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Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of non-cooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This paper proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). Firstly, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e. to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the non-adjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly non-ideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multi-variate input conditions.

In complex electromagnetic environments, the challenge of signal sorting task for multi-function radars (MFRs) with various work modes has arisen. The previous methods are prone to cause the so-called “increasing batch” problem, which means that the work modes of one MFR may be sorted into multiple emitters. In this letter, a MFR signal sorting method based on complex network is proposed to tackle the problem mentioned above. The novel method utilizes limited penetrable visibility graph to construct the network from interleaved radar pulse sequences, then employs label propagation algorithm and density peak clustering to detect community structures, thus fulfilling deinterleaving of pulses from several MFRs. Simulation results show that the proposed method is effective to alleviate the “increasing batch” problem, and is also robust under non-ideal conditions.

In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receiving signals in both time and frequency domain. In this letter, a novel Multi-Instance Multi-Label learning framework based on Deep Convolutional Neural Network (MIML-DCNN) is proposed to automatically recognize the overlapping LPI radar signals,which is trained by single type of signals only. The framework handles signals in an end-to-end manner that is integrated with a well-designed instance generation module, a sophisticated MIML classifier, and an adaptive threshold calibration. Through comprehensive experiments on simulated overlapping signals with four different modulation types, we prove that the proposed framework identifies each individual signal type precisely in the presence of overlapping signals, and is also robust to variation of the signal-to-noise ratio (SNR) and power ratio conditions.

Traditional monopulse radar cannot resolve two targets present in one range and Doppler cell by means of the monopulse ratio. A novel algorithm is proposed to estimate the directions of two steady targets with two pulses. The algorithm has a closed-form expression and its variance is derived at high signal-to-noise ratios (SNRs). Furthermore, the pulse pair selection criterion and the estimation method with multiple pulses are given. Finally, some numerical results are shown to validate the proposed algorithm and the effect of slight target fluctuations is tested.

A 3-D ring oscillator integrated with through silicon vias (TSVs) is designed and fabricated for testing multilayer-stacked integrated circuits (ICs) with TSV. The proposed 3-D ring oscillator consists of 13 stages and 65-nm Complementary Metal-Oxide-Semiconductor Transistor (CMOS) dies with two current-starved inverters and via-last TSVs designed for the five middle layers of the 3-D ring oscillator. The two cascaded inverters are connected to the upside layer through a TSV and to the downside layer through a microbump. One chip with two inverters but without TSV is stacked in the top layer of the 3-D ring oscillator to realize the ring oscillator loop, and one logic chip with one inverter and via-middle TSVs are in the bottom of the ring oscillator. The characteristics of via-last and via-middle TSVs in the 3-D ring oscillator are analyzed based on the equivalent circuits. The oscillation frequency responses of the designed 3-D ring oscillator are measured finally to verify the design concept, and to assess the performance of the 3-D ring oscillator. The measured results demonstrate that the proposed 3-D ring oscillator is an attractive candidate for testing the stacked 3-D IC, and the effect of TSVs dominates the delay of the 3-D ring oscillator.