GPS signals can be affected easily by interference due to the low power of signals and the long way between the satellites and receivers. Interference cancellation is one of the major challenges in using GPS. One of the most common intentional interferences is Continuous Wave Interference (CWI) jamming and the most popular way to reduce the impact of it on the GPS signal is using an Adaptive Notch Filter (ANF). Two kinds of heuristic Evolutionary Algorithms (EAs) are used to design second-order IIR Evolutionary Adaptive Notch Filter (EANF). The first algorithm is the Genetic Algorithm (GA) and the second one is Particle Swarm Optimization (PSO) algorithm. EAs are used to find answers to the problems in which there is no specific solution, and this is exactly what is needed to fix the digital filter design problems. NF is implemented in FPGA hardware according to the obtained filter coefficients. Finally, the efficiency of the proposed methods is compared with similar methods in terms of different evaluation metrics. The simulation results show about 12% SNR improvement by using GA and 97% RMS improvement by using the PSO method for higher than 50-dB JSRs.

中文翻译：

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GPS抗干扰系统的进化自适应陷波滤波器设计

由于信号功率低且卫星与接收器之间的距离较远，GPS 信号很容易受到干扰的影响。干扰消除是使用 GPS 的主要挑战之一。最常见的故意干扰之一是连续波干扰 (CWI) 干扰，而减少其对 GPS 信号影响的最流行方法是使用自适应陷波滤波器 (ANF)。两种启发式进化算法（EA）用于设计二阶IIR进化自适应陷波滤波器（EANF）。第一种算法是遗传算法（GA），第二种是粒子群优化（PSO）算法。EA 用于寻找没有具体解决方案的问题的答案，而这正是解决数字滤波器设计问题所需要的。NF 根据获得的滤波器系数在 FPGA 硬件中实现。最后，在不同的评估指标方面，将所提出方法的效率与类似方法进行了比较。仿真结果表明，对于高于 50-dB 的 JSR，使用 GA 可提高约 12% 的 SNR，使用 PSO 方法可提高 97% 的 RMS。