High mobility of space-based radar (SBR) platforms risks target velocities falling below the minimum detectable velocity (MDV), rendering them undetectable in main-lobe clutter. Aiming at multi-target tracking (MTT) in space-based multiple-input multiple-output (MIMO) radar systems, this paper proposes a joint beam and dwell time allocation (JBTA) strategy. This strategy incorporates the MDV constraint and adopts the Bayesian Cramér-Rao Lower Bound (BCRLB) as the performance metric, where BCRLB is a lower bound for the mean square error (MSE) of target state estimation. To solve the non-convex mixed-integer optimization problem of JBTA, a two-step decomposition approach is designed. Numerical results verify that JBTA effectively improves global MTT performance.

In high-resolution unmanned aerial vehicle (UAV) Synthetic Aperture Radar (SAR) imaging, despite the utilization of inertial navigation system (INS) data for pre-compensation, residual envelope and phase errors may still persist. Residual envelope errors can adversely affect phase error estimation (PEE), consequently degrading autofocus performance. This letter proposes a phase-envelope joint autofocus algorithm for backprojection (BP) imaging. An algorithm framework for alternating iterative estimation of phase and envelope is constructed based on maximizing image sharpness, which can effectively correct phase and envelope errors. In addition, only a small local area is selected for error estimation, significantly reducing memory and time consumption. The effectiveness of the algorithm is verified using X-band UAV SAR raw data.

An efficient raw data generation (RDG) algorithm in the hybrid domain is proposed, which can be applied to the accurate echo generation of spotlight mode synthetic aperture radar (SAR) with trajectory deviation, even in cases of terrain undulation. Generally, the accuracy required for the signal’s envelope is on the order of the range resolution cell. However, the requirement for the phase is significantly higher, on the order of the wavelength. Therefore, the proposed algorithm calculates the phase of the SAR raw data in the time domain through a point-by-point approach to ensure accuracy and computes the envelope of the raw data through subblock processing in the frequency domain to improve efficiency. To balance the computational efficiency and accuracy, the optimal selection of subblock size is discussed in detail. Simulation experiments verify the accuracy and efficiency of the method.

Rotating synthetic aperture radar (RoSAR) employs rotating antennas to provide a 360° panoramic image of the surroundings from a stationary platform. A multichannel in azimuth RoSAR antenna has been used; the system transmits chirp signals with a low pulse repetition frequency (PRF), and all channels simultaneously receive the echo, resulting in an image sequence with the same frame rate as the video. In the main lobe of the azimuth beam, the recorded echo of the single channel is ambiguous, which makes steering vector errors inevitable in real systems and causes both nonadaptive and adaptive beamforming to perform noticeably worse or even fail. In this letter, we propose a Doppler ambiguity suppression algorithm with the doubly constrained robust Capon beamformer (DCRCB) for multichannel RoSAR that overcomes a number of nonideal factors such as amplitude–phase errors and array element displacement errors between channels. In order to obtain a high-resolution scene image, RoSAR imaging procedures are performed. To clearly demonstrate the proposed approach, the experimental results using simulated data are shown.

One concern in Pulse-Doppler (PD) processing for airborne radar is range ambiguity resolving. This paper presents a method for collaborative range-ambiguity resolving among multi-radar using a single pulse repetition frequency (Mono-PRF). The approach eliminates the need for designing multiple PRF sets to resolve range ambiguity. Instead, it acquires the target’s ambiguous range in each radar coordinate system and extends the range based on the maximum ambiguity number. Subsequently, collaborative localization equations are applied to co-locate the same target from multi-radar, resulting in a set of range-angle curves in the reference radar’s coordinate system. The intersection of these curves provides a collection of range points, among which the true target range lies. Finally, a chi-square test based on Mahalanobis distance is used to select the true target point. Other radars utilize collaborative localization equations to resolve range ambiguity. Simulation results validate the effectiveness of this approach.

Measuring target angles is a crucial issue in airborne radar detection. Traditional dynamic programming track-before-detect (DP-TBD) often operates in low signal-to-noise ratio (SNR), posing challenges to angle measurement and localization. This letter presents an angle estimation technique for TBD processing. It interprets target angles across multiple frames using initial frame angle (IF-AG) and inter-frame angular rate of change (IF-ARC). Compensating for the latter and performing noncoherent accumulation to improve SNR before estimating the IF-AG enhance multiframe target angle estimation precision. The method initially formulates the cost function for multiframe angle measurement based on the raw data extracted from the multiframe array by DP-TBD. It subsequently estimates and compensates for IF-ARC, related to velocity, followed by IF-AG estimation, providing target angles for all frames concurrently. Simulation results validate the effectiveness of this method.

This letter deals with the problem of simultaneously locating the single source and refining erroneous sensor positions in a distributed network with the use of ground receivers whose positions are known a priori. Besides the external time delay (TD) measurements from sensors to ground receivers, we also exploit the inter-sensor TD measurements with the aim of improving the positioning accuracy. The devised scheme introduces the coupling terms of source and sensor locations as nuisance parameters to enable the formulation of pseudolinear equations, and then the problem could resort to a two-stage weighted least squares estimator. The scheme is shown in both theory and simulation to be capable of reaching the Cramer-Rao lower bound under the small measurement noise and small sensor position errors.

The helicopter-borne synthetic aperture radar (SAR) system suffers high-frequency vibrations caused by the rotor rotation. This micro-vibration induces phase modulation, which gives rise to paired echoes and seriously degrades the quality of SAR images due to the unexpected ghost phenomenon. In this letter, a nonparametric paired echo suppression method for helicopter-borne SAR imaging is proposed. By constructing a modified vibration error model and introducing the autofocus method for the vibration phase error estimation, it can suppress the paired echoes directly, which avoids the complex vibration parameter estimation in the traditional methods. The effectiveness of the proposed method is demonstrated via simulations and real data processing.

Due to the existence of vertical velocity in diving-squint synthetic aperture radar (SAR) imaging, the azimuth-shift invariance along the horizontal direction is not satisfied. This will lead to a big approximation error and influence the imaging results when the existing imaging processing methods are directly applied. In order to solve these problems, an equivalent model is first introduced to describe the motion characteristic in the diving-squint mode. By adopting this approach, the diving-squint SAR imaging can be treated as the conventional one, i.e., the height remains unchanged, with the azimuth-shift invariance satisfied along the flight direction. Based on the equivalent model, a two-step processing method for the diving-squint SAR imaging with subaperture data is proposed in this article. First, a time-scaling approach is adopted to obtain the well-focused 2-D image in the slant plane. Since the equivalent model will cause the rotation of the imaging projection plane and introduce the severe distortion in the ground imagery, a rapid geometric correction method based on inverse projection is further performed to get the ground imagery with little distortion through 2-D sinc interpolation. Simulation and real-data results validate the effectiveness of the proposed approach.

As training samples are not always identically distributed with the clutter in the cell under test (CUT) in heterogeneous environments, the estimated clutter covariance matrix for space-time adaptive processing (STAP) is not accurate, which degrades the performance of STAP. To improve the performance of STAP in heterogeneous environments, this letter proposes a novel training sample selection algorithm to estimate the covariance matrix. Based on the subaperture smoothing techniques, subapertures' covariance matrices are estimated, which are used to measure the similarities between the clutter covariance matrix of the CUT and the clutter covariance matrices of the training samples. Training samples whose clutter covariance matrices are similar to that of the CUT are selected, leading to a better estimation of the clutter covariance matrix, and the performance of STAP improves. Experimental results confirm the performance of the proposed algorithm.

Space-time adaptive processing (STAP) techniques have achieved good performance when applied to side-looking airborne radar (SLAR) where the ground clutter is relatively stationary, but due to severe range dependence, the performance is not so good in non-SLAR particularly when the pulse repetition frequency is high enough to induce range ambiguity. Because the short-range clutter Doppler for non-SLAR varies rapidly with range while the long-range clutter Doppler varies gently, at the presence of range ambiguity, the clutter in different ambiguous ranges does not coincide. The clutter range dependence mitigation for the case of range ambiguity is the major problem that this letter intends to solve. A subarray synthesis algorithm with prefiltering in elevation is presented. By this technique, the ambiguous short-range clutter is eliminated before STAP; therefore, the clutter range dependence is alleviated, and then, the STAP performance will be improved greatly.