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This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.

Unmanned combat aerial vehicle (UCAV) trajectory planning to avoid radar detection threats is a complicated optimization problem that has been widely studied. The rapid changes in Radar Cross Sections (RCSs), the unknown cruise trajectory of airborne radar, and the uncertain distribution of radars exacerbate the complexity of this problem. In this paper, we propose a novel UCAV trajectory planning method based on deep reinforcement learning (DRL) technology to overcome the adverse impacts caused by the dynamics and randomness of environments. A predictive control model is constructed to describe the dynamic characteristics of the UCAV trajectory planning problem in detail. To improve the UCAV’s predictive ability, we propose a memory-enhanced twin delayed deep deterministic policy gradient (ME-TD3) algorithm that uses an attention mechanism to effectively extract environmental patterns from historical information. The simulation results show that the proposed method can successfully train UCAVs to carry out trajectory planning tasks in dynamic and unknown environments. Furthermore, the ME-TD3 algorithm outperforms other classical DRL algorithms in UCAV trajectory planning, exhibiting superior performance and adaptability.

In this paper, exploring multicarrier waveforms, we investigate the performances of co-existence between radar and communication systems. Performances are measured in terms of radar mutual information (RMI) for the radar system and communication data rate (CDR) for the communication system. Two joint design schemes are proposed based on isolation and sharing, respectively. Firstly, for the isolation-based scheme, radar and communication signals are transmitted/received on the isolated bands. Two independent problems of RMI/CDR maximization are proposed and the optimal power allocation solutions can be characterized using Karush-Kuhn-Tucker (KKT) optimality conditions. This scheme undertakes the advantage of interference avoidance and easy implementation. Secondly, for the sharing-based scheme, radar and communication signals can be transmitted/received on the same band and the RMI/CDR maximization problems are jointly solved. The optimal power allocation solutions are optimized by the proposed sequential optimization algorithm. Compared with the isolation-based scheme, this scheme is more flexible and brings additional gain on performance at the price of higher complexity. Finally, simulation results are provided to analyze and discuss the performance and validate the theoretical results.

In this study, a coherent detection method is proposed to solve the problems during the coherent integration (CI) process, including the range cell migration (RCM) and Doppler spread (DS) effects which are caused by the complex motions of the maneuvering target. Particularly, this method applies the third-order keystone transform firstly, which could remove the third-order RCM induced by the jerk motion. Then target's defined velocity can be estimated by performing the frequency-shifting cross-correlation function and scaled Fourier transform. Thereafter, utilising the estimated defined velocity to construct a velocity compensation function, which could correct the first-order RCM (FRCM). Subsequently, the matched filtering process is performed to eliminate the second-order RCM and DS effects. Finally, applying the Fourier transform with respect to the slow time to realise the CI.

In this study, a signal design algorithm for radar–communications system is investigated. An arbitrary pulse length orthogonal frequency-division multiplexing with sufficient cyclic prefix (CP-based OFDM) signal is designed as the radar–communications signal, the communication symbols are embedded by proposing an interval linear phase compression (ILPC) modulation. Firstly, the authors construct the model of CP-based OFDM signal, and analyse the impact of the communication symbols embedding. Then, to decrease the impact caused by the communication symbols embedding, the ILPC modulation is proposed. To guarantee the performances of the CP-based OFDM radar–communications signal, a time/frequency domain iterative filtering and clipping method is used. This algorithm can be used to design a CP-based OFDM signal that has a balanced performance in communications and radar. Finally, numerical results are provided to verify the validity of the proposed algorithm.

Under the circumstance of simultaneously scanning multiple regions in an environment which can change as time goes by, the authors study an optimal algorithm to solve antenna deployment problem for MIMO radar systems here. It is solved by multi-objective particle swarm optimisation algorithm (MOPSO) combining an autoregressive (AR) prediction model (MOPSO-AR). In a time period and dynamic environment, the MOPSO-AR method uses the previous optimal information to calculate the current deployment schemes before PSO optimisation starts up. It greatly reduces computational load and the error of the solutions. First, by discretising the time period into several time intervals, the problem in each time interval can be seen as a static problem. However, there may be relationship between these time intervals. Second, use the previous information and an AR model to predict temporally optimal solutions. Then, to get the exact optimal solutions, the predicted solutions and PSO method was applied to compute. Simulations show that the prediction strategy improves algorithm performance.