<p>This paper presents a novel low-angle target tracking (LATT) method for smart city surveillance radars, addressing critical safety challenges such as drone and helicopter detection near airports, focusing on the optimization of an orthogonal frequency division multiplexing (OFDM) waveform to enhance the wideband ambiguity function (WAF). By minimizing the discrepancy between the desired and achieved WAF using the least squares error (LSE) method in conjunction with a genetic algorithm (GA), the proposed waveform significantly improves delay and doppler resolution by exploiting multicarrier properties and phase-amplitude coding techniques. The design accounts for LATT challenges, including atmospheric attenuation, surface reflection, diffraction and refraction, incorporating Earth’s curvature and atmospheric refractivity gradients. Numerical results indicate that the proposed method achieves superior resolution and angle measurement accuracy compared to existing approaches, demonstrating reduced root mean square error (RMSE) across various signal to noise ratio (SNR) and range scenarios, thereby enhancing urban resilience and airspace safety in smart city environments. Finally, we conduct a comparison between our methodology and the approach employed in the other article like by Mr. Sen. The findings from this comparison demonstrate that the ambiguity function is improved in the zero doppler slice view. Furthermore, the first sidelobe level of the ambiguity function for our adaptive waveform method is 22 dB lower than that of the corresponding method proposed by Sen, while maintaining comparable performance in the zero delay slice view. Additionally, the performance of the RMSE curve in relation to SNR and range has also shown significant enhancement. Finally improved of RMSE/SNR curve reduction in our LATT method compared to other articles is 41.66%, Also improved of RMSE/Range curve reduction is 66.66%.</p>