Investigating the Ambiguity of Ghosts in Radar and Examining the Diagnosis and Ways to Deal with it
In distance measurement with the help of delay of pulse reception time, the target distance is determined by the time interval between the emission of a pulse and its echo reception. In elementary radars, measurements were made on the screen distance trace. In advanced analog radars, measurements are made by sequential opening of distance windows. Digital radars do the same thing by intermittently sampling the receiver output, converting samples to numbers, and storing these numbers in a bank of distance cells. The distance whose return time is equal to the pulse transmission period is called the maximum unambiguous distance. The solution to the ambiguity problem depends on the presence of ambiguities and the risk of ambiguity for the system. In this article, we look at a type of ambiguity called "ghosts" and look at how to remove it. Finally, we will briefly describe how to measure distance while tracking a single target.
If the PRF can be considered so low that it is required from the maximum distance, the problem can be solved by eliminating returns related to farther goals. This can be easily done using the jump rate (Jitter). If higher PRFs are needed, then ambiguity is no longer appropriate, but we must clear up the ambiguities. This can be accomplished by successively switching the PRF between two or more consecutive values, and measuring changes in apparent boards. In this technique, if two targets are revealed simultaneously, each of them has two apparent distances, and the wrong choice of the pair of apparent locations of each target leads to a kind of error called a ghost. Ghosts can be removed by using additional PRFs. Using more than one PRF, in addition to adding complexity, also reduces the maximum radar range. Therefore, in choosing the optimal number of PRFs, a compromise must be made between these costs and the costs associated with occasional encounters with unresolved ambiguities and ghosts.
 O. Sharifi-Tehrani and S. Talati, “PPU Adaptive LMS Algorithm, a Hardware-Efficient Approach; a Review on”, Majlesi Journal of Mechatronic Systems, vol. 6, no. 1, Jun. 2017.
 Hashemi. Seyed Mohammad, Abyari. Mohammad, Barati. Shahrokh, Tahmasebi Sanaz, Talati, Saeed. “A PROPOSED METHOD TO CONTROLLER PARAMETER SOFT TUNING AS ACCOMMODATION FTC AFTER UNKNOWN INPUT OBSERVER.” ARPN Journal of Engineering and Applied Sciences VOL. 11, NO. 5, MARCH 2016.
 S. Talati, A. Rahmati, and H. Heidari, “Investigating the Effect of Voltage Controlled Oscillator Delay on the Stability of Phase Lock Loops”, MJTD, vol. 8, no. 2, pp. 57-61, May 2019.
 Saeed. Talati, Behzad. Ebadi, Houman. Akbarzade “Determining pf the fault location in distribution systems in presence of distributed generation resources using the original post phasors“QUID 2017, pp. 1806-1812, Special Issue No.1- ISSN: 1692-343X, Medellín-Colombia. April 2017.
 Saeed Talati, Mohamadreza HasaniAhangar, “Analysis, Simulation and Optimization of LVQ Neural Network Algorithm and Comparison with SOM”, MJTD, vol. 10, no. 1, Jan. 2020.
 Talati, S., & Hassani Ahangar, M. R. (2020). Combining Principal Component Analysis Methods and Self-Organized and Vector Learning Neural Networks for Radar Data. Majlesi Journal of Telecommunication Devices, 9(2), 65-69.
 Saeed Talati, Pouriya Etezadifar, “Providing an Optimal Way to Increase the Security of Data Transfer Using Watermarking in Digital Audio Signals”, MJTD, vol. 10, no. 1, Feb. 2020.
 Hassani Ahangar, M. R., Talati, S., Rahmati, A., & Heidari, H. (2020). The Use of Electronic Warfare and Information Signaling in Network-based Warfare. Majlesi Journal of Telecommunication Devices, 9(2), 93-97.
 Talati, S., & amjadi, alireza. (2020). Design and simulation of a novel photonic crystal fiber with a low dispersion coefficient in the terahertz band. Majlesi Journal of Mechatronic Systems, 9(2).