A Joint Optimization Method for Dimensionality Reduction and Quantization of Communication Signals for Target State Estimation

Abstract

The invention discloses a joint optimization method for dimensionality reduction and quantification of a communication signal for object state estimation. The joint optimization method specifically comprises the following steps: (1) reducing the dimensionality of a system state through a state linear dimensionality reduction method to realize dimensionality limit for independent parallel signal channel transmission data; (2) predicating through a multi-level quantization innovation kalman filter to obtain a predicted value of a signal and furthermore obtain the innovation of the signal; (3) quantifying the innovation under the restraint of transmission power and bandwidth by taking least mean-square estimation as the standard by using a joint-design state linear dimensionality reduction method and a time-invariant symmetry quantization method; and (4) filtering the received innovation through the multi-level quantization innovation kalman filter at a signal receiving end to realize object state estimation. By using the joint optimization method, a transmission signal meets communication restraint, and meanwhile, a state estimator at the signal receiving end has higher estimation precision, therefore, the joint optimization method is an effective signal processing method.

Description

technical field [0001] The invention belongs to the field of communication and control, and relates to a joint optimization method of communication signal dimension reduction and quantization for target state estimation. Background technique [0002] Recently, large-scale control systems, distributed control systems, remote control systems, etc. have been vigorously developed. In these systems, the controlled objects, controllers, actuators, etc. are often distributed in different physical locations and connected as a whole through network channels, so communication between them is inevitable. In the above system, the controlled object is often complex in structure. In order to accurately describe the controlled object, its mathematical model often has a high order and many state dimensions. When analyzing and designing the controlled object, such as analyzing the stability of the controlled object, designing a controller, etc., the state of the controlled object is often i...

AI Technical Summary

Problems solved by technology

However, joint optimization methods for state dimensionality reduction and quantizers for quantized dynamic objects are lacking.

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