Time-interleaved analog-to-digital converters can significantly increase system sampling rate, but channel mismatches, such as gain mismatch, offset mismatch and phase mismatch, can cause great error in the system. The problem of broadband mismatches calibration for multichannel time-interleaved analog-to-digital converters system will be discussed in this paper by using the foreground calibration technique. Essentially, the calibration can be divided into two steps: First, all the mismatches between analog input and digital output will be measured by taking a linear frequency modulated signal as the reference signal; Next, a set of finite-impulse response filters will be calculated based on those mismatches to generate frequency dependent mismatches compensation, so that the signal to noise and distortion ratio, and spurious-free dynamic range will not be limited by the channel mismatches. There are two advantages of the proposed method when compared with previous methods. The complexity of error estimation can be reduced dramatically and high precision error estimation can also be achieved. In order to evaluate the performance, a series of experiments were conducted in a two-channel time-interleaved analog-to-digital converter system. The results show that the spurious-free dynamic range is improved by more than 10dB.

This paper investigates the design of waveforms for multiple-input multiple-output (MIMO) radar systems that can exploit multipath returns to enhance target detection performance. By making reasonable use of multipath information in the waveform design, MIMO radar can effectively improve the signal-to-interference and noise ratio (SINR) of the receiver under a constant modulus (CM) constraint. However, optimizing the waveform design under these constraints is a challenging non-linear and non-convex problem that cannot be easily solved using traditional methods. To overcome this challenge, we proposed a novel waveform design method for MIMO radar in multipath scenarios based on deep learning. Specifically, we leveraged the powerful nonlinear fitting ability of neural networks to solve the non-convex optimization problem. First, we constructed a deep residual network and transform the CM constraint into a phase sequence optimization problem. Next, we used the constructed waveform optimization design problem as the loss function of the network. Finally, we used the adaptive moment estimation (Adam) optimizer to train the network. Simulation results demonstrated that our proposed method outperformed existing methods by achieving better SINR values for the receiver.

Hybrid Filter Bank (HFB) is widely used in Frequency-Interleaved analog-to-digital converter, but traditional HFB structures suffer various degrees of imperfections. This paper presents a new-structure HFB based on power complementary pair. The proposed architecture only needs M-1 analog analysis filters in M-channels Frequency-interleaved analog-to-digital converter and the analog analysis filters can satisfy the pairwise power complementary property. Therefore, the new structure simplifies analog circuits to a larger extent compared with traditional structure. Based on the proposed structure, an estimation algorithm is put forward to estimate the channel mismatch errors and filter bank mismatch errors. Accordingly, calibration is carried out in digital domain. Experiments are performed on the basis of designing a four-channel 20GS/s 12- bit Frequency-interleaved analog-to-digital converter system and the simulation results prove the effectiveness of the proposed method. Average distortion error of 10 6.8192 × 10−10 dB and average aliasing error of -104.84dB can be achieved after calibration, and resolution of this architecture can be up to 14-bit.

The multipath effect deteriorates the performance of low elevation target altitude estimation with a ubiquitous radar strongly, especially in complex terrain environments in which multiple reflection paths exist simultaneously. This work shows how the authors reconstructed a multipath model for ubiquitous radar with a known waveform and took advantage of it to improve the altitude measurement performance when locating low‐altitude small targets in complex terrain environments. The authors first present a multipath propagation model for target elevation angle estimation in which not only received multipaths but also transmitted multipaths are taken into consideration. Then, a reconstructed elevation angle estimation model in which the waveform is known and the target elevation information is as same as the original model is derived. Finally, an innovative low elevation angle estimation algorithm based on a maximum likelihood estimator and a sparse representation is proposed. Simulations and experiment results verify that the proposed algorithm has a much more robust low elevation angle estimation performance than existing methods in complex terrain environments. Index Terms —Target altitude measurement, complex multipath effect, known transmitted waveform, sparse representation, ubiquitous radar.

In order to improve the tracking accuracy and the scheduling success rate on the problem of phased array radar task scheduling. Adaptive time window is proposed to solve this problem. Firstly, the target state for the next observation is predicted and the influence of time shifting is sought out. Then, the size of the time window is calculated according to the influence. Finally, the simulation is carried out with X-51A near space hypersonic vehicle. Simulation results show that, comparing with traditional algorithm, this algorithm can improve the tracking accuracy and success rate of scheduling.

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler frequency migration (DFM), and velocity ambiguity (VA) in the RD domain, thus posing significant difficulties in target detection and tracking. Moreover, the presence of VA further complicates the problem. To address these complexities while maintaining integration efficiency, this study proposes a hybrid integration approach. First, methods called Keystone-transform (KT) and matched filtering processing (MFP) are proposed for compensating for range migration (RM) and velocity ambiguity (VA) in Radar Detection (RD) images. The KT approach is employed to compensate for RM, followed by the generation of matched filters with varying ambiguity numbers. Subsequently, MFP enables the production of multiple RD images covering different but contiguous Doppler frequency ranges. These RD images can be compiled into an extended RD (ERD) image that exhibits an expanded Doppler frequency range. Second, an improved particle-filter (IPF) algorithm is raised to perform incoherent integration among ERD images and to achieve track-before-detect (TBD) for a target. In the IPF, the target state vector is augmented with ambiguous numbers, which are estimated via maximum posterior probability estimation. Then, to compensate for the DFM, a line spread model (LSM) is proposed instead of the point spread model (PSM) used in traditional PF. To evaluate the efficacy of the proposed method, a radar simulator is devised, encompassing comprehensive radar signal processing. The findings demonstrate that the proposed approach achieves a harmonious equilibrium between integration efficiency and computational complexity when it comes to detecting and tracking high-speed and high-maneuvering targets with intricate maneuvers. Furthermore, the algorithm’s effectiveness is authenticated by exploiting ubiquitous radar data.