Gain and phase uncertainties would destroy the invariance property of both the transmit array and the receive array in bistatic MIMO radar, so the computationally efficient ESPRIT algorithm cannot be applied directly. Proposed is a novel ESPRIT-like algorithm, which uses the instrumental sensors method (ISM), to estimate the direction of departures and direction of arrivals. The ESPRIT-like algorithm is able to achieve favourable and unambiguous angle estimation without any information of the gain and phase uncertainties. The effectiveness of the proposed algorithm is verified by simulation results.Introduction: Multiple-input multiple-output (MIMO) radar is characterised by using multiple antennas to simultaneously transmit orthogonal waveforms and multiple antennas to receive the reflected signals [1]. Direction of departure (DOD) and direction of arrival (DOA) have been recently investigated in [2][3][4]. All of them are based on the assumption that the transmit array and receive array steering vector are exactly known corresponding to the array geometry. But the transmit array and receive array steering vector cannot be obtained precisely when there exist gain and phase uncertainties. Therefore, the performance of the algorithms proposed in [2 -4] will seriously degrade. To deal with the problem of gain and phase uncertainties of the transmit array and the receive array, a novel ESPRIT-like angle estimation algorithm is proposed in this Letter. As there is not requirement of space searching or iterative procedure, the computational complexity is low.

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Inverse synthetic aperture radar (ISAR) object detection is one of the most important and challenging problems in computer vision tasks. To provide a convenient and high-quality ISAR object detection method, a fast and efficient weakly semi-supervised method, called deep ISAR object detection (DIOD), is proposed, based on advanced region proposal networks (ARPNs) and weakly semi-supervised deep joint sparse learning: 1) to generate high-level region proposals and localize potential ISAR objects robustly and accurately in minimal time, ARPN is proposed based on a multiscale fully convolutional region proposal network and a region proposal classification and ranking strategy. ARPN shares common convolutional layers with the Inception-ResNet-based system and offers almost cost-free proposal computation with excellent performance; 2) to solve the difficult problem of the lack of sufficient annotated training data, especially in the ISAR field, a convenient and efficient weakly semi-supervised training method is proposed with the weakly annotated and unannotated ISAR images. Particularly, a pairwise-ranking loss handles the weakly annotated images, while a triplet-ranking loss is employed to harness the unannotated images; and 3) to further improve the accuracy and speed of the whole system, a novel sharable-individual mechanism and a relational-regularized joint sparse learning strategy are introduced to achieve more discriminative and comprehensive representations while learning the shared- and individual-features and their correlations. Extensive experiments are performed on two real-world ISAR datasets, showing that DIOD outperforms existing state-of-the-art methods and achieves higher accuracy with shorter execution time.