A unified approach is presented for deriving a large class of new and previously known time and order recursive least-squares algorithms with systolic array architectures, suitable for high throughput rate and VLSI implementations of space-time filtering and system identification problems. The geometrical derivation given is unique in that no assumption is made concerning the rank of the sample data correlation matrix. This method utilizes and extends the concept of oblique projections, as used previously in the derivations of the least-squares lattice algorithms. Both the growing and sliding memory, exponentially weighted least-squares criteria are considered.>

This paper considers a fully pipelined systolic array for solving a constrained least squares filtering problem, also known as linearly constrained array processing. This filtering problem is important in adaptive antenna arrays in which the desired narrowband signal arrives from a known direction and narrowband jammers from different directions are present. The systolic array structure allows the filtering to be done with a high throughput, important for real time communication systems.