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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.

In this paper, we consider an optimal antenna deployment problem for distributed multistatic radar under the situation of multiple surveillance regions in a dynamic environment. The problem is solved by proposing a dynamic multi-objective particle swarm optimization (DMOPSO) with the assistance of the proposed Kalman filtering prediction strategy. Firstly, we construct a dynamic multi-objective optimization problem for antenna deployment by choosing the effective coverage rates of different non-fixed surveillance regions as objective functions. Then, to avoid the unnecessary computational load for solving optimization problem and improve the real-time property of antenna deployment, a DMOPSO algorithm based on the Kalman filtering prediction strategy is proposed by extracting the relationships among adjacent optimization time segments. Finally, numerical results are provided to verify the validity of the proposed DMOPSO algorithm.

In this paper, considering multiple surveillance regions in a dynamic environment, we investigate an optimal antenna deployment problem for distributed multistatic radar. The problem is solved by proposing a prediction based particle swarm optimization (PBPSO) algorithm. Unlike the traditional particle swarm optimization (PSO) method, which needs completely recomputation when the environment changes, the proposed method uses the information about the previous optimal deployment schemes to predict and optimize the current one. So it greatly increases the solving efficiency and computational load of the optimal problem. Firstly, by dividing a continuous time period into several scenarios, we use the previous optimal scheme and a Kalman prediction model to predict an approximate optimal scheme for the current scenario. Then, to obtain the real optimal scheme, we can use the predicted optimal scheme and PSO method to optimize. Numerical results are provided to verify the advantages of PBPSO in optimizing the antenna deployment scheme in a dynamic environment.