## 1 Introduction
For the evaluation of the altimetry performance, [PERSON] et al. considered both the altimetric precision and the altimetric accuracy. altimetric accuracy is primarily affected by systematic errors. The altimetric precision is mainly induced by the randomness of the received signal caused by thermal noise and speckle noise. Theoretically, the higher the SNR of the signal, the more accurate the SP delay information extracted from the waveform, and the better the altimetric precision [15]. It is possible to increase the altimetric precision by optimizing the payload and signal post-processing. Payload optimization includes increasing antenna gain, refining antenna pointing and receiver bandwidth, etc. [16; 17]. For signal post-processing, one method to improve the altimetric precision is to increase the signal's coherent integration time and incoherent average number. Both methods inhibit the noise introduced in different ways. The former is to suppress the thermal noise introduced at the receiver end, while the latter is to suppress the speckle noise introduced in the glistening area near the SP. The total integration time of signal processing is the product of the coherent integration time and incoherent average number. In theory, high precision requires an increased integration time. Despite this, the inverse relationship between the along-track resolution and the integration time prevents the latter from increasing indefinitely. It is typically necessary to sacrifice a certain degree of precision when performing altimetry tasks requiring high spatial resolution along the orbit. To achieve an optimal altimetric precision for a given spatial resolution along track, it is necessary to optimize the combination of coherent and incoherent parameters. Accordingly, [PERSON] et al. derived an altimetric precision prediction model with coherent integration time as a variable [18], but this model overlooked the influence of the correlation between the waveforms. [PERSON] et al. constructed a waveform correlation model from the time domain and frequency domain to predict the upper limit of the waveform coherence time [19; 20]. [PERSON] et al., reconstructed the altimetric precision model by considering the correlation of the waveforms which had reasonable consistency with the measured results [21], but the influence of the coherent integration time on the precision was not discussed in detail. Currently, traditional intermediate frequency (IF) data processing uses empirical parameters to approximate an optimal precision. The coherent integration time normally takes 10 ms in airborne scenarios [21] and 1 ms in spaceborne scenarios [22]. However, the actual optimal coherent integration processing depends on factors such as the position and relative motion of the transmitter and receiver. This needs to be estimated through accurate modelling. Different from previous studies, in order to improve the precision of iGNSS-R ocean altimetry, this study constructs a coherent integration time optimization model by deducing the conversion relationship among coherent integration time, waveform correlation, and altimetric precision. Furthermore, the model can more accurately estimate the variation of precision with coherent integration time in different iGNSS-R altimetry applications so as to optimize the final precision result.