Radar systems in practical environments are often affected by radio-frequency interference, which is commonly mitigated by spatial cancellation using auxiliary antennas. However, the use of auxiliary antennas inevitably introduces frequency-dependent amplitude and phase distortions between antennas, degrading signal synthesis and suppression performance. Existing calibration methods usually rely on prior measurements or explicit parameter estimation, making them sensitive to model mismatch, especially in multi-interference scenarios with spectral overlap. This letter proposes a spectral flatness measure (SFM)-based method for distortion compensation and interference suppression. The received signal is decomposed into subbands, where distortions are approximated as locally constant and compensated independently, and all parameters are jointly optimized via a genetic algorithm using the SFM criterion. The method requires no prior information or external calibration signals and achieves robust interference suppression. Simulation and experimental results validate its effectiveness in real interference environments.

In the calibration of distributed radar for radio astronomy, deep space radio sources are commonly used as calibration sources to correct interarray delay errors, and accurate delay estimation is critical. Traditional correlation methods are limited by sampling frequency, achieving accuracy only at the sampling interval level. To achieve higher accuracy, subsample estimation is necessary. This letter proposes a precise delay calibration method using maximum likelihood iteration for subsample delay estimation. The proposed algorithm starts with the frequency domain features, first transforming the delay estimation problem into a phase estimation problem, and then calculating the likelihood function of the phase difference. A cost function is established based on the maximum likelihood criterion, and the optimal solution is obtained using the Newton iteration method. Compared to other algorithms, the proposed algorithm achieves superior accuracy in subsample delay estimation, meeting stringent calibration requirements in radio astronomy. Simulation and experimental results verify the validation of the algorithm.

Three-dimensional images can accurately reflect the target’s posture and structure, providing rich feature information for spatial target recognition. However, challenges arise in image registration and phase reconstruction for interferometric inverse synthetic aperture radar (InISAR) 3-D imaging when observation perspectives significantly differ, the target size is relatively large, or the squint model is present. Therefore, we propose a target 3-D reconstruction method based on phase-derived technology. For the issue of image distortion misregistration caused by differing observation perspectives, we propose transforming the traditional image registration problem into a multiscatterer association problem by extracting target scatterer information from inverse synthetic aperture radar (ISAR) 2-D images and using the iterative closest point (ICP) algorithm to achieve scatterer association between different ISAR images. To tackle the interference phase ambiguity problem caused by large target size or squint model in InISAR 3-D imaging, we propose transforming the 3-D reconstruction problem into a 3-D positioning problem for each scatterer and achieving 3-D reconstruction through a 3-D positioning method based on phase-derived angle measurement (PDAM) and phase-derived range measurement (PDRM). In cases where the extracted scatterers are generally nonideal, the nonideal scatterers can be treated as a whole, and cross correlation processing can be performed between different antennas to extract the interferometric phase. Both simulation and measured data have verified the effectiveness of the proposed method.

Self-defense suppression jammers pose a critical threat to radar by adaptively altering jamming frequencies based on intercepted radar pulses, which can mask the real targets. An effective countermeasure is to transmit a cover pulse before the detection pulse to deceive the jammer. In order to maximize radar detection performance while ensuring successful anti-jamming, reinforcement learning (RL) methods are employed to dynamically adjust the width ratio between the cover pulse and the detection pulse based on the jamming state and reward feedback. However, unknown jammer interception durations and interception-jamming cycles, along with the random feedback affected by noise, pose significant challenges to the pulse width selection based on RL methods. Inspired by biological intelligence to enhance RL, we propose a supervised learning (SL)-based general auxiliary task framework that emulates the spatiotemporal encoding characteristics of grid cells and time cells in mammalian brains to extract richer and more structured environmental information. Building on this, we introduce a flexible grid cell-deep recurrent Q-network (GC-DRQN) architecture, integrating SL and RL, which improves the performance of RL in handling tasks with temporal dependencies. Additionally, we implement a deterministic equivalent reward mechanism to overcome the instability in the RL convergence process caused by random rewards. Simulation results demonstrate that the pulse transmission strategy learned by GC-DRQN achieves significantly higher target detection probabilities compared to several baseline methods. Notably, in a low signal-to-noise ratio (SNR) scenario, GC-DRQN improves the target detection probability and convergence speed of DRQN by up to twofold.

Compared with traditional synthetic wideband signals, synthetic ultrawideband (UWB) signals with higher range resolution can obtain more information for target identification. The stepped-frequency chirp signal (SFCS) based on dechirp processing can simultaneously achieve a UWB, a high data rate and a low sampling rate. In this paper, a synthetic UWB range profiling method for high-speed targets based on phase-derived velocity measurement (PDVM) is proposed. High-precision velocity compensation is key to synthetic UWB range profiling for high-speed targets. Thus, the PDVM based on the pulses at the same carrier frequency is adopted to obtain high-precision velocity measurement results. Then, based on PDVM results, the synthetic UWB range profiling method in the time domain is analyzed in detail, which mainly contains time shift, linear phase correction and constant phase correction. In the phase correction process, the compensation of intrapulse Doppler modulation, range migration and tracking gate movement is emphatically analyzed. In addition, to guide the implementation in radar systems, the phase hopping caused by parameter estimation error is theoretically derived. Finally, simulation results and raw data are presented to verify the performance of the proposed method.

The performance of wideband radar detectors can be further improved if the features of a target's high-resolution range profile (HRRP) are fully utilized. In this paper, a target adaptive double threshold detection method is proposed. First, the amplitude distribution parameters of the HRRP's range cells are extracted for data generation, and the detection probability is statistically calculated. The online gradient descent method is then used to adaptively adjust the second thresholds of the double threshold detector, thereby improving the detection performance with a given constant false alarm rate. The effectiveness of the proposed algorithm is verified using two sets of measured data. The proposed algorithm exhibits superior detection performance compared to traditional detection algorithms for range-spread targets.

The phase-derived velocity measurement (PDVM) technique can achieve a high measurement accuracy at the phase level and thus has great application prospects in the field of micromotion feature extraction and target recognition. To achieve a PDVM with a low signal-to-noise ratio (SNR), a PDVM method based on the generalized Radon–Fourier transform (GRFT) is proposed in this article. The main challenges that we overcome are phase extraction and phase ambiguity resolving under the condition of a low SNR. By utilizing the GRFT to estimate the target motion parameters, the echo peak position can be reconstructed, and then the peak phase value can be extracted. In the meantime, the phase ambiguity integer can be resolved based on the rough velocity estimation results obtained by the GRFT, and the phase ambiguity resolving can be realized at a low SNR. In addition, to suppress the influence of noise on the extracted phase, a filter design method based on the target motion characteristics is proposed to further improve the accuracy of the PDVM. In the simulation, the performance of the proposed method under different motion models and different SNR conditions is analyzed, and the effectiveness of the proposed method under low-SNR conditions is verified. Compared with directly using the GRFT, the proposed method has the advantages of strong applicability to different motion models and low computational load.

In this article, we propose a phase-derived velocity measurement (PDVM) method for high-speed spatial targets based on the stepped-frequency chirp signal (SFCS). This method is capable of accurately measuring the velocity of high-speed targets and yields root-mean-squared error values at the level of centimeters per second; therefore, it has great potential for measuring the micromotion of targets and is of significant importance for target recognition. The traditional phase-derived measurement method is not applicable for high-speed targets. The main challenge that we have solved is how to extract the echo phase from the high-resolution range profile, which is corrupted by range migration, intrapulse motion, and range straddling under high-speed target conditions. To guide the implementation of the proposed method in radar systems, constraint conditions for the compensation accuracy are thoroughly derived and systematically justified under different radar parameter settings. The simulation results are presented to validate the high accuracy of the method under various circumstances. In addition, the small-amplitude micromotion measurement capability of the proposed method is verified, and reconstruction of the target micromotion trajectory is demonstrated.

A novel high-accuracy phase-derived velocity measurement (PDVM) method for fast-moving space targets is presented in this letter. First, a wideband linear frequency-modulated signal model that considers the effect of radial acceleration was developed. To obtain the unambiguous phase difference between two adjacent echo pulses, coarse velocity and acceleration measurements derived from range profile cross correlation were used to resolve the phase ambiguity. Then, the derived accurate and unambiguous phase difference and the phase error induced by the discrete Fourier transform were analyzed. The PDVM technique was applied after the phase error was compensated for, and all required parameters were calculated. Under low signal-to-noise ratio (SNR) conditions, a correction for the phase unwrapping error was developed. The simulation results showed that the proposed PDVM technique was highly accurate. The root-mean-square error of the PDVM results was less than 0.025 m/s when the SNR was greater than 15 dB.

This paper proposes a phase-derived velocity measurement (PDVM) method for high-speed targets based on wideband direct sampling linear frequency modulated radar. First, a high-speed target echo model considering intrapulse Doppler modulation is developed. Then, a PDVM model considering acceleration is established. The key to realizing PDVM is resolving phase ambiguity. Under low signal-to-noise ratio (SNR) conditions, a joint processing method combining acceleration information and multiframe data to solve phase ambiguity is proposed, which can significantly reduce the SNR requirement for PDVM. In this paper, the small-amplitude micromotion measurement capability of the proposed method is verified by simulation. Moreover, the measured data of a Ku-band ground-based radar are used to verify the applicability of the PDVM method under low SNR conditions and its feasibility to be applied to complex multi-scattering point targets. Both the simulation and experimental results show that the proposed method is suitable for high-speed targets with radial motion, including acceleration and jerk, and that the PDVM precision can reach the order of magnitude of centimeters per second or millimeters per second.

The existing methods for motion compensation in inverse synthetic aperture radar (ISAR) imaging are generally limited to the low-order target motion model, and require iterative optimization with limited velocity estimate precision and heavy computational burdens. This paper proposes a high-precision method of phase-derived velocity measurement (PDVM) and applies it to motion compensation of ISAR imaging. The method applies PDVM based on range profiles cross correlation to the translational velocity estimation of targets, and converts the velocity measurement results to the corresponding range increment. The equivalent phase-derived range measurement precision can reach the order of magnitude of millimeter (mm) or even sub-mm, which can satisfy the precision requirements of both envelope alignment and phase adjustment. The key to realizing PDVM is resolving phase ambiguity. The traditional method for resolving ambiguity has very high requirements for the signal-to-noise ratio (SNR). This work resolves ambiguity by combining multiframe data, i.e., by resolving ambiguity of multiframe data simultaneously instead of resolving ambiguity of single-frame data independently and correcting the above ambiguity-resolving results using a minimum-entropy method. Therefore, phase ambiguity can be correctly resolved under a relatively low SNR. Experimental results of an ISAR imaging of an airplane show that the method proposed in this paper can obtain high-quality ISAR imagery, and can efficiently realize robust imaging under the conditions of low SNR.

This paper proposed a 3-D surface deformation retrieval algorithm for synthetic aperture radar interferometry (InSAR) based on a Global Navigation Satellite System (GNSS) as a transmitter and a fixed receiver (GNSS-based InSAR), where simultaneous multiple GNSS transmitters and a repeat-pass concept were adopted. This paper consists of three parts. First, the interferometric phase model under repeat-pass concept was established for both general and bistatic permanent scatterer (PS) cases in the GNSS-based InSAR. Second, the 3-D deformation retrieval algorithm was presented, and the position dilution of precision was analytically derived to evaluate the performance of measured 3-D deformation. Third, using a designed transponder onboard displacement device as the bistatic PS, the feasibility of 3-D deformation retrieval using GNSS-based InSAR was confirmed by repeat-pass experiments, where four simultaneously available Beidou-2 Inclined Geosynchronous Orbit satellites were used as transmitters, and 16 repeat-pass data sets were collected. Using the proposed algorithm, the final experimental results suggested that the GNSS-based InSAR could obtain deformation estimations with better accuracy than at 5 mm in all three directions. Thus, huge potential exists for applications such as landslide prediction and infrastructure safety monitoring.

In this paper, we discuss using field-programmable gate arrays (FPGAs) to process either time- or frequency-domain signals in human sensing radar applications. One example will be given for a continuous-wave (CW) Doppler radar and another for an ultrawideband (UWB) pulse–Doppler (PD) radar. The example for the CW Doppler radar utilizes a novel superheterodyne receiver to suppress low-frequency noise and includes a digital downconverter module implemented in an FPGA. Meanwhile, the UWB PD radar employs a carrier-based transceiver and a novel equivalent time sampling scheme based on FPGA for narrow pulse digitization. Highly integrated compact data acquisition hardware has been implemented and exploited in both radar prototypes. Typically, the CW Doppler radar is a low-cost option for single human activity monitoring, vital sign detection, etc., where target range information is not required. Meanwhile, the UWB PD radar is more advanced in through-wall sensing, multiple-object detection, real-time target tracking, and so on, where a high-resolution range profile is acquired together with a micro-Doppler signature. Design challenges, performance comparison, pros, and cons will be discussed in detail.