Suppression of radar-to-radar jammers, especially the mainbeam jammers, has been an urgent demand in vehicular sensing systems with the expected increased number of vehicles equipped with radar systems. This paper deals with the suppression of mainbeam deceptive jammers with frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar, utilizing its extra degrees-of-freedom (DOFS) in the range domain. At the modelling stage, false targets, which lag several pulses behind the true target, are considered as a typical form of mainbeam jammers. To this end the data-independent beamforming is performed to suppress false targets by nulling at the equivalent transmit beampattern with an appropriate frequency increment. However, the suppression performance degrades in the presence of transmit spatial frequency mismatch, which could be induced by quantization errors, angle estimation errors and frequency increment errors. To solve this problem, a preset broadened nulling beamformer (PBN-BF) is proposed by placing artificial interferences with appropriate powers around the nulls of the equivalent transmit beampattern. In such a way, effective suppression of deceptive jammer can be guaranteed owing to the broadened notches. At the analysis stage, numerical results in a scenario with multiple unmanned aerial vehicles (UAVs) are provided to illustrate the effectiveness of the devised data-independent BF, and the signal-to-interference-plus-noise ratio is improved compared with the conventional data-independent BF.