# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 3 Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
### Principles of Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
The 2D-FFT processed data are input into the real-time target detection algorithm for target detection to obtain the distance \(R\) and velocity \(V\) of the target. 4. Based on the distance information of the target, the Doppler FFT is performed by reading the velocity dimension data of all virtual antenna channels at the target distance \(R\) from memory I. \(N_{a}\) Doppler data vectors \(V_{d}\) are finally obtained, and the number of \(V_{d}\) is equal to the number of velocity samples \(K\). 5. Based on the velocity information \(V\) of the target, the antenna data corresponding to the target are extracted from \(N_{a}\) Doppler data vectors. The set \(S_{a}\) of all virtual antenna channel data associated with this target is obtained, and the data set size is \(N_{a}\). 6. Configure the antenna array parameters, reorder the data in the data set \(S_{a}\) based on the antenna array arrangement information, and obtain the antenna data vector \(V_{a}\). 7. Finally, the antenna data vector \(V_{a}\) is output for angle estimation. The differences compared with the conventional architecture are as follows:  
Firstly, the proposed system solely stores the whole frame data after the Range FFT, eliminating the data storage step following the Doppler FFT. Upon target detection, the velocity dimension data from all virtual antenna channels at the target distance \(R\) are retrieved from memory I based on the target distance information, and a real-time target distance-selective Doppler FFT is performed to acquire Doppler data vectors \(V_{d}\). Subsequently, the antenna data corresponding to the target are extracted from these \(N_{a}\) Doppler data vectors based on the velocity information \(V\), resulting in a set of all virtual antenna  
Figure 3: Efficient dynamic engineering implementation architecture for MIMO radar system. channel data corresponding to the target \(S_{a}\). This modification leverages the latency in the demand for data after Doppler FFT by the angle estimation module, obviating the need for pre-storage of the entire frame of data after Doppler FFT and target detection, and the Doppler dimensional data are extracted based on the distance information to execute the real-time Doppler FFT. This significantly reduces the volume of data post-distance selection, facilitating subsequent Doppler processing and data storage. Secondly, the conventional architecture involves storing the entire frame data after 2D FFT, necessitating extensive filtering and sorting from a large data set based on antenna array information when accessing the antenna data for a specific target. This increases the complexity of system design and diminishes antenna array compatibility. Conversely, in the high-efficiency dynamic MIMO radar system, the total data volume after 2D FFT is \(CN_{a}\), where \(C\) represents the number of targets and \(N_{a}\) denotes the total number of virtual antenna channels. For various antenna arrays, this configuration requires only minimal reordering and retrieval processing based on the antenna array information, with the number of antenna data being \(N_{a}\). This obviates the need to modify other system components, substantially enhancing the compatibility. In this proposed architecture, only the whole frame of radar data after the Range FFT is retained between two FFT processing steps. Assuming a constant radar data bit width \(W_{d}\) throughout the processing flow, the size of storage resource \(\widetilde{S}_{m}\) utilized for data storage after Range FFT can be outlined as follows:  
\[\tilde{S}_{m}=W_{d}MKN_{a}, \tag{15}\]  
where \(M\) denotes the number of distance sampling points, \(K\) denotes the number of velocity sampling points, and \(N_{a}\) denotes the amount of virtual channel data.