The time division multiple access (TDMA) technique has been applied in automotive multiple-input multiple-output (MIMO) radar. However, it suffers from the transmit energy loss, and as a result the parameter estimation performance degradation when the number of transmit elements increases. To tackle these problems, a transmit beamspace (TB) Doppler division multiple access (DDMA) approach is proposed. First, a phase modulation matrix with empty Doppler spectrum is introduced. By exploiting the empty Doppler spectrum, a test function based on sequential detection is developed to mitigate the Doppler ambiguity in DDMA waveform. Then, a discrete Fourier transform (DFT)-based TB in slow-time is formed. The proposed method can achieve waveform diversity in Doppler domain and generate a TB in slow-time that concentrates the transmitted power in a fixed spatial region to improve the transmit energy distribution for automotive MIMO radar, which is favored by medium/long range radar (MRR/LRR) applications. As compared to the conventional TDMA technique, the proposed TB DDMA approach can fully exploit the transmission capabilities of all transmit elements to ensure that the emitted power is efficiently used and inherits easy implementation.Moreover, the proposed TB DDMA method avoids the trade-off between the active time for each transmit antenna element and the frame time due to Doppler ambiguity mitigation. Simulation results verify the effectiveness of the proposed TB DDMA approach for automotive MIMO radar.

The latest progress of the multiple‐input multiple‐output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low‐cost S‐band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target‐based MIMO radar array calibration method to improve the signal‐to‐noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse‐array beam‐pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI‐Phantom 4 drone at a range of up to 5 km. Target features, including micro‐Doppler and radar cross‐section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.

The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets.

This paper proposed a Pulse-Agile-Phase-Coding slow-time MIMO (PAPC-st-MIMO) waveform, where the phase-coded signal is utilized as the intra-pulse modulation of the slow-time MIMO waveform. Firstly, the signal model of the proposed waveform is derived. To improve the orthogonality of the phase-coded waveform sets, a novel hybrid evolutionary algorithm based on Cyclic Algorithm New (CAN) is proposed. After the optimization process of the phase-coded waveform sets, the signal processing method of the PAPC-st-MIMO waveform is derived. Finally, the effectiveness of the proposed method is verified with a simulation experiment. The mitigation ratio of the near-range detection waveform can achieve −30 dB, while the long-range detection waveform can achieve −35 dB. This approach ensures waveform orthogonality while enabling the slow-time MIMO waveform to achieve distance selectivity. By conducting joint pulse-Doppler processing across multiple range segments, range ambiguity can be suppressed, increasing the system’s Pulse Repetition Frequency (PRF) without introducing ambiguity.

Pulse-Doppler (PD) radar systems are extensively utilized for detecting and tracking moving targets. However, conventional PD radar suffers from range ambiguity due to the regularities in waveform. Previous researches have attempted to resolve this issue using techniques such as multi-pulse repetition frequencies scheme, waveform diversity, nonlinear ambiguity suppression (NLAS) and sparse reconstruction. Despite their efforts, these techniques exhibit limitations, including increased search time, residual ambiguous energy, signal distortion, and model mismatch. To overcome these limitations, this paper proposes a novel nonlinear processing approach named ”INLAS-JRMMF” for suppressing range ambiguity, which combines a joint reversible mismatched filter (JRMMF) with improved NLAS. In general, INLAS-JRMMF scheme achieves range ambiguity suppression by transforming the signals into the range-Doppler domain, removing the undesired ambiguous energy, and retrieving radar signals by inverse transformation. To successfully implement this scheme and improve the range ambiguity suppression performance, we have designed an iterative processing framework, and two reversible mismatched filters, i.e., RMMF and JRMMF. The proposed method can effectively address the problems of signal distortion, residual ambiguous energy, and range sidelobe modulation effect, as well as enjoys low integrated sidelobe level and signal-to-noise loss. The effectiveness of the proposed approach is evaluated through simulations and the processing of measured data. The results demonstrate that the INLAS-JRMMF method surpasses the NLAS method in terms of range ambiguity suppression and radar imaging outcomes.

Multiple-input multiple-output (MIMO) radar realize waveform diversity by transmitting orthogonal waveforms on each array. Generally, implementing the waveform requires multiple signal generators or carrier frequencies, which results in increased hardware complexity and high cost. Slow-time MIMO waveform realizes the orthogonality between waveforms in the Doppler frequency domain, it has the advantage of being easy to implement and the signal processing algorithm is similar to traditional PD radar. However, the current method still implement the Slow-time MIMO waveforms by analog mixing, which has low phase accuracy and easy to be affected by temperature. In this paper, a method for realizing Slow-time MIMO radar waveform based on direct digital waveform synthesis (DDWS) is proposed. The method achieves low cost, high phase precision and strong coherent performance, and can be implemented by using existing radar signal generator. This paper presents the implementation method of Slow-time MIMO waveforms and uses S-band MIMO radar for practical verification.