalignment angle, a nonlinear strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS) integrated navigation estimation algorithm based on the Lie group manifold space is proposed. The proposed nonlinear algorithm is based on unscented Kalman filter (UKF), and its core is to realize the propagation of Lie algebra state error variable sigma points between Lie group space and Lie algebra space through the retraction operation and inverse retraction operation. Meanwhile, the sensor bias state error variable sigma points are always propagated through linear operations in the Euclidean space. Finally, the covariance of the two forms of state error variable is calculated in the Euclidean space, from which the lie algebra state error variable and sensor bias state error variable are estimated through the measurement updating process. The simulation and experimental results show that the proposed algorithm has higher accuracy and faster convergence speed compared with the existing state-of-the-art integrated navigation algorithms, and it has good estimation consistency.

This paper considers the transmit designs in spectral coexistence of multiple-input multiple-output (MIMO) communication and MIMO radar systems. A joint optimization of the communication transmit covariance matrix and the radar transmit waveform is designed to maximize the communication rate, with constraints concerning the similarity with a standard radar waveform and the transmit energies of both the radar and the communication system. The original design is constructed by solving two convex optimization problems. Then an algorithm of alternating iterative procedure is developed, whose solutions are provided in closed form. Simulation results are presented to validate the effectiveness of the proposed algorithm.