## 1 Introduction  
Over the past decades, passive radars have evolved significantly [1; 2; 3], which can be seen in numerous demonstrations and works devoted to this topic [4; 5; 6; 7; 8]. This has resulted from the advantages of passive radars and the possibility to detect targets which do not have their own emission. In fact, issues related to passive radars are generally widely described in the literature, however, there are still problems that require a specific approach. The main problem in Passive Coherent Location (PCL) radar technology is that using classical passive radar processing for air target detection [9] does not allow one to detect and localize the target in the direction of the illuminator of opportunity, therefore the radar is \"blind\" at this particular angle and additionally the target is in the first range cell, which provides unclear detection results. This direction is reserved for the collection of the reference signal, which is used for cross-correlation with signals collected from other receiver channels whose measurement antennas are pointed in other surveillance directions where a target echo is suspected to be received. However, this paper deals with the methodology which allows kinematic parameters of the object to be distinguished even if the range information is lost. Various answers to the problem of passive radar direct path \"blindness\" can be found in numerous literature positions [10; 11; 12; 13; 14; 15]. A possible solution to this issue may be the employment of reference signal reconstruction [10]. However, this technique only works for digital signals and with sufficient signal to-noise ratio (SNR) values. Using a beamforming technique to reduce direct signal leakage to surveillance channels [11; 12] can also be employed. [PERSON] et al. [13], used a Constrained Least Squares two-dimensional localization algorithm. Its performance, expressed in terms of Root Mean Square Error (RMSE), is even comparable to square root of the Cramer Rao Lower Bound (CRLB) for some of the simulation scenarios presented. A significant disadvantage of this algorithm is a necessity of employment multiple transmitters of opportunity. A different solution to the localization issue can be found in [PERSON] et al. [14]. Joint target location is based on a PCL and Time Difference of Arrival (TDOA) measurement techniques. However, the TDOA method requires multiple dislocated radar receivers. Another study on the target location accuracy in multistatic scenario is presented by [PERSON] et al. [15]. The other solution for this problem, utilizing a single receiver and single transmitter of opportunity only, might be to additionally use FSR methods in the PCL processing chain. The use of FSR geometry allows passive radar to detect and estimate main movement parameters such as target velocity for the targets crossing the T\({}_{\mathrm{x}}\)-R\({}_{\mathrm{x}}\) baseline [16; 17]. In such a case, data from the FSR module applied in PCL radars might be used as additional information for the radar tracker, and consequently the detection and velocity estimation from the FSR module to the tracker working in the bistatic range-Doppler plane. Such a method will significantly improve the detection and tracking performance in PCL processing. This fact led the authors to study in more detail the possibility of applying FSR geometry in passive radar, and test novel methods for target Doppler frequency rate estimation which might be applied in PCL processing. An additional motivation was using low-frequency DVB-T sources of illumination in passive radars and their ability to perform foliage penetration. The VHF DVB-T operates in the band of 174-230 MHz [18]. As these are relatively low frequencies, they penetrate the foliage well. The authors did one experiment where a VHF DVB-T based passive radar was deployed in a forest on a low mast around 3 m in height, which was much lower than the surrounding trees, and successfully detected the air targets. The results have been described by [PERSON] et al. [19].