In this work, we consider linear observer design to reconstruct the state vector of discrete-time systems with both white(time-wise uncorrelated) and jump-Markov type parameters based on noisy observations. ( Bloss, 2014 Drewry, Shutske, Trechter, Luck, & Pitman, 2019 Tsiropoulos, Fountas, Liakos, & Tekin, 2013).Įdwin Engin Yaz, in Control and Dynamic Systems, 1995 I INTRODUCTION Robotics applies to a variety of functions in the agricultural sector, which cuts across cultivation and harvests, since they can be used in the livestock sector for operations ranging from milking, cow dung disposal, cleaning of barns, field fencing, etc. The former has brought about innovation in robotic assistance in agricultural development. Its operation is paramount in pest management, disease management, agricultural product monitoring, storage control, soil and irrigation management, weed management, yield prediction, etc. McKinion and Lemmon in 1985 first proposed the use of artificial intelligence (AI) to be applied in general crop management in their paper “Expert Systems for Agriculture” ( McKinion & Lemmon, 1985). The adoption of other smart and intelligent machines that apply inputs in effective ways, for the development of small, durable, and smart machines, can address waste reduction, environmental protection, and guarantee sustainable food production as well. This enables remote monitoring of farms and machinery and maps out gracing fields for livestock in a network protocol for precision farming ( Srbinovska, Gavrovski, Dimcev, Krkoleva, & Borozan, 2015 Suma, Samson, Saranya, Shanmugapriya, & Subhashri, 2017).

With the adoption of GPS, generic radio communication techniques can be applied to mobile telephones or other networked systems. The successful creation of these virtual models has made it easy to evaluate the performance improvement in each location. As such, antennas can be used to meet the demands of security and radar tracking, as well as to offer customers services such as Wi-Fi and live television broadcast of farming operations (cattle tracking, trespasser, and visual performance of crops, etc.). The Internet serves as a network for process control, which includes the use of cell phones, for example, that functions as the system interface. In essence, users can record reduced costs of management, reduced environmental impact (as it optimizes waste generation), safety, and excellence in the food preservation and farming dispositions ( Bogatinoska, Malekian, Trengoska, & Nyako, 2016). The use of sensors helps in generating the data that are used to manage the operations, while working to solve problems remotely and immediately. The intelligent farming innovation uses dedicated sensor and antennas/tracking mechanism, global positioning system (GPS), robotics, etc., to mitigate agricultural resources management ( Li, Guo, & Gao, 2015 Shekhar, Colletti, Krintz, & Muñoz-arriola, 2017 Sivamani, Bae, & Cho, 2013). Peter Onu, Charles Mbohwa, in Agricultural Waste Diversity and Sustainability Issues, 2021 Intelligent farming innovations It is unknown how the algorithm will behave for radar applications. The algorithm aimed to maximize system utilities for a soft real-time task set. The computational complexity of the proposed method was very low compared to straightforward methods solving a nonlinear problem.

The proposed controller eliminated the need for precise detection of the consumption functions as in conventional feedback control methods. In the proposed adaptive QoS controller, the resource utilization was assigned to each task through an online search for the fair QoS level based on the errors between the current QoS levels and their average. proposed a novel control method for fair resource allocation and maximization of the QoS levels of individual tasks. A simulation model of a QoS resource management diagram was proposed and implemented. A finite-horizon scheduling algorithm was also proposed. The QoS optimization was performed using the Q-RAM approach.

presented a QoS optimization and dwell scheduling scheme for radar tracking applications. Moo, Zhen Ding, in Adaptive Radar Resource Management, 2015 2.4.3.3 Other Q-RAM Algorithms

Antilaser priority net radar filtering