Pose is a fundamental attribute of 3D point cloud shape, which profoundly impacts point cloud analysis tasks. However, it is very tricky to directly solve the pose attribute since it is deeply coupled with geometry shape. To this end, the representation separation strategy has been proposed, where the global representation is modeled as a combination of the pose-related part representation and the geometry shape part representation. However, these methods still can not model the representation of the pose attribute well. As a reply, we design a new pose decoder in this paper, learning to disentangle the pose attribute by exploiting its complement,i.e. the geometry shape part representation. Specifically, a Siamese structure is introduced constituting of two shared branches, where two consistent point clouds with different pose attributes are input. The geometry shape part representation and the global representation are learned in each branch network to solving the pose-related part representation for disentangling the pose distribution. Then, we emphasize the completeness and no-redundancy of geometry shape part representation by designing two constraints. 1) We recover the learned geometry shape part representation to a point cloud and enforce it to maintain the same geometry shape as the original input point cloud to guarantee all geometry shape information is retained. 2) We develop two geometry shape part representations embedded from two branches to be the same so as to filter the pose information out. These two constraints are incorporated into the unsupervised loss function to train our pose decoder. Our pose decoder can be integrated into different point cloud shape analysis methods. We evaluate our pose decoder in point cloud classification and part segmentation tasks to handle the pose diversity problem of the input point cloud, which significantly improves the robustness. Besides, the obtained respective poses of input point clouds can be used to register them naturally, making the unsupervised method achieving superior performance.

This paper considers the joint design of the transmit waveform and receive filter for airborne multiple-input multiple-output (MIMO) radar under spectral constraint in signal-dependent clutter. The spatial-frequency spectral compatibility constraint is imposed in the joint design problem. To tackle the non-convex joint design problem, we develop an iterative algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). The proposed algorithm can handle the non-convex terms by the convex approximation. Simulation results demonstrate the superiority of the proposed algorithm in terms of better signal-to-interference-plus-noise ratio (SINR) and better spectral compatibility ability.

This letter deals with the joint design of transmit waveform and receive filter bank for airborne multiple-input multiple-output (MIMO) radar under the target uncertainties. Assuming that the spatial angle and the Doppler frequency of the target are unknown, we formulate the maximin joint design problem by maximizing the worst-case signal-to-interference-plus-noise ratio (SINR) under the energy constraint, flexible modulus constraint, and similarity constraint on the transmit waveform. To tackle this problem, we develop a computationally efficient algorithm based on iterative feasible point pursuit successive convex approximation (FPP-SCA). Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm.

Diversity smoothing has been widely developed for angle estimation with bistatic multiple input multiple output (MIMO) radar in the presence of coherent targets, the parameter identifiability of which is an important issue. In this paper, we are devoted to establishing more accurate conditions by studying the positive definiteness of smoothed target covariance matrix. The antenna numbers of transmit and receive arrays are derived as functions of the target number and target structure. We show that the new results improve upon previous ones and recover them in special cases. Simulation results are presented that corroborate our theoretical findings.

Space-time adaptive processing (STAP) based on sparse recovery (SR-STAP) techniques exhibits significantly better performance than conventional STAP algorithms within a very small number of snapshots. However, when the clutter patches do not locate exactly on the discrete space-time grid points, the performances of SR-STAP algorithms degrade severely. In this letter, a low-complexity off-grid STAP algorithm based on local search clutter subspace estimation is proposed to overcome this issue. In the proposed algorithm, the global atoms are first selected from the reduced-dimension global STAP dictionary using the design selection criterion. Then, the optimal atoms are searched from the local STAP dictionary. Finally, these space-time steering vectors corresponding to the optimal atoms are used to construct the clutter subspace iteratively, and the STAP weight is obtained by projecting the snapshot on the subspace orthogonal to the clutter subspace. Numerical experiments with both simulated and Mountain-Top data are carried out to demonstrate the effectiveness of the proposed algorithm.