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.

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.

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.