*3.2. Resolution of the Frequency Diversity Algorithm*

According to Equation (15), increasing the signal processing bandwidth *M*Δ*f* and the sensor interval *d* can improve the resolution of the algorithm. Figure 7 is a simulation verification of this property. The sampling frequency is 4 kHz, the number of samples is 4096, Δ*f* = 1 Hz, and the starting frequency *f*<sup>0</sup> = 100 Hz.

**Figure 7.** Azimuth resolution of the proposed algorithm, with different *d* and *M*Δ*f*. (**a**) Sensor interval *d* and (**b**) processing bandwidth *M*Δ*f*.

In Figure 7a, there is only one target at 50◦. When the processing bandwidth *M*Δ*f* is set to 400 Hz, as the sensor interval decreases, the width of the main lobe becomes wider, so the resolution decreases. The two targets in Figure 7b have an incoming wave direction of 50◦ and 52◦. When *d* is set to 128 m, as the processing bandwidth *M*Δ*f* increases, the main lobe width becomes narrower, and the resolution increases. In addition, when *M*Δ*f* = 100 Hz, the algorithm cannot separate two targets. When *M*Δ*f* = 300 Hz, the algorithm can separate the two targets, but the peaks do not appear exactly at 50◦ and 52◦, but at 48◦ and 53◦. When *M*Δ*f* = 700 Hz, the two peaks appear at exactly 50◦ and 52◦, so the azimuth estimation is accurate. In summary, the larger the sensor interval, the wider the processing bandwidth *M*Δ*f*, the higher the resolution of the algorithm, and the more accurate the azimuth estimation.
