In this letter, we have studied the all-digital code tracking problem for band-limited direct sequence spread spectrum (DSSS) systems. Band-limited DSSS systems have many advantages over rectangular-pulse DSSS systems. However, the current all-digital code tracking methods for band-limited DSSS systems have more computational complexity than the latter. Therefore, we propose an efficiently all-digital code tracking method for band-limited DSSS systems where we implement despreading in the frequency domain with others and take advantage of the narrow time-width characteristic of the signal autocorrelation to achieve a lower complexity and constant tracking performance, i.e., the constant steady-state time-delay error variance at the same time. We also give a closed-form expression of the steady-state time-delay error variance of the proposed method in this letter, which can be used to analyze the performance of the proposed method theoretically and design proper band-limited DSSS systems. The theoretical analysis and simulation results show that the proposed method has lower computational complexity and constant tracking performance.

Group target tracking (GTT) is a promising approach for countering unmanned aerial vehicles (UAVs). However, the complex distribution and high mobility of UAV swarms may limit GTTs performance. To enhance GTT performance for UAV swarms, this paper proposes potential solutions. An automatic measurement partitioning method based on ordering points to identify the clustering structure (OPTICS) is suggested to handle non-uniform measurements with arbitrary contour distribution. Maneuver modeling of UAV swarms using deep learning methods is proposed to improve centroid tracking precision. Furthermore, the group’s three-dimensional (3D) shape can be estimated more accurately by applying key point extraction and preset geometric models. Finally, optimized criteria are proposed to improve the spawning or combination of tracking groups. In the future, the proposed solutions will undergo rigorous derivations and be evaluated under harsh simulation conditions to assess their effectiveness.

With the rapid increase of data generated by remote sensing satellites, the demand of real-time demodulation of ultra-high-speed remote sensing data by digital receivers has become a research hotspot and difficulty. Aiming at the international research gap of parallel demodulation of remote sensing satellite data using multi-channel channelization technology, a parallel matched filtering architecture of filter banks based on Weighted OverLap-Add (WOLA) structure is designed. This structure has the following advantages: (a) The decimation factor is flexible and adjustable when the signal is channelized, which is not restricted by the number of channels. (b) The signal is divided into several sub-bands according to the frequency spectrum, which is beneficial to the parallel analysis and processing of the signal. This paper completes the parallel matched filtering processing and provides ideas for the subsequent processing operation. (c) The structural channelization decomposition has strong expandability and can be flexibly applied to signal transmission of different systems. Aiming at the characteristics of high speed and wide bandwidth of remote sensing data transmission, this paper designs the channelization method of filter bank based on WOLA structure, and deduces and realizes the sub-band matching filtering operation. Taking QPSK modulation as an example, the simulation results show that parallel matched filtering has almost no loss in bit error performance. The research results of this paper have great reference value for designing multiphase parallel receivers of high-speed remote sensing data in the future.

The Linear Quadratic Regulator (LQR) problem for systems with uncertainty is challenging: model-based design loses optimality, while prevailing reinforcement learning methods demand prohibitive data and computation. This paper introduces a Hybrid-Driven Recurrent Learning (HDRL) framework that bridges this gap by synergizing model-based optimal control with data-driven learning in a novel way. The core of HDRL is a hybrid training strategy: the policy is evaluated by rolling out on the actual uncertain system (forward pass), while the policy gradient is computed by backpropagating through a deterministic nominal model (backward pass). This approach creates a low-variance, model-guided policy gradient, a fundamental departure from high-variance model-free estimators. Architecturally, HDRL employs a recurrent neural network-like structure, repurposing the LQR cost as a self-supervised loss. A key enabler is our method to convert both additive and structured uncertainties into an additive signal, making training feasible without knowledge of the perturbed dynamics. Simulations demonstrate that HDRL provides a robust, sample-efficient, and practical solution for optimal control under uncertainty.